Systems and methods for delivery of digital biomarkers and genomic panels

Abstract

To provide systems and methods suitable for delivery of digital biomarkers and genomic panels.SOLUTION: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and / or a plurality of pieces of clinical information, determining one or more of a prediction, a recommendation, and / or a plurality of pieces of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and / or the plurality of pieces of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.SELECTED DRAWING: None

Description

[Technical Field]

[0001] (Related Applications) This application claims priority to U.S. Provisional Application No. 62 / 966,659, filed January 28, 2020, the entire disclosure of which is incorporated herein by reference in its entirety.

[0002] Various embodiments of the present disclosure generally relate to deploying artificial intelligence (AI) techniques to detect biomarkers, genomic features, treatment resistance, and other relevant features necessary for additional testing of pathology samples. More specifically, certain embodiments of the present disclosure relate to systems and methods for predicting, identifying, or detecting biomarkers and genomic features in prepared tissue samples. The present disclosure further provides systems and methods for creating predictive models that predict labeling from first-look slides. [Background technology]

[0003] For a pathologist to receive results for a biomarker or genomic panel, there are multiple steps that can be costly and time-consuming. For biomarker results, (a) the pathologist may focus on the patient's appropriate or suspicious body part, (b) the laboratory may receive a request for slide staining, (c) the laboratory may section the block or find an appropriate unstained slide, (d) the body part is stained, and (e) the test is electronically logged against the case and given to the pathologist for final review. For genomic panels, (a) a request for molecular testing may be given to the pathologist, (b) the pathologist may select the slide to be sequenced, (c) prompt for tissue resectioning, (d) prompt for tumor scraping based on the pathologist's outline from the previous biopsy section, (e) the genome in the scraped tumor tissue may be sequenced, and (f) a genetic report may be generated. These processes can be expensive and time-intensive.

[0004] The foregoing general description and the following detailed description are exemplary and explanatory only and are not limitations of the present disclosure. The background provided herein is generally for the purpose of providing a context for the present disclosure. Unless otherwise indicated herein, the material described in this section is not prior art to the claims in this application, and is not admitted to be prior art or an indication of prior art by inclusion in this section. Summary of the Invention [Means for solving the problem]

[0005] According to an aspect of the present disclosure, a system and method for predicting biomarkers and / or at least one genomic feature in a digital image associated with a tissue sample is disclosed.

[0006] A computer-implemented method for processing an electronic image corresponding to a sample includes receiving one or more digital images associated with the tissue sample, an associated case, a patient, and / or a plurality of clinical information; determining one or more predictions, recommendations, and / or a plurality of data regarding the one or more digital images using a machine learning system, wherein the machine learning system has been trained using a plurality of training images to predict biomarkers and a plurality of genomic panel elements; and determining whether an output and at least one visualization region should be logged as part of a case history in a clinical reporting system based on the predictions, recommendations, and / or a plurality of data.

[0007] A system for processing electronic images corresponding to a sample includes a memory that stores instructions and at least one processor that executes the instructions to perform a process including receiving one or more digital images associated with a tissue sample, an associated case, a patient, and / or multiple clinical pieces of information; using a machine learning system to determine one or more predictions, recommendations, and / or multiple pieces of data regarding the one or more digital images, wherein the machine learning system is trained using multiple training images to predict biomarkers and multiple genomic panel elements; and determining whether to log an output and at least one visualization region as part of a case history in a clinical reporting system based on the predictions, recommendations, and / or multiple pieces of data.

[0008] A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for processing an electronic image corresponding to a sample, includes receiving one or more digital images associated with a tissue sample, an associated case, a patient, and / or multiple clinical information; using a machine learning system to determine one or more predictions, recommendations, and / or multiple data regarding the one or more digital images, wherein the machine learning system has been trained using multiple training images to predict biomarkers and multiple genomic panel elements; and determining whether to log an output and at least one visualization region as part of a case history in a clinical reporting system based on the predictions, recommendations, and / or multiple data.

