Method and system for generating and visualizing patient information including tumor heterogeneity, therapeutic effect and survival statistics

Abstract

A method (100) for visualizing heterogeneity of a tumor of a patient, comprising: (i) receiving (120) information about the patient, the received information comprising demographic information and clinical information about the patient, an identification of a type of tumor of the patient, and an assessment of heterogeneity of a tumor of the patient; (ii) obtaining (130) therapeutic effect information for each of two or more subclones of a tumor of the patient based on the identification of the type of the tumor of the patient and the heterogeneity assessment of the tumor of the patient; (iii) generating (140) a visual representation of the heterogeneity assessment of a tumor of the patient and the obtained therapeutic effect information for each of the two or more subclones of the tumor; and (iv) providing (150) the generated visual representation via a user interface of the system, where the visual representation comprises a tumor heterogeneity graph and a therapeutic effect graph.

Description

Technical Field

[0001] The present disclosure generally relates to methods and systems for generating and providing visual representations of patient information, including tumor heterogeneity, treatment effects, and survival statistics. Background Art

[0002] Data visualization is a growing and evolving field. Visualization of medical data is an important part of this growing field, as skilled visualization can benefit many different aspects of medicine, including diagnosis, treatment, and more. In fact, visualization of medical data has become richer and more diverse over time due to the use of electronic medical records (EMRs). For example, with the use of EMRs, more patient information and larger patient groups become possible, resulting in richer data analysis and visualization.

[0003] An important type of medical data is tumor heterogeneity, which can be described as the differences between cancer cell populations or subclones within a single tumor. While there are several types of differences between cancer cells, some of the most important are genetic mutations, which can lead to significant variations in how the cancer cells function. Some cells within a single tumor may have a first set of genetic mutations that confer certain properties, including sensitivity or resistance to therapy, while other cells within the same tumor may have another set of genetic mutations that confer different properties. Tumor heterogeneity can be an important factor in how tumors are identified and treated, as well as how they respond to therapy.

[0004] Understanding and visualizing tumor heterogeneity remains a complex challenge. While tumor heterogeneity can be quantified, displaying this quantification in an actionable and understandable form is more difficult. Current methods for visualizing tumor heterogeneity are not efficient or easy to understand. Additionally, current methods for visualizing tumor heterogeneity fail to incorporate potential treatments, especially given the quantified tumor heterogeneity.

[0005] Another important type of medical data is mortality data. For example, given a common diagnosis for a patient group, the death or survival of that patient group over time is often utilized. This provides information about their chances of death or survival over a similar time period for individuals who receive that diagnosis. A common survival plot is the Kaplan-Meier plot, which in the medical field is a graphical representation of the calculated results of the percentage of patients who survive a certain amount of time after a diagnosis or treatment. However, Kaplan-Meier plots and similar visualizations are limited. They only show data in one form, rather than containing all the information that can be obtained from the EMR. Summary of the invention

[0006] Therefore, there is a need for methods and systems for improved medical data visualization. Various embodiments and implementations herein are directed to a tumor visualization system that generates and provides a visual representation of a patient's heterogeneous tumor and a treatment effect on the heterogeneous tumor. The tumor visualization system receives information about the patient, the received information including demographic information and clinical information about the patient, identification of the patient's tumor type, and an assessment of the heterogeneity of the patient's tumor. The heterogeneity assessment includes an analysis of the heterogeneity of the patient's tumor and identification of two or more subclones of the tumor, wherein the two or more subclones are distinguished by one or more genetic variations. The tumor visualization system obtains treatment effect information for each of the two or more subclones of the tumor. The tumor visualization system then generates a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor, and provides the generated visual representation to a user via a user interface of the system.

[0007] Various embodiments and implementations herein are also directed to a survival statistics analysis system that generates and provides a visual representation of a patient's survival. The survival statistics analysis system receives information about a patient, the received information including at least the patient's diagnosis and the date of diagnosis. The system uses the patient information and a reference survival data set to calculate one or more survival statistics for the patient relative to a first historical patient group over a first time period. The system then generates a visual representation of the calculated one or more survival statistics, and provides the generated visual representation via a user interface of the system. According to one embodiment, the generated visual representation of the patient's survival includes one or more of the following: a survival function graph augmented with death data, a median survival graph, a mortality risk graph, and a survival probability graph.

[0008] In general, in one aspect, a method for visualizing the heterogeneity of a patient's tumor and the treatment effect on the patient's heterogeneous tumor is provided. The method includes: (i) receiving information about a patient, the received information including demographic information and clinical information about the patient, identification of the patient's tumor type, and heterogeneity assessment of the patient's tumor, the heterogeneity assessment including analysis of the heterogeneity of the patient's tumor and identification of two or more subclones of the tumor; (ii) obtaining treatment effect information for each of the two or more subclones of the tumor based on the identification of the patient's tumor type and the heterogeneity assessment of the patient's tumor; (iii) generating a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor; and (iv) generating a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor via a user of the system. The user interface provides the generated visual representation, wherein the visual representation includes: 1. a tumor heterogeneity graph, which includes: (i) an identification of each of the two or more subclones of the patient's tumor, wherein the identification includes an indication of the relative amount of each subclone in the patient's tumor; and (ii) a heterogeneity score that indicates the overall complexity of the patient's tumor composition, wherein the heterogeneity score is visualized in an ordered data plot of heterogeneity scores of the same tumor type from a patient group, thereby indicating the degree of tumor heterogeneity in the patient as a percentile rank within the group; and 2. a treatment effect graph, which includes an indication of the effect of each of a plurality of different treatments for each of the two or more subclones of the patient's tumor.

[0009] According to one embodiment, the generated visual representation also includes some or all of the demographic information and the clinical information about the patient.

[0010] According to one embodiment, the tumor heterogeneity graph also includes an indication of the metastatic potential of each subclone in the patient's tumor.

[0011] According to one embodiment, the two or more subclones are distinguished by one or more molecular and / or histological variations. According to one embodiment, the tumor heterogeneity graph also includes identification of some or all of the one or more molecular and / or histological variations that are specific to each of the two or more subclones. According to one embodiment, the one or more molecular variations include one or more of the following: genetic variation, epigenetic variation, transcriptomic variation, proteomic variation, and other possible variations, including but not limited to single nucleotide variation, insertion and deletion (indels), gene fusion, structural variation, gene / protein expression, and methylation, etc.

[0012] According to one embodiment, each of the two or more subclones of the patient's tumor on the tumor heterogeneity graph is selectable, and wherein selecting a subclone results in a visual display of some or all of one or more genetic variations unique to the selected subclone.

[0013] According to one embodiment, the treatment effect graph further comprises an indication of the effect of a combination of two or more treatments on the two or more subclones of the patient's tumor.

[0014] According to one embodiment, the tumor heterogeneity graph and / or the treatment effect graph further includes one or more of the following: side effect information for each of the plurality of different treatments, and cost of each of the plurality of different treatments.

[0015] According to another aspect, a system for providing visualization of the heterogeneity of a patient's tumor and the therapeutic effect on the patient's heterogeneous tumor is provided. The system includes: patient information, which includes at least demographic information and clinical information about the patient, identification of the patient's tumor type, and an assessment of the heterogeneity of the patient's tumor, the heterogeneity assessment including analysis of the heterogeneity of the patient's tumor and identification of two or more subclones of the tumor; a treatment database; a processor, which is configured to: (i) obtain therapeutic effect information for each of the two or more subclones of the tumor from the treatment database based on the identification of the patient's tumor type and the heterogeneity assessment of the patient's tumor; and (ii) generate a visual representation of the heterogeneity assessment of the patient's tumor and the obtained therapeutic effect information for each of the two or more subclones of the tumor; and a user interface. A graphical representation of the generated visual representation of the patient's tumor comprising: (i) an identification of each of the two or more subclones of the patient's tumor, wherein the identification comprises an indication of the relative amount of each subclone in the patient's tumor; and (ii) a heterogeneity score indicating the overall complexity of the patient's tumor composition, wherein the heterogeneity score is visualized in an ordered data plot of heterogeneity scores of the same tumor type from a group of patients, thereby indicating the degree of tumor heterogeneity in the patient as a percentile rank within the group; and (ii) a graphical representation of the generated visual representation of the patient's tumor, wherein the generated visual representation comprises: 1. a tumor heterogeneity graph comprising: (i) an identification of each of the two or more subclones of the patient's tumor, wherein the identification comprises an indication of the relative amount of each subclone in the patient's tumor; and (ii) a heterogeneity score indicating the overall complexity of the patient's tumor composition, wherein the heterogeneity score is visualized in an ordered data plot of heterogeneity scores of the same tumor type from a group of patients, thereby indicating the degree of tumor heterogeneity in the patient as a percentile rank within the group; and 2. a treatment effect graph comprising an indication of the effect of each of a plurality of different treatments for each of the two or more subclones of the patient's tumor.

[0016] In general, in another aspect, a method for providing a visual representation of survival for a patient is provided. The method includes: (i) receiving information about the patient, the received information including at least a patient diagnosis; (ii) using the patient information and a reference survival data set to calculate one or more survival statistics for the patient over a first time period relative to a first historical patient cohort; (iii) generating a visual representation of the calculated one or more survival statistics; and (iv) providing the generated visual representation via a user interface of the system. According to one embodiment, the generated visual representation includes one or more of the following:

[0017] 1. A survival function graph, the survival function graph comprising a single graph having: (i) a Kaplan-Meier estimate plot for the first historical group during the first time period; and (ii) a trailing death plot comprising the percentage of deaths of the first historical group during a trailing time period, the trailing time period being a windowed segment of the first time period;

[0018] 2. A median survival graph, the median survival graph comprising a single graph having: (i) an identification of the median survival time for the first historical cohort during the first time period; and (ii) a plot of the percentage of deaths for the first historical cohort during the pre-median survival time period; and (iii) a plot of the percentage of survival for the first historical cohort during the post-median survival time period;

[0019] 3. a mortality risk graph, the mortality risk graph comprising a trailing (also referred to as a moving window) mortality plot, the trailing mortality plot comprising a percentage of deaths in the first historical group during a trailing time period relative to the number of survivors in the first historical group at the beginning of the trailing time period, the trailing time period being a windowed segment of the first time period; and

[0020] 4. A survival probability graph, comprising a plot of survival probabilities within the first time period, wherein the survival probability is calculated for each of a plurality of time points within the first time period by dividing the total number of survivors of the first historical group at the end of the first time period by the total number of survivors of the first historical group at the time point being calculated.

[0021] According to one embodiment, the first period of time is 5 years.

[0022] According to one embodiment, the trailing period is 6 months.

[0023] According to one embodiment, the survival function graph further comprises a final survivor probability estimate for the first time period.

[0024] According to one embodiment, the median survival graph further comprises a final survivor probability estimate for the first time period.

[0025] According to one embodiment, providing the generated visual representation includes providing two or more of: (i) the survival function graph; (ii) the median survival graph; (iii) the mortality risk graph; and (iv) the survival probability graph.

[0026] According to one embodiment, the user interface is configured to allow a user to navigate between the two or more graphics.

[0027] According to one embodiment, the user interface is configured to display two or more graphics at a time.

[0028] According to one embodiment, each graph is calculated using each of a plurality of different historical patient groups, each of the plurality of different historical patient groups including different relevant conditions, such as tumor heterogeneity index, mutation load, BMI, etc., for patients in the corresponding historical group.