[0009] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not limitations of the disclosed embodiments as claimed. The present invention provides, for example, the following. (Item 1) 1. A computer-implemented method for processing an electronic image corresponding to a specimen, the method comprising: receiving one or more digital images associated with the tissue sample, an associated case, a patient, and / or a plurality of clinical information; determining one or more of a prediction, a recommendation, and / or a plurality of data regarding the one or more digital images using a machine learning system, wherein the machine learning system has been trained using a plurality of training images to predict biomarkers and a plurality of genomic panel elements; determining whether the output and at least one visualization region should be logged as part of a case history in a clinical reporting system based on the prediction, the recommendation, and / or the plurality of data; 11. A computer-implemented method comprising: (Item 2) Item 10. The computer-implemented method of item 1, further comprising generating a notification indicating that a prediction or visualization is available for the one or more digital images. (Item 3) Item 10. The computer-implemented method of item 1, further comprising generating an option for a user to review the prediction or the visualization. (Item 4) Item 10. The computer-implemented method of item 1, further comprising generating one or more indications of at least one recommended treatment based on the prediction. (Item 5) Item 5. The computer-implemented method of item 4, wherein the one or more displays may be generated through one of the following methods: overlay of at least one region of interest layered on top of the original image; juxtaposition visualization; reporting using at least one quantification method; and summary of at least one digital test process with multiple results. (Item 6) 2. The computer-implemented method of claim 1, wherein visualization of digital immunohistochemistry or genomic panel results comprises one of a number of methods: overlay of at least one region of interest layered on top of the original image; juxtaposition visualization; reporting with at least one quantified measurement; and digital test process summary with at least one result. (Item 7) 2. The computer-implemented method of claim 1, wherein the recommendation of at least one treatment pathway is based on a plurality of clinical practice guidelines. (Item 8) Generating at least one prediction is receiving one or more digitized images of the pathology sample, related information, clinical information, and patient information; Deploying a system for storing and archiving a plurality of images and a plurality of corresponding patient data; determining at least one predicted biomarker and at least one predicted genomic panel element based on said plurality of images and said plurality of corresponding patient data; generating a list of recommended treatment pathways based on the plurality of predicted biomarker and genomic panel elements; converting the one or more predictive values ​​and at least one treatment pathway recommendation into a user-readable form; Item 1. The computer-implemented method of item 1, comprising: (Item 9) 9. The computer-implemented method of claim 8, further comprising outputting the one or more predictive values ​​and the at least one treatment pathway recommendation to a user interface. (Item 10) 1. A system for processing an electronic image corresponding to a specimen, the system comprising: at least one memory for storing instructions; at least one processor, wherein the at least one processor executes the instructions to: receiving one or more digital images associated with the tissue sample, an associated case, a patient, and / or a plurality of clinical information; determining one or more of a prediction, a recommendation, and / or a plurality of data regarding the one or more digital images using a machine learning system, wherein the machine learning system has been trained using a plurality of training images to predict biomarkers and a plurality of genomic panel elements; determining whether the output and at least one visualization region should be logged as part of a case history in a clinical reporting system based on the prediction, the recommendation, and / or the plurality of data; at least one processor configured to perform operations including A system comprising: (Item 11) Item 11. The system of item 10, wherein the method further includes generating a notification indicating that a prediction or visualization is available for the one or more digital images. (Item 12) Item 11. The system of item 10, wherein the method further includes generating an option for a user to review the prediction or the visualization. (Item 13) 11. The system of claim 10, wherein the method further comprises generating one or more indications of at least one recommended treatment based on the prediction. (Item 14) Item 11. The system of item 10, wherein the one or more displays may be generated through one of the following methods: overlay of at least one region of interest layered on top of the original image; juxtaposition visualization; reporting using at least one quantification method; and summary of at least one digital test process with multiple results. (Item 15) Item 11. The system of item 10, wherein visualization of digital immunohistochemistry or genomic panel results comprises one of a plurality of methods: overlay of at least one region of interest layered on top of the original image; juxtaposition visualization; reporting with at least one quantification measurement; and digital test process summary with at least one result. (Item 16) Item 11. The system of item 10, wherein the recommendation of at least one treatment pathway is based on a plurality of clinical practice guidelines. (Item 17) Generating at least one prediction is receiving one or more digitized images of the pathology sample, related information, clinical information, and patient information; Deploying a system for storing and archiving a plurality of images and a plurality of corresponding patient data; determining at least one predicted biomarker and at least one predicted genomic panel element based on said plurality of images and said plurality of corresponding patient data; generating a list of recommended treatment pathways based on the plurality of predicted biomarker and genomic panel elements; converting the one or more predictive values ​​and at least one treatment pathway recommendation into a user-readable form; Item 11. The system according to item 10, comprising: (Item 18) 11. The system of claim 10, further comprising outputting the one or more predictive values ​​and the at least one treatment pathway recommendation to a user interface. (Item 19) A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to perform a method for monitoring population health, the method comprising: receiving one or more digital images associated with the tissue sample, an associated case, a patient, and / or a plurality of clinical information; determining one or more of a prediction, a recommendation, and / or a plurality of data regarding the one or more digital images using a machine learning system, wherein the machine learning system has been trained using a plurality of training images to predict biomarkers and a plurality of genomic panel elements; determining whether the output and at least one visualization region should be logged as part of a case history in a clinical reporting system based on the prediction, the recommendation, and / or the plurality of data; 1. A non-transitory computer-readable medium comprising: (Item 20) 20. The method of claim 19, further comprising generating a notification indicating that a prediction or visualization is available for the one or more digital images. [Brief explanation of the drawings]

[0010] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and, together with the description, serve to explain the principles of the disclosed embodiments.

[0011] [Figure 1A] FIG. 1A illustrates an exemplary block diagram of a system and network for detecting biomarkers and / or at least one genomic feature, according to an exemplary embodiment of the present disclosure.

[0012] [Figure 1B] FIG. 1B illustrates an exemplary block diagram of a biomarker detection platform for predicting biomarkers and genomic panel features using machine learning, according to an embodiment of the present disclosure.

[0013] [Figure 1C] FIG. 1C illustrates an exemplary block diagram of a slide analysis tool according to an exemplary embodiment of the present disclosure.

[0014] [Figure 2A]FIG. 2A is a flowchart illustrating an exemplary method for detecting biomarkers and / or at least one genomic feature using a machine learning system, according to one or more exemplary embodiments of the present disclosure.

[0015] [Figure 2B] FIG. 2B is a flowchart illustrating an exemplary method for training a machine learning system to detect biomarkers and / or at least one genomic feature, according to one or more exemplary embodiments of the present disclosure.

[0016] [Figure 3] FIG. 3 is a flowchart illustrating an exemplary method for visualizing positive biomarker foci, according to one or more exemplary embodiments of the present disclosure.

[0017] [Figure 4] FIG. 4 is a flowchart illustrating an exemplary method for visualizing tumor regions to guide a molecular pathologist, according to one or more exemplary embodiments of the present disclosure.

[0018] [Figure 5] FIG. 5 is a flow chart illustrating an exemplary method for reporting predicted development of anti-neoplastic drug resistance, according to one or more exemplary embodiments of the present disclosure.

[0019] [Figure 6] FIG. 6 depicts example options for a user to review visualizations and / or reports, according to one or more example embodiments of the present disclosure.

[0020] [Figure 7] FIG. 7 depicts an example system that may implement the techniques presented herein. DETAILED DESCRIPTION OF THE INVENTION

[0021] Description of the embodiment Reference will now be made in detail to the exemplary embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

[0022] The systems, devices, and methods disclosed herein are described in detail, by way of example, with reference to the Figures. The examples discussed herein are examples only and are provided to aid in the explanation of the apparatus, devices, systems, and methods described herein. None of the features or components shown in the drawings or discussed below should be construed as essential for any particular implementation of any of these devices, systems, or methods, unless specifically designated as essential.

[0023] Also, with respect to any method described, whether the method is described in conjunction with a flow diagram or not, unless otherwise specified or required by context, it should be understood that any explicit or implicit ordering of steps performed in the execution of the method does not imply that these steps must be performed in the order presented, but may instead be performed in a different order or in parallel.

[0024] As used herein, the term "exemplary" is used in the sense of "an example," as opposed to "ideal." Furthermore, the terms "a" and "an," as used herein, do not denote a limitation of quantity, but rather denote the presence of one or more of the referenced item.