[0029] According to another aspect, a system for providing a visual representation of survival for a patient is provided. The system includes: patient information including at least a patient diagnosis; a reference survival data set; a processor configured to: (i) receive information about the patient, the received information including at least a patient diagnosis and a diagnosis date; (ii) use the patient information and the reference survival data set to calculate one or more survival statistics for the patient over a first time period relative to a first historical patient cohort; and (iii) generate a visual representation of the calculated one or more survival statistics; and a user interface configured to provide the generated visual representation. According to one embodiment, the generated visual representation includes one or more of the following:

[0030] 1. A survival function graph, the survival function graph comprising a single graph having: (i) a Kaplan-Meier estimate plot for the first historical group during the first time period; and (ii) a trailing death plot comprising the percentage of deaths of the first historical group during a trailing time period, the trailing time period being a windowed segment of the first time period;

[0031] 2. A median survival graph, the median survival graph comprising a single graph having: (i) an identification of the median survival time for the first historical cohort during the first time period; and (ii) a plot of the percentage of deaths for the first historical cohort during the pre-median survival time period; and (iii) a plot of the percentage of survival for the first historical cohort during the post-median survival time period;

[0032] 3. a mortality risk graph, the mortality risk graph comprising a trailing mortality plot, the trailing mortality plot comprising a percentage of deaths of the first historical group during a trailing time period relative to the number of survivors of the first historical group at the beginning of the trailing time period, the trailing time period being a windowed segment of the first time period; and

[0033] 4. A survival probability graph, comprising a plot of survival probabilities within the first time period, wherein the survival probability is calculated for each of a plurality of time points within the first time period by dividing the total number of survivors of the first historical group at the end of the first time period by the total number of survivors of the first historical group at the time point being calculated.

[0034] It should be understood that all combinations of the foregoing concepts and the additional concepts discussed in more detail below (assuming such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of the claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be understood that terms explicitly used herein that may also appear in any disclosure incorporated by reference should be given the meaning most consistent with the specific concepts disclosed herein.

[0035] These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In the drawings, the same reference numerals generally refer to the same parts throughout the different views. The drawings illustrate features and modes of implementing various embodiments and should not be construed as limiting other possible embodiments falling within the scope of the appended claims. Moreover, the drawings are not necessarily drawn to scale, but emphasis is generally placed on illustrating the principles of the various embodiments.

[0037] Figure 1 is a flow chart of a method for generating and providing a visual representation of tumor heterogeneity and / or treatment effects according to one embodiment.

[0038] Figure 2 is a schematic representation of a tumor visualization system according to one embodiment.

[0039] Figure 3 is an example of a visual representation including a patient information panel and a tumor heterogeneity graphical panel according to one embodiment.

[0040] Figure 4 is an example of a visual representation of a tumor heterogeneity graph according to one embodiment.

[0041] Figure 5 is an example of a tumor heterogeneity graphical panel according to one embodiment.

[0042] Figure 6 is an example of a tumor heterogeneity graphical panel according to one embodiment.

[0043] Fig. 7A is an example of a visual representation including a treatment effects panel according to one embodiment.

[0044] Figure 7B is an example of a visual representation of a medication selection panel according to one embodiment.

[0045] Figure 8 is an example of a treatment effect panel according to one embodiment.

[0046] Fig. 9 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0047] Fig.10 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0048] Fig.11 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0049] Fig.12 is an example of a drug selection panel according to one embodiment.

[0050] Fig.13 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0051] Fig.14 is an example of a treatment effect panel according to one embodiment.

[0052] Fig.15 is an example of a drug selection panel according to one embodiment.

[0053] Fig.16 is an example of a drug selection panel according to one embodiment.

[0054] Fig.17 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0055] Fig.18 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0056] Fig.19 is an example of a treatment effect graph for multiple subclones according to one embodiment.

[0057] Fig. 20 is an example of a treatment effect panel according to one embodiment.

[0058] Fig.21 is an example of a portion of a treatment effect panel according to one embodiment.

[0059] Fig. 22 is a flow chart of a method for generating and providing a visual representation of a patient's survival, according to one embodiment.

[0060] Fig.23 is a schematic representation of a survival statistics analysis system according to one embodiment.

[0061] Fig.24A is an example of a survival function graph according to one embodiment.

[0062] Fig. 24B is an example of a survival function graph according to one embodiment.

[0063] Fig.25A is an example of a median survival graph according to one embodiment.

[0064] Fig.25B is an example of a median survival graph according to one embodiment.

[0065] Fig.26A is an example of a mortality risk graph according to one embodiment.

[0066] Fig.26B is an example of a mortality risk graph according to one embodiment.

[0067] Fig.27A is an example of a survival probability graph according to one embodiment.

[0068] Fig.27B is an example of a survival probability graph according to one embodiment.

[0069] Fig.28 is an example of a survival function graph according to one embodiment.

[0070] Fig.29 is an example of a median survival graph according to one embodiment.

[0071] Fig.30 is an example of a mortality risk graph according to one embodiment.

[0072] Fig.31 is an example of a survival probability graph according to one embodiment. DETAILED DESCRIPTION

[0073] This disclosure describes various embodiments of systems and methods for improved medical data visualization.

[0074] According to one aspect, a system and method are configured to generate and provide a visual representation of a patient's tumor. More generally, the applicant has recognized and appreciated that it would be beneficial to provide an improved method and system for visualizing tumor heterogeneity. Therefore, a tumor heterogeneity system is described that generates and provides a visual representation of tumor heterogeneity. The tumor heterogeneity system receives information about a patient, the received information including demographic information and clinical information about the patient, identification of the patient's tumor type, and a heterogeneity assessment of the patient's tumor. The heterogeneity assessment includes an analysis of the heterogeneity of the patient's tumor and identification of two or more subclones of the tumor, wherein the two or more subclones are distinguished by one or more molecular and / or histological variations. The tumor visualization system obtains treatment effect information for each of the two or more subclones of the tumor. Then, the tumor visualization system generates a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor, and provides the generated visual representation to the user via a user interface of the system. According to one embodiment, the generated visual representation of tumor heterogeneity includes a tumor heterogeneity graph and a treatment effect graph.

[0075] It should be understood that while the systems and methods described herein or otherwise contemplated focus on using a system to visualize tumor heterogeneity for purposes of explanation only, the visualization systems and methods are also applicable to anything that can quantify and display heterogeneity. This can include medical systems and non-medical systems.

[0076] According to another aspect, a system and method configured to generate and provide a visual representation of survival is provided. More generally, the applicant has recognized and appreciated that it would be beneficial to provide an improved method and system for visualizing the probability of survival of a patient over time. Therefore, a survival statistics analysis system is described that generates and provides a visual representation of the patient's survival. The survival statistics analysis system receives information about a patient, the received information including at least the patient's diagnosis and the date of diagnosis. The system uses the patient information and a reference survival data set to calculate one or more survival statistics for the patient relative to a first historical patient group over a first time period. The system then generates a visual representation of the calculated one or more survival statistics, and provides the generated visual representation via a user interface of the system. According to one embodiment, the generated visual representation of the patient's survival includes one or more of the following: a survival function graph, a median survival graph, a mortality risk graph, and a survival probability graph.

[0077] It should be understood that while the systems and methods described herein or otherwise contemplated focus on using the system for survival statistics for purposes of explanation only, while survival data is used to illustrate this concept, the systems and methods are applicable to any time-to-event data, where events may include, for example, injury, disease onset, disease recovery, disease recurrence, hospital discharge or equipment failure or termination of a relationship or attention, and a variety of other data. Typically, time-to-event data focuses on the time that elapses before an event is experienced, which is often referred to as survival data in statistics. It is worth noting that time-to-event data can be based on events other than death, for example, disease recurrence events or hospital discharge events, etc.

[0078] According to one embodiment, in some non-limiting embodiments, the systems and methods described herein or otherwise contemplated can be implemented as elements of a commercial product for medical data, such as, for example, Philips IntelliSpace (available from Royal Philips of the Netherlands), or as elements of a commercial product for patient analysis or monitoring, or any suitable system.

[0079] The methods and systems described herein or otherwise contemplated provide many advantages over existing data generation and visualization methods and systems. Medical displays capable of providing patient data / information in such novel formats (including tumor heterogeneity graphs and treatment effect graphs) and / or the claimed novel survival function graphs, median survival graphs, mortality risk graphs, and / or survival probability graphs provide creative solutions to the problem of overwhelming information and overly cluttered visualizations and computer displays. For example, a 2020 study by Philips (“Future Health Index 2020”) showed that 35% of young health care professionals do not know how to use digital patient data to inform patient care, and 35% of young health care professionals are overwhelmed by large amounts of digital patient data (including large amounts of information shared via patient monitors and visualization systems). This is not an unusual result; many other surveys have found that physicians and clinicians are overwhelmed by information and patient care is affected. The claimed data generation and visualization methods and systems provide a simplified mechanism for displaying massive amounts of data, including tumor homogeneity data (which can come from thousands of data points), treatment effects, and survival statistics. Unlike general purpose computer systems that provide general information, the data generation and visualization methods and systems described herein or otherwise contemplated as claimed provide very specific creative solutions to persistent data visualization problems. The claimed data generation and visualization methods and systems are also not simply computerized versions of paper records. Rather, such visualizations are novel computer-based visualizations that enable data visualization and manipulation that is not possible with paper records.

[0080] refer to Figure 1 , in one embodiment, Figure 1 1 is a flow chart of a method 100 for generating and providing a visual representation of tumor heterogeneity using a tumor visualization system. The methods described in conjunction with the figures are provided only as examples and should be understood not to limit the scope of the present disclosure. The tumor visualization system can be any system described herein or otherwise contemplated. The tumor visualization system can be a single system or a plurality of different systems.

[0081] At step 110 of the method, a tumor visualization system 200 is provided. Figure 2 In the depicted embodiment of the tumor visualization system 200, for example, the system includes one or more of the following: a processor 220, a memory 230, a user interface 240, a communication interface 250, and a storage device 260, which are interconnected via one or more system buses 212. It will be appreciated that Figure 2 In some respects, an abstraction is constituted, and the actual organization of the components of the system 200 may be different and more complex than that illustrated. Additionally, the tumor visualization system 200 can be any system described or otherwise contemplated herein. Other elements and components of the tumor visualization system 200 are also disclosed and / or otherwise contemplated herein.

[0082] At step 120 of the method, the tumor visualization system receives information about the patient. The patient information can be any information about the patient that the tumor visualization system can or may utilize to perform an analysis as described herein or otherwise contemplated. According to one embodiment, the patient information includes one or more of: demographic and clinical information about the patient, identification of the patient's tumor type, and an assessment of the heterogeneity of the patient's tumor. Other information is also possible.

[0083] According to one embodiment, demographic information and clinical information about a patient may include any information about the patient, including, but not limited to, information about the patient (e.g., name, age, address, body mass index (BMI), and any other demographic information). Clinical information may also include diagnostic information about the patient, which may be any information (historical and / or current) about the patient's medical diagnosis. Clinical information may also include the patient's medical history, which may be any historical admission or discharge information, historical treatment information, historical diagnostic information, historical examination or imaging information, and / or any other information.

[0084] According to one embodiment, identification of the patient's tumor type includes identification of the patient's cancer type. For example, the cancer type can be any known cancer type, including but not limited to breast cancer, lung cancer, melanoma, endometrial cancer, kidney cancer, colorectal cancer, leukemia, non-Hodgkin's lymphoma, pancreatic cancer, prostate cancer, thyroid cancer, etc. In addition to advanced cancer types, identification of the patient's tumor type can also include more specific identifications, such as head and neck squamous cell carcinoma (HNSC), non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), familial colorectal cancer (FCC), etc.

[0085] According to one embodiment, in addition to high-level cancer types and / or more specific cancer type identifications, identification of a patient's tumor type can also include information about the severity of the patient's cancer or other qualitative or quantitative assessments. For example, the information may include multiple stages of the patient's tumor (stage 0, stage 1, stage 2, stage 3, and stage 4, each of which is associated with a specific characteristic as known in the art). The information may include a TNM (tumor (T), lymph node (N), and metastasis (M)) staging system, in which T (numbers 1 to 4) describes the size of the tumor, N (numbers 0 to 3) describes the involvement of lymph nodes, and M (numbers 0 or 1) describes whether the tumor has metastasized. The information may include grades (grades 1, 2, and 3) based on how cells look under a microscope, in which lower grades indicate slower growing cancers, while higher grades indicate faster growing cancers. Other qualitative or quantitative assessments of a patient's cancer are also possible.

[0086] According to one embodiment, identification of the patient's tumor type can be extracted or inferred from medical records, received patient demographic and clinical information, and / or other sources of information about the patient.