[0025] Pathology refers to the study of disease and its causes and effects. More specifically, pathology refers to the performance of tests and analyses used to diagnose disease. For example, a tissue sample may be placed on a slide to be viewed under a microscope by a pathologist (e.g., a medical doctor, a specialist who analyzes tissue samples and determines whether any abnormalities are present). That is, a pathology specimen may be cut into multiple sections, stained, and prepared as a slide for the pathologist to examine and render a diagnosis. When diagnostic uncertainty is found on a slide, the pathologist may prescribe additional sections, stains, or other tests to gather more information from the tissue. A technician may then create a new slide that may contain additional information for the pathologist to use in making a diagnosis. This process of creating additional slides can be time-consuming, not only because it may involve taking a block of tissue, cutting it, creating a new slide, and then staining the slide, but also because it may be bulky for multiple instructions. This can significantly delay the final diagnosis rendered by the pathologist. Additionally, even after a delay, there may still be no guarantee that the new slides will have enough information to render a diagnosis.

[0026] Pathologists may evaluate cancer and other disease pathology slides in isolation. This disclosure presents methods for using AI to detect and predict biomarkers and genomic panel features. In particular, this disclosure describes various exemplary user interfaces available within the workflow, and AI tools that can be integrated into the workflow to facilitate and improve the pathologist's work.

[0027] For example, a computer can be used to analyze images of tissue samples and quickly identify whether additional information may be needed for a particular tissue sample and / or highlight areas that a pathologist should examine more closely. Thus, the process of acquiring additional stained slides and tests can be done automatically before being reviewed by a pathologist. When paired with automated slide segmentation and staining machinery, this can provide a fully automated slide preparation pipeline. This automation has the following advantages: (1) minimizing the amount of time wasted by a pathologist determining insufficient slides to make a diagnosis; (2) minimizing the (average total) time from sample acquisition to diagnosis by avoiding additional time between when additional tests are ordered and when they are generated; (3) reducing the amount of time and material wasted per resection by allowing resections to be performed while the tissue block (e.g., pathology sample) is on the cutting table; (4) reducing the amount of tissue material wasted / discarded during slide preparation; (5) reducing the cost of slide preparation by partially or fully automating the procedure; (6) enabling automated, customized cutting and staining of slides, which may result in more representative / informative slides from the sample; (7) reducing the overhead of requiring additional tests for the pathologist, thereby allowing a larger number of slides to be generated per tissue block, contributing to a more informative / precise diagnosis; and / or (8) identifying or verifying the correct nature (e.g., with respect to sample type) of digital pathology images.

[0028] The process of using computers to assist pathologists is known as computational pathology. Computing methods used for computational pathology may include, but are not limited to, statistical analysis, autonomous or machine learning, and AI. AI may include, but is not limited to, deep learning, neural networks, classification, clustering, and regression algorithms. The use of computational pathology can save lives by helping pathologists improve their diagnostic accuracy, reliability, efficiency, and accessibility. For example, computational pathology may be used to assist in detecting slides that are suspicious for cancer, thereby allowing pathologists to check and confirm their initial assessment before rendering a final diagnosis.

[0029] As described above, the disclosed computational pathology process and device may also integrate with a laboratory information system (LIS) to provide an integrated platform that enables a fully automated process, including data capture, processing, and viewing of digital pathology images via a web browser or other user interface. Furthermore, clinical information may be aggregated using cloud-based data analysis of patient data. Data may originate from hospitals, clinics, field researchers, etc., and may be analyzed by machine learning, computer vision, natural language processing, and / or statistical algorithms to provide real-time monitoring and prediction of health patterns at multiple levels of geographic specificity.

[0030] The present disclosure is directed to systems and methods for quickly and correctly identifying and / or matching sample types of digital pathology images or any information related to the digital pathology images, without necessarily accessing an LIS or similar information database. One embodiment of the present disclosure may include a system that is trained to identify various properties of digital pathology images based on a dataset of previous digital pathology images. The trained system may provide a classification for the sample shown in the digital pathology image. The classification may be useful in providing a treatment or diagnostic prediction for a patient associated with the sample.

[0031] The disclosed systems and methods can use artificial intelligence to detect scanned slides with any features (e.g., highest tumor volume for molecular or invasiveness for human epidermal growth factor receptor 2 / estrogen receptor / progesterone receptor (HER2 / ER / PR)) that may suggest further testing. This feature detection may be performed at the case, body site, or sample mass level. Results may be available via any user interface (e.g., through a viewer, reporting, through a laboratory information system (LIS), etc.). The disclosed systems and methods can also provide immediate visualization of predicted immunohistochemistry (IHC) results, genomic panels, AI-derived information (e.g., treatment resistance), etc., from one or more digital pathology sample images obtained from a patient. This can provide response time and cost efficiency for both hospitals and patients. In addition to displaying digital IHC or digital genomic panel results, the system can also manage reimbursement factors for their purchase, which can provide additional efficiencies for hospitals and patients.

[0032] The disclosed systems and methods can use artificial intelligence to detect scanned slides with any features (e.g., highest tumor volume for molecular or invasiveness for human epidermal growth factor receptor 2 / estrogen receptor / progesterone receptor (HER2 / ER / PR)) that may suggest further testing. This feature detection may be performed at the case, body site, or sample mass level. Results may be available via any user interface (e.g., through a viewer, reporting, through a laboratory information system (LIS), etc.). The disclosed systems and methods can also provide immediate visualization of predicted immunohistochemistry (IHC) results, genomic panels, AI-derived information (e.g., treatment resistance), etc., from one or more digital pathology sample images obtained from a patient. This can provide response time and cost efficiency for both hospitals and patients. In addition to displaying digital IHC or digital genomic panel results, the system can also manage reimbursement factors for the order. This can provide additional efficiencies for hospitals and patients.

[0033] The present disclosure includes one or more embodiments of a slide analysis tool. Input to the tool may include a digital pathology image and any associated additional inputs. Output of the tool may include global and / or local information about the sample. The sample may include a biopsy or surgical resection sample.

[0034] Exemplary global outputs of the disclosed tools may include information about the entire image, such as the sample type, the overall quality of the sample section, the overall quality of the glass pathology slide itself, and / or tissue morphology characteristics. Exemplary local outputs may indicate information within specific regions of the image, for example, a particular image region may be classified as having blur or cracks in the slide. The present disclosure includes embodiments for both the deployment and use of the disclosed slide analysis tools, as described in further detail below.

[0035] FIG. 1A illustrates a block diagram of a system and network for determining sample property or image property information for digital pathology images using machine learning, according to an exemplary embodiment of the present disclosure.

[0036] 1A illustrates an electronic network 120 that may be connected to servers at a hospital, laboratory, and / or doctor's office, etc. For example, a physician server 121, a hospital server 122, a clinical trial server 123, a research laboratory server 124, and / or a laboratory information system 125, etc., may each be connected to the electronic network 120, such as the Internet, through one or more computers, servers, and / or handheld mobile devices. According to an exemplary embodiment of the present disclosure, the electronic network 120 may also be connected to a server system 110, which may include a processing device configured to implement a biomarker detection platform 100, according to an exemplary embodiment of the present disclosure, including slide analysis tools for using machine learning to determine sample property or image property information for digital pathology images to create genomic panels.

[0037] The physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125 may create or otherwise acquire one or more patient images of cytology samples, histopathology samples, slides of cytology samples, digitized images of histopathology sample slides, or any combination thereof. The physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125 may also acquire any combination of patient-specific information, such as age, medical history, cancer treatment history, family history, previous biopsies, or cytology information. The physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125 may transmit the digitized slide images and / or patient-specific information to the server system 110 via the electronic network 120. Server system 110 may include one or more storage devices 109 for storing images and data received from at least one of physician server 121, hospital server 122, clinical trial server 123, research laboratory server 124, and / or laboratory information system 125. Server system 110 may also include a processing device for processing images and data stored in one or more storage devices 109. Server system 110 may further include one or more machine learning tools or capabilities. For example, the processing device may include machine learning tools for biomarker detection platform 100, according to one embodiment. Alternatively, or in addition, the present disclosure (or portions of the systems and methods of the present disclosure) may be implemented on a local processing device (e.g., a laptop).

[0038] Physician server 121, hospital server 122, clinical trial server 123, research laboratory server 124, and / or laboratory information system 125 refer to systems used by pathologists to review slide images. In a hospital setting, tissue type information may be stored within the laboratory information system 125. However, the correct tissue classification information is not always paired with the image content. Additionally, even if an LIS is used to access the sample type for digital pathology images, this label may be incorrect due to the fact that many components of the LIS are manually entered and may leave a large margin of error. According to exemplary embodiments of the present disclosure, the sample type may be identified without the need to access the laboratory information system 125, or possibly to correct the laboratory information system 125. For example, a third party may be given anonymized access to image content without the corresponding sample type label stored within the LIS. Additionally, access to LIS content may be limited due to its sensitive content.