[0087] According to one embodiment, the heterogeneity assessment of a patient's tumor includes a qualitative or quantitative assessment of the heterogeneity of the patient's tumor. A single tumor can have several different heterogeneities. For example, it is known that genetic heterogeneity, epigenetic heterogeneity, microenvironment heterogeneity, transcriptome heterogeneity, proteome heterogeneity, histological heterogeneity, and imaging heterogeneity exist in tumors - and coexist, and can be associated with phenotypic diversity (including behavioral phenotypes and responses to treatment). It is worth noting that tumor heterogeneity can be detected within the same tumor (so-called intratumor heterogeneity) and can also be detected between primary tumors and metastatic lesions. For the purposes of this disclosure, the term "heterogeneity" refers to any difference within the same tumor or between primary tumors and metastatic lesions, etc. Tumor heterogeneity has been detected in almost every type of cancer and tumor, and as the cancer develops, tumor heterogeneity changes and evolves over time.

[0088] There are several methods for analyzing tumor heterogeneity. One method for analyzing tumor heterogeneity is by imaging the tumor. Depending on the tumor type and physical location in or on the patient's body, the tumor can be imaged using different imaging modalities alone or in combination, including but not limited to X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Imaging can then be used to qualitatively and / or quantitatively assess the heterogeneity of the tumor.

[0089] According to another embodiment, genetic research methods and systems can be used to analyze tumor heterogeneity, where "genetic" can refer to at least genetic, epigenetic and transcriptomic analysis or variation. In other research methods, a patient's tumor can be examined using molecular testing to determine a qualitative and / or quantitative assessment of tumor heterogeneity. The molecular testing can include sequencing or other genetic, epigenetic or transcriptomic analysis of one or more cells from one or more regions of the tumor.

[0090] For example, a single-cell molecular testing approach will examine individual cells from one or more regions of a tumor (or from a primary site and one or more metastatic sites). A multi-region molecular testing approach will examine one or more cells from one or more regions of a tumor (or from a primary site and one or more metastatic sites). Other sampling approaches are also possible, including representative sampling (Rep-Seq) and liquid biopsy. Heterogeneity can also be assessed by applying specific computational analysis algorithms to large amounts of sequencing data.

[0091] According to one embodiment, genetic analysis, epigenetic analysis, microenvironmental analysis, transcriptomic analysis and / or proteomic analysis of tumor heterogeneity results in the identification of at least two subclones of a tumor. A subclone can be defined as a variation (either within a single tumor (intratumor heterogeneity) or between a primary tumor and a metastatic tumor - identified by heterogeneity analysis). For example, a heterogeneous tumor thus includes at least two different subclones, but there may be more than two subclones. The threshold for labeling a variation as a subclone may depend on the user, system programming, standards in the art, and other parameters.

[0092] According to one embodiment, at least two different subclones of a heterogeneous tumor can be defined in a variety of different ways (including one or more based on genetic analysis, epigenetic analysis, microenvironment analysis, transcriptome analysis, proteome analysis, histological analysis and / or imaging analysis). For example, at least two different subclones of a heterogeneous tumor can be defined by epigenetic differences (e.g., different histone modifications, microenvironment differences, transcriptome differences, and / or proteome differences, etc.). According to one embodiment, at least two different subclones of a heterogeneous tumor are defined by controlling genetic variation (including but not limited to gene mutations, etc.). Therefore, two or more subclones can be distinguished by one or more genetic variations.

[0093] According to one embodiment, the patient information is received from one or more different sources. According to one embodiment, the patient information is received, retrieved, or otherwise obtained from an electronic medical record (EMR) database or system 270. The EMR database or system can be local or remote. The EMR database or system can be a component of the tumor visualization system, or can communicate locally and / or remotely with the tumor visualization system. The received patient information can be used immediately, or the received patient information can be stored in a local or remote storage device for use in additional steps of the method.

[0094] At step 130 of the method, the tumor visualization system 200 receives, retrieves, or otherwise obtains treatment effect information for each of the two or more subclones of the tumor. The treatment effect information is identified and obtained based on the received identification of the patient's tumor type and the received heterogeneity assessment of the patient's tumor.

[0095] The treatment effect can be based on the known effect of one or more treatments on the tumor type and the identified subclonal type. For example, there may be a database of possible cancer treatments associated with the known or estimated effects of some or all of these possible cancer treatments in treating the identified subclonal types of a particular tumor type (where "treating" can be a variety of outcomes, including cell death, growth inhibition, and other outcomes). For example, when a subclone is defined by a genetic mutation X, it may be known in the art or in a treatment database (now or in the future) how a subclone with genetic mutation X responds to various different treatments.

[0096] According to one embodiment, a tumor visualization system can retrieve information from a treatment database based on a received assessment of heterogeneity of a patient's tumor (e.g., results from qualitative and / or quantitative genetic, epigenetic, microenvironmental, transcriptomic, proteomic, histological, and / or imaging analysis assessments of the tumor). As just one example, the tumor visualization system can retrieve information from a treatment database by searching for one or more genetic mutations associated with a subclone in order to retrieve treatments associated with those one or more genetic mutations. Notably, the treatment can be associated with a genetic mutation (or other identification of subclonal heterogeneity) based on whether the subclone responded well to the treatment or based on whether the subclone responded poorly to the treatment. Such information is important information for a tumor visualization system described herein or otherwise contemplated.

[0097] According to one embodiment, treatment effect information is received from one or more different sources. According to one embodiment, treatment effect information is received, retrieved, or otherwise obtained from a treatment database or system 274. The treatment database or system may be local or remote. The treatment database or system may be a component of the tumor visualization system, or may communicate locally and / or remotely with the tumor visualization system. The received treatment effect information may be used immediately, or the received treatment effect information may be stored in a local or remote storage device for use in further steps of the method.

[0098] At step 140 of the method, the tumor visualization system generates a visual representation using the received heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor. The visual representation includes a representation of the heterogeneity assessment of the patient's tumor and a representation of the obtained treatment effect information. The visual representation can be generated immediately after receiving the heterogeneity assessment and the treatment effect information, or the visual representation can be performed using the stored information. The system can generate one or more visual representations as needed or as a result of programming or other automation. According to one embodiment, the system generates one or more visual representations in response to user input. For example, a physician or other health care professional can request the information. Alternatively, the system can be partially or fully automated so that one or more visual representations can be automatically calculated. For example, the system can be a stand-alone system or a component of a medical data or analysis system, which can include a user interface that displays information about the patient. The information may include one or more visual representations described herein or otherwise contemplated.

[0099] One or more visual representations of heterogeneity assessments and treatment effect information are generated according to any method described herein or otherwise contemplated. For example, described below are several different non-limiting methods and examples of visual representations of heterogeneity assessments and treatment effect information, and thus the system can be designed or programmed to generate such visual representations using known methods and mechanisms for graphically representing data. The system can utilize these methods and many other methods.

[0100] Once one or more visual representations of heterogeneity assessment and treatment effect information are generated, the one or more visual representations may be used immediately or may be stored in a local or remote storage device for use in further steps of the method.

[0101] At step 150 of the method, the tumor visualization system provides one or more visual representations of the generated heterogeneity assessment and treatment effect information via a user interface. For example, the visual representation can be displayed to medical professionals or other users including patients via the user interface of the system. The generated one or more visual representations can be provided to the user via any mechanism for displaying, visualizing or otherwise providing information via the user interface. According to one embodiment, the information can be transmitted to the user interface and / or another device via wired and / or wireless communication. For example, the system can transmit the information to a mobile phone, a computer, a laptop, a wearable device, and / or any other device configured to allow display and / or other communications of the report. The user interface can be any device or system that allows the transmission and / or reception of information, and can include a display, a mouse, and / or a keyboard for receiving user commands. As a non-limiting example only, the user interface can be a component of a patient monitoring system.

[0102] According to one embodiment, the display may also include patient information, such as demographic and clinical information about the patient, the patient's diagnosis, the patient's medical history, and / or any other information.

[0103] According to one embodiment, some or all of the displayed information can be manipulated in response to user input provided via a user interface, as described herein.

[0104] The tumor visualization system provides a generated visual representation of the heterogeneity assessment of the patient's tumor and the treatment effect information for the heterogeneous tumor via a user interface, and this visual representation can be any of a variety of different statistical and visual representations. According to one embodiment, the visual representation includes a tumor heterogeneity graph. The tumor heterogeneity graph can include, for example, an identification or other indication of each of two or more subclones of the patient's tumor. According to one embodiment, this identification includes an indication of the relative amount of each subclone in the patient's tumor relative to each other and / or relative to the entire tumor. The tumor heterogeneity graph can also include, for example, a tumor heterogeneity "score" or "index" (which can be referred to as a "tumor heterogeneity score" in this article) for the patient's tumor. There are many different ways to evaluate or calculate the tumor heterogeneity score of a tumor. According to one embodiment, the tumor heterogeneity score is calculated based on a methodology for determining heterogeneity. For example, the score can be based on parameters derived from an imaging modality, based on genetic variation (e.g., mutation load), or based on any other method.

[0105] The tumor heterogeneity graph can also include an ordered data plot of heterogeneity scores for the same tumor type, for example based on other patient cohorts, which the system can obtain or which can be provided in the patient information or heterogeneity assessment. The ordered data plot of the tumor heterogeneity score can include an identification on the patient's tumor heterogeneity score graph that represents the degree of tumor heterogeneity for the patient as a percentile rank within the cohort.

[0106] According to one embodiment, the tumor heterogeneity graph also includes identification of some or all of one or more variations unique to each of the two or more subclones (e.g., one or more molecular and / or histological variations unique to one subclone).

[0107] The tumor visualization system also provides a generated treatment effect graph via a user interface, which is a visual representation of treatment effect information for a heterogeneous tumor. The treatment effect graph includes, for example, an indication of the effect of each of a plurality of different treatments on each of two or more subclones of a patient's tumor. It is noteworthy that the effect can mean a treatment with a favorable or positive outcome (e.g., effectively targeting or treating cells of a cancer subclone, including but not limited to killing, neutralizing or terminating cells of a cancer subclone), a treatment with a neutralizing outcome, or a treatment with an unfavorable or negative outcome (e.g., being unable to target or treat cells of a cancer subclone).

[0108] According to one embodiment, the treatment effect graphic also includes an indication of the effect of a combination of two or more treatments on two or more subclones of the patient's tumor. For example, the treatment effect graphic can automatically or in response to user input include a set of cancer treatments that can advantageously target and / or treat most or all of the identified tumor subclones. According to one embodiment, the cancer visualization system can be programmed or designed to identify the combination of treatments that have the most favorable predicted outcome for most or all of the identified tumor subclones.

[0109] At step 160 of the method, a user provides input regarding one or more visualizations displayed via a user interface of the tumor visualization system 200. The input can be any input, including but not limited to commands to modify a visualization, add a visualization, remove a visualization, and / or any other input or command. For example, the user interface can be configured to allow a user to navigate between two or more visualizations. The user interface can be configured to display one, two, or more visualizations at a time. The user can be a health care professional, a patient, and / or any other user.

[0110] According to another embodiment, at step 160 of the method, the user provides input to the system in response to information provided via a reporting mechanism. For example, a clinician can select a treatment to be administered to a patient, and can then report that treatment (optionally along with evidence of all available options) to a clinical decision support system or patient management system for future audit purposes, etc.

[0111] Thus, at step 170 of the method, the tumor visualization system regenerates one or more visualizations based on the received input, such as returning to a previous step of the method and generating a visualization based on the input. Alternatively, the system can modify the visualization based on the received input. As just one example, a user can request information about one or more subclones or one or more treatments or treatment combinations. This may require the system to recalculate and / or regenerate the visualization, and then be able to display the recalculated and / or regenerated visualization via the user interface. Many other modifications are also possible.

[0112] Example

[0113] Described below are examples of possible applications of the methods and systems described herein or otherwise contemplated. These examples are provided only as possible embodiments of the methods and systems described herein or otherwise contemplated, and therefore do not limit or prohibit other possible variations and embodiments.

[0114] refer to Figure 3 , in one embodiment, Figure 3is a visual representation 300 generated by a tumor visualization system. The visual representation 300 includes a patient information panel 310 and a tumor heterogeneity graphical panel 320. According to the embodiment, the patient information panel 310 includes some or all of the received patient information, including but not limited to identification of the tumor type (HNSC), address, patient identification information, date of birth, gender, social security number, primary physician, email address, and / or other information.

[0115] refer to Figure 5 , Figure 5 yes Figure 3 5 is a larger representation of the tumor heterogeneity graphical panel 320 of FIG. The tumor heterogeneity graphical panel includes a tumor heterogeneity graph 510. In this example, the tumor heterogeneity graph includes a bar graph 510. The bar graph shows five different subclones (subclone A, subclone B, subclone C, subclone D, and subclone E) and their relative content in the tumor (52%, 21%, 15%, 10%, and 2%, respectively).

[0116] refer to Figure 4 , in one embodiment, Figure 4 5 is a larger representation of a tumor heterogeneity bar graph 510 showing five different subclones (subclone A, subclone B, subclone C, subclone D, and subclone E). The tumor heterogeneity bar (THB) provides basic information about the relative sizes of cancer subclones. Each block (c) represents a subclone and is labeled with an identification label (a) and optional additional information (e.g., specific size percentage (b)).

[0117] Back to Figure 5 , as shown in the graphic, according to one embodiment, some or all of the subclones can be selected or otherwise interacted with to reveal more information. For example, a subclone can be clicked, selected, or otherwise identified for information expansion. Clicking or selecting "Actionable Mutations" in the graphic will, for example, generate a list of one or more genetic mutations that can be affected by or can implement or affect therapeutic information or treatment. In other embodiments where subclones are identified by other methods (e.g., histology and imaging), clicking on or selecting a subclone can reveal other differences based on the method.

[0118] refer to Figure 6 , in one embodiment, Figure 6600 is a modification of the tumor heterogeneity graphical panel, in which the user clicks, selects, or otherwise identifies subclone B for information expansion. When these subclones are identified using genetic analysis, the information expansion shows a list of one or more genetic mutations (particularly five different mutations (i.e., "genetic abnormalities") that characterize the subclone. In addition to the identification of the mutation, other information can be provided, including but not limited to chromosome number, start and end position, mutation type, copy number, score, annotation, and other information.

[0119] Back to Figure 5 In the tumor heterogeneity graphic panel 320, as shown in the graphic and according to one embodiment, some or all of the subclones can include information about metastatic potential or other information. In this embodiment, the tumor heterogeneity graphic 510 includes color coding indicating the metastatic potential of five different subclones (ranging from "high" to "low"). For example, subclone D has very low metastatic potential, while subclone A has high metastatic potential. Other methods of conveying this information (e.g., patterning, sizing, or other methods) are also possible. According to one embodiment, the system is able to obtain metastatic potential, or metastatic potential can be provided in patient information or heterogeneity assessment.

[0120] As shown in the graph, according to one embodiment, the tumor heterogeneity graph panel can also include a tumor heterogeneity score 520 for the patient's tumor. As described above, there are many different ways to evaluate or calculate a tumor heterogeneity score for a tumor. According to one embodiment, the tumor heterogeneity score is calculated based on a methodology for determining heterogeneity. Here, the tumor heterogeneity score determined for the patient's tumor is 3.5.

[0121] As shown in the figure, according to one embodiment, the tumor heterogeneity graphical panel can also include an ordered data plot 530 of the tumor heterogeneity score for the patient's tumor type, which can be obtained by the system or can be provided in the patient information or heterogeneity assessment. The ordered data plot of the tumor heterogeneity score can include an identification 540 on the tumor heterogeneity score map for the patient's tumor. In this embodiment, the tumor heterogeneity graphical panel includes an ordered data plot 430 of the tumor heterogeneity score for head and neck squamous cell carcinoma (HNSC) for a similar patient cohort. In this example, the patient's tumor heterogeneity score is 3.5, which falls into the 78th percentile of the cohort.

[0122] refer to Fig. 7A and Figure 7B , in one embodiment, Fig. 7A and Figure 7Bis a visual representation 700 generated by a tumor visualization system. The visual representation 700 includes a treatment effect panel 710 and a drug selection panel 720. According to this embodiment, the treatment effect panel 710 includes information about the effect of one or more treatments on one or more subclones, and shows the combined effect of one or more drugs selected in the drug selection panel 720. According to this embodiment, the drug selection panel 720 includes information about the effect of one or more treatments (here, one or more drug treatments) on one or more subclones.

[0123] refer to Figure 8 , in one embodiment, Figure 8 yes Fig. 7A 810 depicts the five different subclones in this particular example (Subclone A, Subclone B, Subclone C, Subclone D, and Subclone E) and an indication of the effect of the selected treatment (letrozole) on each of these subclones. This measure of effect can be identified or displayed in a variety of ways, including a bar graph, a line graph, or a variety of other graphs and display mechanisms. Figure 8 In the example shown, five different subclones are displayed as bubbles, wherein the size of the bubble shows the size of the subclone relative to the entire tumor (52%, 21%, 15%, 10% and 2% respectively). In this example, the effect metric is shown via the filling (or lack thereof) of the bubble for each subclone. For example, the selected treatment (letrozole) is very effective for subclone D, and the bubble is not filled at all. For example, this can indicate that the treatment will kill or otherwise strongly affect (e.g., "shrink") subclone D. The treatment is ineffective for subclone C (e.g., subclone C is resistant), so the bubble is completely filled. As shown in the bubble filling, the effect of this treatment on subclones A, B and E is different. Many other methods of displaying the effect are also possible.

[0124] As shown in the figure, according to one embodiment, the treatment effect panel 710 can display other information. For example, the treatment effect panel in this example includes information 810 about the metastatic potential of the subclone, information 820 about the side effects of the selected treatment (letrozole), and the cost 830 of the selected treatment. This information and other information are also possible. In one example, the metastatic potential is visualized by the size and depth of color of the shaded block on top of each subclone, the size of the shaded block indicates the proportion of cells that are resistant to the selected treatment, and the depth of color indicates the invasiveness of the subclone.

[0125] refer to Fig. 9 , in one embodiment, Fig. 9is an example of a possible visualization of tumor heterogeneity using a hexagonal grid with cell groupings (a) and labels (b) that depict the relative sizes of tumor subclones. This possible visualization creates a framework for representing more specific information about the drug sensitivity and metastatic potential of each subclone, such as Fig.10 and Fig.11 shown.

[0126] refer to Fig.10 , in one embodiment, Fig.10 is an example of a possible visualization of tumor heterogeneity using a hexagonal grid with cell groupings for drug sensitivity. The spots in the center of the grid cells (d) provide a visual clue to the drug response of each of the subclones. The degree of shrinkage of the spots relative to the size of the grid cells (c) indicates the drug sensitivity of the subclone. In extreme cases, the central spot may disappear completely (e), indicating complete sensitivity; or the central spot may remain full size (f), marking resistance.

[0127] refer to Fig.11 , in one embodiment, Fig.11 is an example of a possible visualization of tumor heterogeneity using a hexagonal grid grouping cells with metastatic potential. In this example, different shades of color can be used to represent metastatic potential or other aspects of subclonal aggressiveness. More dangerous subclones get darker or more intense colors. This approach allows for a more precise description of treatment effects by combining the relative size of subclones, their drug sensitivity, and information about the actual danger they pose.

[0128] therefore, Fig. 9 , Fig.10 and Fig.11 The combination of enables possible visualization of tumor heterogeneity using a hexagonal grid with cell groupings, which simultaneously depicts relative tumor subclonal abundance, drug sensitivity, and metastatic potential.

[0129] refer to Fig.12 , in one embodiment, Fig.12 yes Figure 7B The drug selection panel 720 includes a list of one or more possible treatments (letrozole, everolimus, anastrozole, etc.) that can be used to treat one or more of the subclones. According to one embodiment, the tumor visualization system 200 obtains the possible treatments as a list from Figure 1 The treatment effect information portion of step 130 of method 100 .

[0130] According to one embodiment, for each possible treatment in one or more possible treatments that can be used to dispose of one or more of these subclones, the drug selection panel 720 shows the predicted effect of the treatment on each subclone in different subclones. This is similar to the indication of the effect shown in the treatment effect panel 710, but it is shown in a different way (although it can be shown in the same or similar way). In this example of the drug selection panel 720, the indication of the effect is shown as a bar graph for each subclone in different subclones, wherein the higher bar indicates a larger predicted effect (indicating, for example, a larger eradication or effect on subclone cells). For example, the selected treatment (letrozole) is invalid for subclone C, so the bar graph for subclone C is empty. The treatment is very effective for subclone D, so the bar graph for subclone D is full. As shown in these bar graphs, the therapeutic effects of subclones A, B and E are different.

[0131] refer to Fig.13 , in one embodiment, Fig.13 is an example of a possible visualization of an indication of treatment effect, where a more filled bar or block indicates a very effective treatment, while an unfilled bar or block indicates a very ineffective treatment. Hatching or other indications may indicate an uncertain effect of a treatment on a subclone represented by a bar or block.

[0132] refer to Fig.14 , in one embodiment, Fig.14 is an optional portion of the treatment effect panel 710 showing a legend 1400. The legend, which can be shown or hidden, shows a legend 1410 of metastatic potential, a legend 1420 of sensitivity, a legend 1430 of resistance, and a legend 1440 of severity of side effects.

[0133] refer to Fig.15 , in one embodiment, Fig.15 is an optional portion of the drug selection panel 720 showing a legend 1500. The legend, which can be shown or hidden, shows a legend of the clinical status of the treatment, including whether it is guideline approved, in clinical trials, or not guideline approved.

[0134] refer to Fig.16 , in one embodiment, Fig.16 yes Figure 7B1600. In this embodiment, the drug selection panel 720 includes the combined effects of two or more treatments on one or more of the subclones. In this example, everolimus and fulvestrant have been selected for treatment. The panel shows the individual effects of everolimus and fulvestrant on the subclones, and shows the combined effect of the two drugs, as well as information such as cumulative side effects and cost.

[0135] refer to Fig.17 , in one embodiment, Fig.17 It is an example of a possible visualization of the combined effects of two or more treatments on one or more of these subclones. As described above, the size of the dark area within each block represents the degree of influence of the drug on the corresponding subclone (d). The resulting image enables a rapid assessment of the therapeutic effect. The fewer blocks that remain light (not completely affected), the better the potential results. The total therapeutic effect can be shown as a combination of the effects of the component drugs. For each subclone, the merging algorithm promotes the strongest drug effect to be represented by a combined effect, regardless of the effects of other drugs. The combined drug effect visually merges the subclone blocks into a fusion representation of tumor sensitivity (dark area) and resistance (light area), while the single drug depiction (e) clearly separates the subclones to emphasize the specific interaction of each drug with each subclone.

[0136] refer to Fig.18 , in one embodiment, Fig.18 is an example of a possible visualization of the combined effects of two or more treatments on one or more of these subclones. In this example, the strongest subclone-drug interaction is represented in full intensity color, while other subclone-drug interactions are grayed out (g), in order to emphasize which drug contributes to the combined effect (f).

[0137] refer to Fig.19 , in one embodiment, Fig.19 is an example of a possible visualization that aids in further drug selection to improve the combined effect of two or more drugs on one or more of these subclones in the selected treatment. In this example, after the initial proposed treatment is established ( Fig.17 d), a list of drugs considered as possible additions is presented (e). In this list, subclone-drug interactions that are more effective than the currently selected treatment are highlighted. Drugs with the most beneficial interactions are pushed to the top of the list. Adding a new drug to the suggested treatment changes its beneficial profile and causes the list of remaining drugs to be reordered accordingly.

[0138] refer to Fig. 20 , in one embodiment, Fig. 202000 is a visualization of a treatment effect panel 710 in which two different possible treatments are compared. In this example, the effect of treatment 1 (everolimus) is compared to treatment 7 (letrozole + anastrozole). The comparison shows the effect of each treatment on five different subclones in the form of bubbles and bar graphs.

[0139] refer to Fig.21 , Fig.21 yes Fig. 20 A larger representation 2100 of a portion of the visualization 2000 in FIG. The comparison shows the metastatic potential of five different subclones in a direct side-by-side alignment of bar graphs. The comparison also shows side effects and costs in a direct side-by-side alignment, allowing comparison of each.

[0140] According to one embodiment, a user can interactively navigate between different panels, different subclones, different treatments, different treatment combinations, and other aspects of embodiments described herein or otherwise contemplated. Navigation can be accomplished using any user interface interaction method.

[0141] refer to Figure 2 , Figure 2 is a schematic representation of a tumor visualization system 200. The system 200 may be any system described herein or otherwise contemplated, and may include any components described herein or otherwise contemplated. It will be understood that Figure 2 An abstraction is constituted in some respects, and the actual organization of the components of system 200 may be different and more complex than that illustrated.

[0142] According to one embodiment, the system 200 includes a processor 220 that can run instructions stored in the memory 230 or the storage device 260, or otherwise process data to, for example, perform one or more steps of the method. The processor 220 may include one or more modules. The processor 220 may take any suitable form, including but not limited to a microprocessor, a microcontroller, multiple microcontrollers, a circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a single processor, or multiple processors.

[0143] Memory 230 can take any suitable form, including non-volatile memory and / or RAM. Memory 230 can include various memories, for example, L1, L2 or L3 cache memory or system memory. As such, memory 230 can include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read-only memory (ROM) or other similar memory devices. The memory can store operating systems, etc. The processor uses RAM to temporarily store data. According to one embodiment, the operating system may include code that controls the operation of one or more components of system 200 when run by the processor. It is apparent that in an embodiment where the processor implements one or more functions described herein in hardware, software described as corresponding to such functions in other embodiments may be omitted.

[0144] User interface 240 may include one or more devices for realizing communication with a user. A user interface can be any device or system that allows transmission and / or reception of information, and may include a display, a mouse and / or a keyboard for receiving user commands. In certain embodiments, user interface 240 may include a command line interface or a graphical user interface, which may be presented to a remote terminal via communication interface 250. The user interface may be located at one place with one or more other components of the system, or may be located at a position away from the system and communicate via a wired and / or wireless communication network.

[0145] The communication interface 250 may include one or more devices for implementing communication with other hardware devices. For example, the communication interface 250 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. In addition, the communication interface 250 may implement a TCP / IP stack for communication according to the TCP / IP protocol. Various alternatives or additional hardware or configurations for the communication interface 250 will be apparent.

[0146] The storage device 260 may include one or more machine-readable storage media, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, or a similar storage medium. In various embodiments, the storage device 260 may store instructions executed by the processor 220 or data that the processor 220 may operate on. For example, the storage device 260 may store an operating system 261 for controlling various operations of the system 200.

[0147] It will be apparent that various information described as being stored in storage device 260 may be stored in memory 230 in addition or in lieu thereof. In this regard, memory 230 may also be considered to constitute a storage device, and storage device 260 may be considered to be a memory. Various other arrangements will be apparent. Additionally, both memory 230 and storage device 260 may be considered to be non-transitory machine-readable media. The term "non-transitory" as used herein will be understood to exclude transient signals, but rather to include all forms of storage devices (including volatile memory and non-volatile memory).

[0148] Although system 200 is shown as including one such component for each described component, in various embodiments, various components may be repeated. For example, processor 220 may include multiple microprocessors that are configured to independently run the methods described herein, or are configured to perform the steps or subroutines of the methods described herein, so that multiple processors collaborate to implement the functions described herein. In addition, in the case of implementing one or more components of system 200 in a cloud computing system, various hardware components may belong to separate physical systems. For example, processor 220 may include a first processor in a first server and a second processor in a second server. Many other variations and configurations are also possible.

[0149] According to one embodiment, the electronic medical record database or system 270 is an electronic medical record database from which information about the patient and heterogeneity assessment, as well as other possible information, can be obtained or received. The electronic medical record database or system can be a local or remote database and communicate directly and / or indirectly with the tumor visualization system 200. Thus, according to one embodiment, the system includes the electronic medical record database or system 270.

[0150] According to another embodiment, the heterogeneity assessment is obtained by performing heterogeneity analysis on data from a LIMS, DICOM, and / or EMR system. Thus, the system may include or communicate with a patient imaging data system or database 272 (e.g., a LIMS or DICOM system or database). According to one embodiment, the system includes or communicates with a laboratory information management system 276 (including test or analysis data and / or other information). The system 200 is capable of receiving information from the laboratory information management system 276 and is capable of sending information to the laboratory information management system 276. The laboratory information management system 276 can be a local or remote system or database and communicates directly and / or indirectly with the tumor visualization system 200.

[0151] According to one embodiment, the treatment database 274 is a database from which information about the effects of one or more treatments on one or more tumors or subclones can be received or obtained. The treatment database can be a local or remote database and communicate directly and / or indirectly with the tumor visualization system 200. Thus, according to an embodiment, the system includes a treatment database 274.

[0152] According to one embodiment, the storage device 260 of the system 200 may store one or more algorithms, one or more modules, and / or one or more instructions to perform one or more functions or one or more steps of the methods described herein or otherwise contemplated. For example, the system may include patient information 262a, patient heterogeneity assessment data and / or instructions 262b, retrieval instructions 263, generation instructions 264, reporting instructions 265, treatment instructions 266, and other instructions or data.

[0153] According to one embodiment, the patient information 262a is any information about the patient, and optionally a corresponding heterogeneity assessment, that a tumor visualization system can or may utilize to perform an analysis as described herein or otherwise contemplated. According to one embodiment, the patient information includes one or more of: demographic and clinical information about the patient, identification of the patient's tumor type, and an assessment of the heterogeneity of the patient's tumor. Other information is also possible.

[0154] According to one embodiment, the patient heterogeneity assessment data and / or instructions 262b direct the system to perform a heterogeneity assessment or retrieve data necessary for a heterogeneity assessment. According to one embodiment, the heterogeneity assessment of a patient's tumor includes a qualitative or quantitative assessment of the patient's tumor heterogeneity as described herein or otherwise contemplated.

[0155] According to one embodiment, the retrieval instructions 263 direct the system to receive, retrieve or otherwise obtain treatment effect information for each of the two or more subclones of the tumor. This treatment effect information is identified and obtained based on the received identification of the patient's tumor type and the received heterogeneity assessment of the patient's tumor. The treatment effect can be based on the known effects of one or more treatments on the tumor type and the identified subclone type. The treatment effect information can be obtained, for example, from the treatment database 274.

[0156] According to one embodiment, the generation instruction 264 instructs the system to generate one or more visual representations described herein or otherwise contemplated. The operation can be performed immediately after receiving the patient information and obtaining the treatment effect information, or the operation can be performed using the stored information. The system can generate one or more visual representations as needed or as a result of programming or other automation. According to one embodiment, the system generates one or more visual representations in response to user input. For example, a physician or other health care professional can request this information. Alternatively, the system can be partially or fully automated so that one or more visual representations can be automatically calculated. For example, the system can be a stand-alone system or a component of a medical data or analysis system, which can include a user interface that displays information about the patient. The information can include one or more information and graphics described herein or otherwise contemplated.

[0157] According to one embodiment, the report instruction 265 instructs the system to provide one or more generated visual representations via a user interface. For example, the visual representation can be displayed to a medical professional or other user including a patient via the user interface of the system. The one or more generated visual representations can be provided to the user via any mechanism for displaying, visualizing or otherwise providing information via a user interface. According to one embodiment, the information can be transmitted to the user interface and / or another device via wired and / or wireless communication. For example, the system can transmit the information to a mobile phone, a computer, a laptop, a wearable device and / or any other device configured to allow display and / or other communications of the report. The user interface can be any device or system that allows the transmission and / or reception of information, and can include a display, a mouse and / or a keyboard for receiving user commands. As a non-limiting example only, the user interface can be a component of a patient monitoring system.

[0158] According to one embodiment, reporting instructions 265 direct the system to provide options for interacting with one or more provided visual representations via a user interface. For example, the provided display can include an input field or other interactive mechanism that allows a user to provide input. Thus, a user can provide input to the system via a user interface in response to information provided via a reporting mechanism. For example, a clinician can select a treatment to be administered to a patient, and can then report the treatment (optionally along with evidence of all available options) to a clinical decision support system or a patient management system for future audit purposes, etc.

[0159] According to one embodiment, treatment instructions 266 direct the system to manage treatment information. For example, treatment instructions 266 can manipulate treatment effect information for each of two or more subclones of a tumor, such as by creating and evaluating different combination treatment options. Treatment instructions 266 can, for example, evaluate and rate the effects of possible drug combinations on one or more subclones. Treatment effects can be based on the known effects of one or more treatments on the tumor type and the identified subclone type. Treatment effect information can be obtained, for example, from treatment database 274.

[0160] Thus, in the context of the present disclosure, aspects of an embodiment may take the form of a computer program product embodied in one or more non-transitory computer-readable media on which a computer-readable program code is embodied. Thus, according to one embodiment, a non-transitory computer-readable storage medium includes computer program code instructions that, when executed by a processor, enable the processor to perform the following method, the method comprising: (i) receiving information about a patient, the received information including demographic information and clinical information about the patient, identification of a tumor type for the patient, and an assessment of heterogeneity of the patient's tumor; (ii) obtaining treatment effect information for each of two or more subclones of the tumor based on the identification of the tumor type for the patient and the assessment of heterogeneity of the patient's tumor; (iii) generating a visual representation of the assessment of heterogeneity of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor; and (iv) providing the generated visual representation via a user interface of the system. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.

[0161] According to one embodiment, the tumor visualization system is configured to process thousands or millions of data points in the analysis of received patient information, in the retrieval and analysis of tumor effect information, in the generation of one or more visualizations, and in the display of the generated one or more visualizations. For example, analyzing the data, generating the visualizations, and providing the generated visualizations require processing millions of data points. This requires millions or billions of calculations, which cannot be completed by the human brain in a lifetime.

[0162] This novel tumor visualization system has a tremendous positive impact on patient care by providing improved visualization of tumor heterogeneity and treatment effects compared to prior art systems. A better understanding of tumor heterogeneity and treatment effects (fully made possible by the novel visualization provided herein) improves clinician analysis, thereby improving patient health and care, among other things.

[0163] Improved patient survival analysis and visualization

[0164] According to another aspect, a system and method configured to generate and provide a visual representation of a patient's survival is provided. More generally, the applicant has recognized and appreciated that it would be beneficial to provide an improved method and system for visualizing the probability of patient survival over time. Therefore, a survival statistics analysis system is described that generates and provides a visual representation of a patient's survival. The survival statistics analysis system receives information about a patient, the received information including at least a patient diagnosis and a diagnosis date. The system uses the patient information and a reference survival data set to calculate one or more survival statistics for the patient relative to a first historical patient group over a first time period. The system then generates a visual representation of the calculated one or more survival statistics, and provides the generated visual representation via a user interface of the system. According to one embodiment, the generated visual representation of the patient's survival includes one or more of the following: a survival function graph, a median survival graph, a mortality risk graph, and a survival probability graph.

[0165] refer to Fig. 22 , in one embodiment, Fig. 22 2200 is a flow chart of a method for generating and providing a visual representation of survival for a patient using a survival statistics analysis system. The methods described in conjunction with the accompanying drawings are provided only as examples and should be understood not to limit the scope of the present disclosure. The survival statistics analysis system can be any system described herein or otherwise contemplated. The survival statistics analysis system can be a single system or a plurality of different systems.

[0166] At step 2210 of the method, a survival statistics analysis system 2300 is provided. For example, referring to Fig.23 In the depicted embodiment of the survival statistics analysis system 2300, the system includes one or more of the following: a processor 2320, a memory 2330, a user interface 2340, a communication interface 2350, and a storage device 2360, which are interconnected via one or more system buses 2312. It will be understood that Fig.23In some respects, an abstraction is constituted, and the actual organization of the components of the system 2300 may be different from and more complex than that illustrated. In addition, the survival statistics analysis system 2300 can be any system described or otherwise contemplated herein. Other elements and components of the survival statistics analysis system 2300 are also disclosed and / or otherwise contemplated herein.

[0167] At step 2220 of the method, the survival statistics analysis system receives information about the patient. Patient information can be any information about the patient, and the survival statistics analysis system can or can use the information to perform analysis as described herein or otherwise contemplated. According to one embodiment, the patient information includes one or more of the following: demographic information about the patient, the diagnosis of the patient, the patient's medical history, and / or any other information. For example, demographic information may include information about the patient (e.g., name, age, body mass index (BMI), and any other demographic information). The diagnosis of the patient can be any information (historical and / or current) about the medical diagnosis of the patient. The patient's medical history can be any historical admission or discharge information, historical treatment information, historical diagnosis information, historical examination or imaging information, and / or any other information.

[0168] Patient information is received from one or more different sources. According to one embodiment, patient information is received, retrieved or otherwise obtained from an electronic medical record (EMR) database or system 2370. The EMR database or system can be local or remote. The EMR database or system can be a component of the survival statistics analysis system, or can communicate locally and / or remotely with the survival statistics analysis system. The received patient information can be used immediately, or the received patient information can be stored in a local or remote storage device for use in additional steps of the method.

[0169] At step 2230 of the method, the survival statistics analysis system calculates one or more survival statistics for the patient. Although the calculated output is identified herein as a "survival statistic," it should be understood that the term is non-limiting and may mean a probability, likelihood, or estimate for the patient (including death, survival, and / or many other probabilities, likelihoods, or estimates for the patient) among other possible definitions.

[0170] The system can calculate one or more survival statistics for a patient as needed or as a result of programming or other automation. According to one embodiment, the survival statistics analysis system calculates one or more survival statistics for a patient in response to user input. For example, a physician or other health care professional can request the information. Alternatively, the survival statistics analysis system can be partially or fully automated, enabling automatic calculation of survival statistics for a patient. For example, the system can be a stand-alone system or a component of a medical data or analysis system, which can include a user interface that displays information about a patient. The information can include one or more survival statistics and graphics described herein or otherwise contemplated.

[0171] One or more survival statistics for a patient are calculated according to any method described herein or otherwise contemplated. For example, several different non-limiting methods and examples for calculating one or more survival statistics for a patient are described below. The system can utilize these methods and many other methods.

[0172] According to one embodiment, the survival statistics analysis system calculates one or more survival statistics for a patient over a predetermined or user-defined first time period. The system can calculate one or more survival statistics over any time period. Non-limiting examples of the first time period include days, weeks, months, years, and decades. For example, the system can calculate one or more survival statistics over a five-year time period, which is a time period commonly used for survival data, but the first time period can also be shorter or longer than five years.

[0173] According to one embodiment, the survival statistics analysis system includes or communicates with a reference survival data set that is used to calculate one or more survival statistics for a patient. The reference survival data set can be received from one or more different sources. According to one embodiment, the reference survival data set is received, retrieved, or otherwise obtained from an electronic medical record (EMR) database or system 2370. The EMR database or system can be local or remote. The EMR database or system can be a component of the survival statistics analysis system, or can communicate locally and / or remotely with the survival statistics analysis system. Alternatively, the reference survival data set can be a remote data set or another storage data set of the system. The received reference survival data set can be used immediately, or the received reference survival data set can be stored in a local or remote storage device for use in other steps of the method.

[0174] According to one embodiment, the reference survival data set includes information about the following one or more historical patients: the one or more historical patients can be collected, filtered or otherwise combined into one or more patient groups. For example, a patient group can be a collection of the following multiple historical patients: the survival data of the multiple historical patients at least in the first time period is available. The patient group can be composed of historical patients with the same diagnosis, condition, treatment or other similarities as the patient for which one or more survival statistics are being calculated. Therefore, according to one embodiment, the system identifies a patient group related to the patient or object being analyzed. According to another embodiment, the system identifies a patient group related to the patient or object being analyzed and a patient group that does not have the same diagnosis, condition, treatment or other similarities as the patient for which one or more survival statistics are being calculated. For example, the latter embodiment enables analysis or determination of the impact of a diagnosis, condition, treatment or other similarity on the patient for which one or more survival statistics are being calculated.

[0175] According to one embodiment, the calculated survival statistics for the patient are used immediately or can be stored in a local or remote storage device for use in further steps of the method. Thus, for example, whenever the display is pointed at or otherwise designed or programmed to display patient information, the stored survival statistics for the patient can be called up and utilized.

[0176] At step 2240 of the method, the survival statistics analysis system generates one or more visual representations of the calculated one or more survival statistics. The operation can be performed immediately after the one or more survival statistics are calculated, or the operation can be performed using the stored survival statistics. The system can generate one or more visual representations of the calculated one or more survival statistics as needed or as a result of programming or other automation. According to one embodiment, the survival statistics analysis system generates one or more visual representations in response to user input. For example, a physician or other health care professional can request this information. Alternatively, the survival statistics analysis system can be partially or fully automated, so that one or more visual representations can be automatically calculated. For example, the system can be a stand-alone system or a component of a medical data or analysis system, which can include a user interface that displays information about the patient. The information may include one or more survival statistics and graphics described herein or otherwise contemplated.

[0177] One or more visual representations of the calculated one or more survival statistics are generated according to any method described herein or otherwise contemplated. For example, several different non-limiting methods and examples of visual representations of the calculated survival statistics are described below, and thus the system can be designed or programmed to generate such visual representations using known methods and mechanisms for graphically representing data. The system can utilize these methods and many other methods.

[0178] Once the one or more visual representations of the calculated one or more survival statistics are generated, the one or more visual representations may be used immediately or may be stored in a local or remote storage device for use in further steps of the method.

[0179] At step 2250 of the method, the survival statistics analysis system provides one or more visual representations of the one or more survival statistics calculated via a user interface. For example, the visual representation can be displayed to a medical professional or other user including a patient via the user interface of the system. The one or more visual representations generated can be provided to the user via any mechanism for displaying, visualizing or otherwise providing information via a user interface. According to one embodiment, the information can be transmitted to the user interface and / or another device via wired and / or wireless communication. For example, the system can transmit the information to a mobile phone, a computer, a laptop, a wearable device and / or any other device configured to allow display and / or other communications of the report. The user interface can be any device or system that allows the transmission and / or reception of information, and can include a display, a mouse and / or a keyboard for receiving user commands. As a non-limiting example only, the user interface can be a component of a patient monitoring system.

[0180] According to one embodiment, the display may also include patient information, such as demographic information about the patient, a diagnosis of the patient, the patient's medical history, and / or any other information.

[0181] According to one embodiment, some or all of the displayed information can be manipulated in response to user input provided via a user interface, as described herein.

[0182] The survival statistics analysis system provides a generated visual representation of one or more calculated survival statistics via a user interface, which visual representation can be any of a variety of different statistics and visual representations. According to one embodiment, the visual representation includes a survival function graph. The survival function graph can include a single graph showing two different (preferably overlapping) survival statistics. The first survival statistic of the survival function graph is a Kaplan-Meier estimation plot for a first historical group in a first time period. The Kaplan-Meier estimate is a statistic for estimating a survival function over time. In a health care environment, the Kaplan-Meier estimate can be a measure of the proportion of patients or objects that survive a certain amount of time after diagnosis or treatment. According to one embodiment, the Kaplan-Meier estimation plot in the graph is based on comprehensive data from multiple Kaplan-Meier type estimators. The survival function can present overall survival data, disease-free survival or progression-free survival, as well as other options. The second survival statistic of the survival function graph is a visualization of deaths within the same period, including a trailing death plot. The trailing death includes the percentage of deaths in the trailing time period for the first historical group, wherein the trailing time period is a windowed segment of the first time period. The trailing time period can be any time period as a subset or fragment of the first time period. For example, the trailing time period can be a few weeks, a few months, or a few years. According to one embodiment, the trailing time period is six months, but it can be shorter or longer. According to another embodiment, the trailing time period is one year, but it can be shorter or longer. The trailing time period can be predetermined or preprogrammed, or can be determined entirely or in part based on a user input received via a user interface.

[0183] According to one embodiment, the first historical group for this graph and any other graphs includes a collection of multiple historical patients for which survival data for at least a first time period is available, and the multiple historical patients are collected, filtered, or otherwise retrieved from a reference survival data set. For example, the first historical group can be composed of historical patients with the same diagnosis, condition, treatment, or other similarity as the patient for whom one or more survival statistics are being calculated. As described herein, the reference survival data set (and the first historical group) can be received or generated from one or more different sources, and the reference survival data set can be a component of the survival statistics analysis system, or can be in local and / or remote communication with the survival statistics analysis system (e.g., a remote data set or another stored data set of the system).

[0184] According to one embodiment, the first time period for the graph and any other graphs includes any time period in which survival statistics are desired. According to another embodiment, the time period is five years, but it can be shorter or longer. The time period can be predetermined or preprogrammed, or can be determined in whole or in part based on user input received via a user interface.

[0185] refer to Fig.24A and Fig. 24B , Fig.24A and Fig. 24B is a survival function graph 3000. Each survival function graph includes five years of survival statistics since the initial diagnosis, although described as such in this article, this is a non-limiting option. The time frame can be longer or shorter, and the starting point can be the initial diagnosis, treatment, or any other determined starting event. Each survival function graph includes a Kaplan-Meier estimate 3010 and a trailing death plot 3020. To emphasize the survival chances over the entire time period (five years in this case), the last point on the survival function is the final survival percentage 3030 (in this example, the 5-year survival percentage of 10%).

[0186] refer to Fig.24A , in this example, the graph includes a first historical cohort having a specifically determined tumor heterogeneity index of 4.0, which indicates a higher level of heterogeneity in the cells within the sampled tumor (e.g., compared to indices of 1.0, 2.0, 3.0, and variations thereof). In this example, a higher heterogeneity index is associated with a lower probability of survival, including a 5-year survival percentage of 10%. Reference Fig. 24B In this example, the graph includes a first historical group with a specifically determined tumor heterogeneity index of 1.0, which indicates a low level of heterogeneity in the cells within the sampled tumor. In this example, a higher heterogeneity index is associated with a higher probability of survival, including a 5-year survival percentage of 47%.

[0187] According to another embodiment, the visual representation includes a median survival graph. The median survival graph can include a single graph displaying several different (preferably overlapping) pieces of information. The first information provided by the graph is the identification of the median survival time for the first historical group in the first time period. The second piece of information is a plot of the percentage of death for the first historical group in the pre-median survival time period, which will be displayed on the left side of the median survival time. The third piece of information is a plot of the percentage of survival for the first historical group in the post-median survival time period, which will be displayed on the right side of the median survival time.

[0188] According to one embodiment, this novel method of representing survival time focuses on the median survival time. For untrained users (e.g., patients), this method helps identify the average life expectancy of a group and the probability of dying near the group average time.

[0189] According to one embodiment, for the time period before and after the median survival time (MST), the graph is divided into two distinct sections using different functions. In the interval before the MST, the graph tracks the mortality rate (i.e., as a percentage of the entire group). In the period after the MST, the graph uses the survival function - the number of survivors (i.e., as a percentage of the entire group). This composite vision focuses attention on the average survival time. The second important benefit of this approach is a clear depiction of the probability of death near the MST, represented by the steepness of the "median peak".

[0190] refer to Fig.25A and Fig.25B , Fig.25A and Fig.25B are median survival graphs 4000. Each median survival graph includes five years of survival statistics since initial diagnosis, although described as such in this article, this is a non-limiting option. The time frame can be longer or shorter, and the starting point can be initial diagnosis, treatment, or any other determined starting event. Each median survival graph includes the median survival time 4010, a pre-median plot 4020 of the mortality rate (i.e., as a percentage of the entire group), and a post-median plot 4030 of the number of survivors (i.e., as a percentage of the entire group). To emphasize the chance of survival over the entire time period (five years in this case), the last point on the survival function is the final survival percentage 4040 (in this example, the 5-year survival percentage of 10%). These graphs illustrate how deaths are concentrated in time periods near the MST, which gives more meaning to the averages ( Fig.25A Graphics in THI = 4.0) or more expansion ( Fig.25B The graph in THI=1.0) (lowers its predicted value).

[0191] refer to Fig.25B To provide additional information, the visual image can be layered with a mortality graph 4050 that shows deaths in the trailing period (i.e., as a percentage of the original cohort). For example, the trailing period in this figure is 6 months, but the trailing period can be shorter or longer.

[0192] refer to Fig.25A, in this example, the graph includes a first historical group with a specifically determined tumor heterogeneity index of 4.0, which indicates a high level of heterogeneity in the cells within the sampled tumor. In this example, a higher heterogeneity index is associated with a lower probability of survival, including a 5-year survival percentage of 10%. Reference Fig.25B In this example, the graph includes a first historical group with a specifically determined tumor heterogeneity index of 1.0, which indicates a low level of heterogeneity in the cells within the sampled tumor. In this example, a higher heterogeneity index is associated with a higher probability of survival, including a 5-year survival percentage of 47%.

[0193] According to another embodiment, the visual representation includes a mortality risk graph. The mortality risk graph can include a single graph showing a trailing mortality plot. The trailing mortality plot includes the percentage of deaths of a first historical group during a trailing time period relative to the number of survivors of the first historical group at the beginning of the trailing time period, wherein the trailing time period is a windowed segment of the first time period. The trailing time period can be any time period that is a subset or fragment of the first time period. For example, the trailing time period can be weeks, months, or years. According to one embodiment, the trailing time period is six months, but it can be shorter or longer. According to another embodiment, the trailing time period is one year, but it can be shorter or longer. The trailing time period can be predetermined or preprogrammed, or can be determined in whole or in part based on user input received via a user interface.

[0194] According to one embodiment, a method of calculating mortality reveals changes in mortality risk over a period of time since a triggering event. The period of time can be any period of time, and the triggering event can be any triggering event, including but not limited to diagnosis, treatment, and other events. According to one embodiment, non-survival risk is calculated by expressing the number of deaths as a percentage of the number of survivors in each period of time. This data view reveals which time periods within the total time period since the triggering event are the most dangerous.

[0195] According to one embodiment, the data for the mortality risk graph is calculated using the following formula:

[0196] Mr=D(time) / S (Formula 1)

[0197] where Mr is the mortality risk, D is the number of deaths during the follow-up period (time), and S is the number of survivors at the beginning of that period.

[0198] refer to Fig.26A and Fig.26B , Fig.26A and Fig.26Bis the mortality risk graph 5000. Each mortality risk graph includes survival statistics for five years since the initial diagnosis, although described as such herein, this is a non-limiting option. The time frame can be longer or shorter, and the starting point can be the initial diagnosis, treatment, or any other defined starting event. Reference Fig.26A , in this example, the graph includes a first historical cohort having a specifically determined tumor heterogeneity index of 4.0, indicating a high level of heterogeneity in the cells within the sampled tumor. Fig.26B , in this example, the graph includes a first historical cohort having a particular determined tumor heterogeneity index of 1.0, indicating a low level of heterogeneity among cells within the sampled tumor.

[0199] According to another embodiment, the visual representation includes a survival probability graph. The survival probability graph includes a plot of survival probabilities over a first time period. The probability of survival for each of a plurality of time points over the first time period is calculated by dividing the total number of survivors of the first historical cohort at the end of the first time period by the total number of survivors of the first historical cohort at the time point being calculated.

[0200] According to one embodiment, this view focuses on the positive aspects of moving along the survival probability timeline. It illustrates the changes in the outlook of patients that have moved forward from the time of diagnosis (or another triggering event). According to one embodiment, the probability of surviving to the end of the time period (e.g., five years) is calculated by comparing the number of all survivors of the total time interval with the number of survivors at each represented time point. This data view can help visualize the hope for the upcoming period when combined with a marker representing the time elapsed since the patient's diagnosis.

[0201] According to one embodiment, the following formula is used to calculate the data for the survival probability graph:

[0202] Survival probability (time) = S (time) / S (moment) (Formula 2)

[0203] where S(time) is the number of survivors at the end of the total time period (time), and S(time) is the total number of survivors at time along the graph.

[0204] refer to Fig.27A and Fig.27B , Fig.27A and Fig.27B is a survival probability graph 6000. Each survival probability graph includes survival statistics for five years since initial diagnosis, although described as such herein, this is a non-limiting option. The time frame can be longer or shorter, and the starting point can be initial diagnosis, treatment, or any other defined starting event. Reference Fig.27A, in this example, the graph includes a first historical cohort having a specifically determined tumor heterogeneity index of 4.0, indicating a high level of heterogeneity in the cells within the sampled tumor. Fig.27B , in this example, the graph includes a first historical cohort having a specifically determined tumor heterogeneity index of 1.0, indicating a low level of heterogeneity among cells within the sampled tumor.

[0205] At step 2260 of the method, the user provides input about one or more visualizations displayed via the user interface of the survival statistics analysis system 2000. The input can be any input, including but not limited to commands to modify visualization, add visualization, remove visualization, and / or any other input or command. For example, the user interface can be configured to allow the user to navigate between two or more visualizations. The user interface can be configured to display one, two or more visualizations at a time. The user can be a health care professional, a patient, and / or any other user.

[0206] Therefore, at step 2270 of the method, the survival statistics analysis system regenerates one or more visualizations based on the received input, thereby, for example, returning to the previous step of the method and generating a visualization based on the input. Alternatively, the system can modify the visualization based on the received input. As just one example, a user can adjust one or more of the first historical group, the first time period, and the trailing time period for one or more visualizations. This will require the system to recalculate and / or regenerate the visualization, and then be able to display the recalculated and / or regenerated visualization via the user interface. Many other modifications are also possible.

[0207] According to one embodiment, any of the graphs described above or otherwise contemplated herein may include multiple plots, each of which is calculated using a different historical group. Fig.28 In the different survival function plots in Figure 3000, each survival function plot includes a different historical group, each historical group has a different tumor heterogeneity index (THI). As another example, in Fig.29 In the different median survival graphs 4000 in FIG. 4 , each median survival graph includes a different historical group, and each historical group has a different THI. As another example, in Fig.30 In the different death risk graphs 5000 in FIG. 1 , each death risk graph 5000 includes a different historical group, and each historical group has a different THI. As another example, in Fig.31 In the different survival probability graphs 6000, each survival probability graph includes a different historical group, and each historical group has a different THI.

[0208] refer to Fig.23 , Fig.23 is a schematic representation of a survival statistics analysis system 2300. System 2300 may be any system described herein or otherwise contemplated, and may include any components described herein or otherwise contemplated. It will be understood that Figure 1 In some ways, Fig.23 An abstraction is constituted in some respects, and the actual organization of the components of system 2300 may be different and more complex than that illustrated.

[0209] According to one embodiment, the system 2300 includes a processor 2320 that can run instructions stored in the memory 2330 or the storage device 2360 or otherwise process data to, for example, perform one or more steps of the method. The processor 2320 may include one or more modules. The processor 2320 may take any suitable form, including but not limited to a microprocessor, a microcontroller, multiple microcontrollers, a circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a single processor, or multiple processors.

[0210] Memory 2330 can take any suitable form, including non-volatile memory and / or RAM. Memory 2330 can include various memories, such as L1, L2 or L3 cache memory or system memory. As such, memory 2330 can include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read-only memory (ROM) or other similar memory devices. The memory can store operating systems, etc. The processor uses RAM to temporarily store data. According to one embodiment, the operating system may include code that controls the operation of one or more components of system 2300 when run by the processor. It is apparent that in an embodiment where the processor implements one or more functions described herein in hardware, software described as corresponding to such functions in other embodiments may be omitted.

[0211] User interface 2340 may include one or more devices for realizing communication with a user. User interface can be any device or system that allows transmission and / or reception of information, and may include a display, a mouse and / or a keyboard for receiving user commands. In certain embodiments, user interface 2340 may include a command line interface or a graphical user interface, which may be presented to a remote terminal via communication interface 2350. User interface may be located at one place with one or more other components of system, or may be located at a position away from system and communicate via wired and / or wireless communication network.

[0212] The communication interface 2350 may include one or more devices for implementing communication with other hardware devices. For example, the communication interface 2350 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. In addition, the communication interface 2350 may implement a TCP / IP stack for communication according to the TCP / IP protocol. Various alternatives or additional hardware or configurations for the communication interface 2350 will be apparent.

[0213] The storage device 2360 may include one or more machine-readable storage media, such as a read-only memory (ROM), a random access memory (RAM), a disk storage medium, an optical storage medium, a flash memory device, or a similar storage medium. In various embodiments, the storage device 2360 may store instructions executed by the processor 2320 or data that the processor 2320 can operate on. For example, the storage device 2360 may store an operating system 2361 for controlling various operations of the system 2300.

[0214] It will be apparent that various information described as being stored in storage device 2360 may be stored in memory 2330 in addition or in lieu thereof. In this regard, memory 2330 may also be considered to constitute a storage device, and storage device 2360 may be considered to be a memory. Various other arrangements will be apparent. In addition, both memory 2330 and storage device 2360 may be considered to be non-transitory machine-readable media. The term "non-transitory" as used herein will be understood to exclude transient signals, but rather to include all forms of storage devices (including volatile memory and non-volatile memory).

[0215] Although system 2300 is shown as including one such component for each described component, in various embodiments, various components may be repeated. For example, processor 2320 may include multiple microprocessors that are configured to independently run the methods described herein, or are configured to perform the steps or subroutines of the methods described herein, so that multiple processors collaborate to implement the functions described herein. In addition, in the case of implementing one or more components of system 2300 in a cloud computing system, various hardware components may belong to separate physical systems. For example, processor 2320 may include a first processor in a first server and a second processor in a second server. Many other variations and configurations are also possible.

[0216] According to one embodiment, the electronic medical record system 2370 is an electronic medical record database from which information about a patient and / or a reference survival data set can be obtained or received. The electronic medical record database can be a local or remote database and communicate directly and / or indirectly with the survival statistics analysis system 2300. Therefore, according to one embodiment, the system includes an electronic medical record database or system 2370.

[0217] According to one embodiment, the storage device 2360 of the system 2300 may store one or more algorithms, one or more modules, and / or one or more instructions to perform one or more functions or one or more steps of the methods described herein or otherwise contemplated. For example, the system may include patient information 2362, reference data sets 2363, calculation instructions 2364, generation instructions 2365, and / or reporting instructions 2366, as well as other instructions or data.

[0218] According to one embodiment, patient information 2362 is any information about a patient that a survival statistics analysis system can or may utilize to perform an analysis as described herein or otherwise contemplated. According to one embodiment, patient information includes one or more of the following: demographic information about the patient, a diagnosis of the patient, the patient's medical history, and / or any other information. For example, demographic information may include information about the patient (e.g., name, age, body mass index (BMI), and any other demographic information). The diagnosis of the patient may be any information (historical and / or current) about the patient's medical diagnosis. The patient's medical history may be any historical admission or discharge information, historical treatment information, historical diagnostic information, historical examination or imaging information, and / or any other information. Patient information may be stored in a storage device 2360 of system 2300, and / or may be stored in an EMR database or system 2370, etc.

[0219] According to one embodiment, the reference survival data set 2363 includes information about the following one or more historical patients: the one or more historical patients can be collected, filtered or otherwise combined into one or more patient groups. For example, a patient group can be a collection of the following multiple historical patients: the survival data of these multiple historical patients for at least a first time period is available. The patient group can be composed of historical patients with the same diagnosis, condition, treatment or other similarities as the patient for whom one or more survival statistics are being calculated. The reference survival data set 2363 can be stored in the storage device 2360 of the system 2300, and / or can be stored in the EMR database or system 2370, etc.

[0220] According to one embodiment, the calculation instructions 2364 direct the system to calculate one or more survival statistics for the patient. The system can calculate one or more survival statistics for the patient as needed or as a result of programming or other automation. The one or more survival statistics for the patient are calculated according to any method described or otherwise contemplated herein. For example, several different non-limiting methods and examples for calculating one or more survival statistics for the patient are described herein. The system can utilize these methods and many other methods.

[0221] According to one embodiment, the generation instruction 2365 instructs the system to generate one or more visual representations of the calculated one or more survival statistics. The operation can be performed immediately after the one or more survival statistics are calculated, or the operation can be performed using the stored survival statistics. The system can generate one or more visual representations of the calculated one or more survival statistics as needed or as a result of programming or other automation. According to one embodiment, the survival statistics analysis system generates one or more visual representations in response to user input. For example, a physician or other health care professional can request this information. Alternatively, the survival statistics analysis system can be partially or fully automated, so that one or more visual representations can be automatically calculated. For example, the system can be a stand-alone system or a component of a medical data or analysis system, which can include a user interface that displays information about the patient. The information may include one or more survival statistics and graphics described herein or otherwise contemplated.

[0222] According to one embodiment, the report instruction 2366 instructs the system to provide one or more visual representations of the calculated one or more survival statistics generated via a user interface. For example, the visual representation can be displayed to a medical professional or other user including a patient via the user interface of the system. The generated one or more visual representations can be provided to the user via any mechanism for displaying, visualizing or otherwise providing information via a user interface. According to one embodiment, the information can be transmitted to the user interface and / or another device via wired and / or wireless communication. For example, the system can transmit the information to a mobile phone, a computer, a laptop, a wearable device and / or any other device configured to allow display and / or other communications of the report. The user interface can be any device or system that allows the transmission and / or reception of information, and can include a display, a mouse and / or a keyboard for receiving user commands. As a non-limiting example only, the user interface can be a component of a patient monitoring system.

[0223] Thus, in the context disclosed herein, aspects of the embodiments may take the form of a computer program product embodied in one or more non-transitory computer-readable media having computer-readable program code embodied thereon. Thus, according to one embodiment, a non-transitory computer-readable storage medium comprising computer program code instructions is provided, which when executed by a processor enables the processor to perform the following method, the method comprising: (i) receiving information about a patient, the received information comprising at least a patient diagnosis and a diagnosis date; (ii) using the patient information and a reference survival data set to calculate one or more survival statistics for the patient relative to a first historical patient group over a first time period; and (iii) generating a visual representation of the calculated one or more survival statistics; and (iv) providing the generated visual representation via a user interface of the system. The program code may be executed entirely on a user's computer, partially on a user's computer, as a stand-alone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0224] According to one embodiment, the survival statistics analysis system is configured to process thousands or millions of data points in calculating one or more survival statistics for a patient in a first time period, in generating one or more visualizations of the one or more survival statistics calculated for the patient, and in displaying the one or more generated visualizations. For example, using data from 100 or 1000 patients in a historical cohort to calculate statistics and then plotting these calculated statistics requires processing millions of data points. This requires millions or billions of calculations, which cannot be completed by the human brain in a lifetime.

[0225] In addition, the survival statistics analysis system can be configured to continuously receive new data for the reference survival data set. The system can then update the calculations, visualizations, and displays. This requires continuous analysis of thousands or millions of data points to optimize the display, which requires a large amount of calculation and analysis that the human brain cannot accomplish in a lifetime.

[0226] This novel survival statistics analysis system has a tremendous positive impact on patient care by providing improved visualization of survival and mortality statistics compared to prior art systems. A better understanding of survival and mortality statistics facilitated by the novel visualization provided herein can facilitate improved health and care for patients, among other things.

[0227] The present invention may be a system, method and / or computer program product. A computer program product may include (one or more) non-transient computer-readable storage media, on which (one or more) non-transient computer-readable storage media are computer-readable program instructions, which are used to enable a system or processor to perform various aspects of the present invention. A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing devices. The "computer-readable program instructions" described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or can be downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, and / or a wireless network). Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, devices, systems, and computer program products according to embodiments of the present invention. It will be understood that each box of a flowchart and / or block diagram and a combination of boxes in a flowchart and / or block diagram can be implemented by a computer-readable program instruction.

[0228] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0229] Unless explicitly stated to the contrary, the terms "a" and "an" as used in this specification and the claims should be understood to mean "at least one."

[0230] The phrase "and / or" as used in this specification and claims should be understood to mean "either or both" of the elements so combined, i.e., elements that are present in combination in some cases and separately in other cases. Multiple elements listed with "and / or" should be interpreted in the same manner, i.e., "one or more" of the elements so combined. In addition to the elements specifically identified with the "and / or" clause, other elements may optionally be present, whether related or unrelated to those elements specifically identified.

[0231] "Or" used in the specification and claims herein should be understood to have the same meaning as "and / or" defined above. For example, when separating items in a list, "or" or "and / or" should be interpreted as inclusive, that is, including at least one, but also including more than one element of a plurality of elements or element lists and (optionally) additional unlisted items. Only terms that clearly indicate the opposite (e.g., "only one of" or "exactly one of" or "consisting of" used in the claims) will refer to including exactly one element in a plurality of elements or element lists. In general, when there is an exclusive term (e.g., "any one of", "one of", "any one of" or "exactly one of") in front of the term "or" used herein, the term "or" should only be interpreted as representing an exclusive alternative (i.e., "one or the other but not both").

[0232] The phrase "at least one" used herein in the specification and claims referring to a list of one or more elements should be understood to mean at least one element selected from any one or more elements in the list of elements, but not necessarily including at least one element of each element specifically listed in the list of elements, and not excluding any combination of elements in the list of elements. This definition also allows for optional elements other than the elements specifically identified in the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.

[0233] It should also be understood that in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order in which the steps or actions of the method are described unless explicitly stated to the contrary.

[0234] In the claims and the description above, all transitional phrases (e.g., "including," "comprising," "carrying," "having," "containing," "involving," "holding," "with," etc.) should be construed as open-ended, i.e., meaning including, but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" should be closed or semi-closed transitional phrases, respectively.

[0235] Although several inventive embodiments have been described and illustrated herein, a person of ordinary skill in the art will readily appreciate various other units and / or structures for performing functions and / or obtaining results and / or one or more of the advantages described herein. Each of such changes and / or modifications is considered to be within the scope of the inventive embodiments described herein. More generally, it will be readily understood by those skilled in the art that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and that actual parameters, dimensions, materials, and / or configurations will depend on one or more specific applications using the teachings of the present invention. Those skilled in the art will recognize or be able to use experimental means not exceeding conventional means to determine many equivalents of the specific inventive embodiments described herein. Therefore, it should be understood that the foregoing embodiments are presented only as examples, and within the scope of the claims and their equivalents, embodiments of the present invention may be practiced in a manner different from that specifically described and claimed. The inventive embodiments of the present disclosure relate to each individual feature, system, article, material, kit, and / or method described herein. In addition, if such features, systems, articles, materials, kits, and / or methods are not mutually contradictory, any combination of two or more such features, systems, articles, materials, kits, and / or methods can be included within the scope of the invention of the present disclosure.

Claims

1. A method (100) for visualizing the heterogeneity of a patient's tumor and the effect of a treatment on the patient's heterogeneous tumor, comprising: receiving (120) information about a patient, the received information including demographic and clinical information about the patient, identification of a tumor type for the patient, and an assessment of heterogeneity of the patient's tumor, the assessment of heterogeneity including analysis of heterogeneity of the patient's tumor and identification of two or more subclones of the tumor; obtaining (130) treatment effect information for each of the two or more subclones of the tumor based on the identification of the patient's tumor type and the assessment of heterogeneity of the patient's tumor; generating (140) a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor; and providing (150) the generated visual representation via a user interface of the system, wherein the visual representation comprises:

1. Tumor heterogeneity graph, including: (i) an identification of each of the two or more subclones of the patient's tumor, wherein the identification includes an indication of the relative amount of each subclone in the patient's tumor; and (ii) a heterogeneity score that indicates the overall complexity of the patient's tumor composition, wherein the heterogeneity score is visualized in an ordinal data plot of heterogeneity scores of the same tumor type from a cohort of patients, thereby indicating the degree of heterogeneity in the patient's tumor as a percentile rank within the cohort of patients; and 2. A treatment effect graph comprising an indication of the effect of each of a plurality of different treatments on each of the two or more subclones of the patient's tumor.

2. The method according to claim 1, wherein: The generated visual representation also includes some or all of the demographic information and the clinical information about the patient.

3. The method according to claim 1, wherein: The tumor heterogeneity profile also includes an indication of the metastatic potential of each subclone in the patient's tumor.

4. The method according to claim 1, wherein: The two or more subclones are distinguished by one or more molecular and / or histological variations.

5. The method according to claim 4, wherein: The tumor heterogeneity profile also includes identification of some or all of the one or more molecular and / or histological variations that are unique to each of the two or more subclones.

6. The method according to claim 1, wherein: Each of the two or more subclones of the patient's tumor on the tumor heterogeneity graph is selectable, and wherein selecting a subclone results in a visual display of some or all of one or more molecular and / or histological variations unique to the selected subclone.

7. The method according to claim 1, wherein: The treatment effect graph also includes an indication of the effect of a combination of two or more treatments on the two or more subclones of the patient's tumor.

8. The method according to claim 1, wherein: The tumor heterogeneity graph and / or the treatment effect graph further includes one or more of the following: side effect information for each of the plurality of different treatments, and a cost for each of the plurality of different treatments.

9. The method according to claim 1, further comprising the steps of: A selection of one or more of the plurality of different treatments to be administered to the patient is received (160) from a user via a user interface, wherein the selected one or more of the plurality of different treatments are optionally reported to a clinical decision support system.

10. The method according to claim 1, further comprising: calculating ( 2230 ) one or more survival statistics for the patient over a first time period relative to a first historical patient cohort using the received patient information and a reference survival data set; generating (2240) a visual representation of the calculated one or more survival statistics; and The generated visual representation is provided (2250) via a user interface of the system, wherein the one or more survival statistics include one or more of:

1. A survival function graph, the survival function graph comprising a single graph having: (i) a Kaplan-Meier estimate plot for the first historical group during the first time period; and (ii) a trailing death plot comprising the percentage of deaths of the first historical group during a trailing time period, the trailing time period being a windowed segment of the first time period; 2. A median survival graph, the median survival graph comprising a single graph having: (i) an identification of the median survival time for the first historical cohort during the first time period; and (ii) a plot of the percentage of deaths for the first historical cohort during the pre-median survival time period; and (iii) a plot of the percentage of survival for the first historical cohort during the post-median survival time period; 3. a mortality risk graph, the mortality risk graph comprising a trailing mortality plot, the trailing mortality plot comprising a percentage of deaths of the first historical group during a trailing time period relative to the number of survivors of the first historical group at the beginning of the trailing time period, the trailing time period being a windowed segment of the first time period; and 4. A survival probability graph, comprising a plot of survival probabilities within the first time period, wherein the survival probability is calculated for each of a plurality of time points within the first time period by dividing the total number of survivors of the first historical group at the end of the first time period by the total number of survivors of the first historical group at the time point being calculated.

11. A system (200) for providing visualization of the heterogeneity of a patient's tumor and the effect of a treatment on the patient's heterogeneous tumor, comprising: Patient information (262) comprising at least demographic and clinical information about the patient, identification of the patient's tumor type, and an assessment of heterogeneity of the patient's tumor, the heterogeneity assessment comprising an analysis of the heterogeneity of the patient's tumor and identification of two or more subclones of the tumor; Treatment Database (274); A processor (220) configured to: (i) obtain, from the treatment database, treatment effect information for each of the two or more subclones of the tumor based on the identification of the patient's tumor type and the heterogeneity assessment of the patient's tumor; and (ii) generate a visual representation of the heterogeneity assessment of the patient's tumor and the obtained treatment effect information for each of the two or more subclones of the tumor; A user interface (240) configured to provide the generated visual representation, wherein the generated visual representation includes:

1. A tumor heterogeneity graph comprising: (i) an identification of each of the two or more subclones of the patient's tumor, wherein the identification comprises an indication of the relative amount of each subclone in the patient's tumor; and (ii) a heterogeneity score indicating the overall complexity of the patient's tumor composition, wherein the heterogeneity score is visualized in an ordinal data plot of heterogeneity scores of the same tumor type from a cohort of patients, thereby indicating the degree of heterogeneity of the patient's tumor as a percentile rank within the cohort of patients; and 2. A treatment effect graph comprising an indication of the effect of each of a plurality of different treatments on each of the two or more subclones of the patient's tumor.

12. The system according to claim 11, wherein: The tumor heterogeneity profile also includes an indication of the metastatic potential of each subclone in the patient's tumor.

13. The system according to claim 11, wherein: The two or more subclones are distinguished by one or more molecular and / or histological variations.

14. The system according to claim 11, wherein: Each of the two or more subclones of the patient's tumor on the tumor heterogeneity graph is selectable, and wherein selecting a subclone results in a visual display of some or all of one or more molecular and / or histological variations unique to the selected subclone.

15. The system according to claim 11, wherein: The treatment effect graph also includes an indication of the effect of a combination of two or more treatments on the two or more subclones of the patient's tumor.

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