[0039] FIG. 1B illustrates an exemplary block diagram of a biomarker detection platform for predicting biomarkers and genomic panel features using machine learning, according to an embodiment of the present disclosure.

[0040] 1B depicts components of biomarker detection platform 100, according to one embodiment. For example, biomarker detection platform 100 may include slide analysis tool 101, data capture tool 102, slide capture tool 103, slide scanner 104, slide manager 105, storage device 106, and viewing application tool 108.

[0041] Slide analysis tool 101, as described below, refers to a process and system for processing digital images and using machine learning to analyze slides associated with tissue samples, according to an exemplary embodiment.

[0042] Data capture tools 102 refer to processes and systems for facilitating the transfer of digital pathology images to various tools, modules, components, and devices used to classify and process the digital pathology images, according to an exemplary embodiment.

[0043] Slide capture tool 103 refers to a process and system for scanning pathology images and converting them into digital form, according to an exemplary embodiment. Slides may be scanned using slide scanner 104, and slide manager 105 may process the images on the slides into digitized pathology images and store the digitized images in storage device 106.

[0044] The viewing application tool 108 refers to processes and systems for providing a user (e.g., a pathologist) with sample property or image property information related to a digital pathology image, according to an exemplary embodiment. The information may be provided through various output interfaces (e.g., a screen, a monitor, a storage device, and / or a web browser, etc.).

[0045] The slide analysis tool 101 and its components may each transmit and / or receive digitized slide images and / or patient information to and / or from the server system 110, the physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125 via the electronic network 120. Additionally, the server system 110 may include one or more storage devices 109 for storing images and data received from at least one of the slide analysis tool 101, the data capture tool 102, the slide capture tool 103, the slide scanner 104, the slide manager 105, and the viewing application tool 108. The server system 110 may also include a processing device for processing the images and data stored in the one or more storage devices 109. The server system 110 may further include one or more machine learning tools or capabilities, for example, due to the processing device. Alternatively, or in addition, the present disclosure (or portions of the systems and methods of the present disclosure) may be implemented on a local processing device (eg, a laptop).

[0046] Any of the above devices, tools, and modules may be located on devices that may be connected to an electronic network 120, such as the Internet or a cloud service provider, through one or more computers, servers, and / or handheld mobile devices.

[0047] 1C illustrates an exemplary block diagram of a slide analysis tool 101, according to an exemplary embodiment of the present disclosure. The slide analysis tool 101 may include a training image platform 131 and / or a target image platform 135.

[0048] According to one embodiment, the training image platform 131 may create or receive training images that are used to train the machine learning system to effectively analyze and classify digital pathology images. For example, the training images may be received from any one or any combination of the server system 110, the physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125. The images used for training may be derived from real sources (e.g., humans, animals, etc.) or synthetic sources (e.g., graphics rendering engines, 3D models, etc.). Examples of digital pathology images may include (a) digitized slides stained with various stains, such as (but not limited to) H&E, hematoxylin only, IHC, molecular pathology, etc., and / or (b) digitized tissue samples from 3D imaging devices, such as micro-CT.

[0049] The training image capture module 132 may create or receive a dataset comprising one or more training images corresponding to one or both of images of human tissue and graphically rendered images. For example, the training images may be received from any one or combination of the server system 110, the physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125. This dataset may be maintained on a digital storage device. The quality score determiner module 133 may identify quality control (QC) issues (e.g., imperfections) with the training images at a global or local level that may significantly affect the usefulness of the digital pathology image. For example, the quality score determiner module may use information about the image as a whole, such as the sample type, the overall quality of the sample sections, the overall quality of the glass pathology slide itself, or tissue morphological characteristics, to determine an overall quality score for the image. The treatment identification module 134 may analyze images of tissue to determine digital pathology images that have a treatment effect (e.g., post-treatment) and images that do not have a treatment effect (e.g., pre-treatment). Identifying whether a digital pathology image has a treatment effect is useful because previous treatment effects in tissue can affect the morphology of the tissue itself. Most LISs do not explicitly track this characteristic, and therefore, classifying sample types with previous treatment effects may be desirable.

[0050] According to one embodiment, the target image platform 135 may include a target image capture module 136, a sample detection module 137, and an output interface 138. The target image platform 135 may receive a target image and apply a machine learning model to the received target image to determine characteristics of the target sample. For example, the target image may be received from any one or any combination of the server system 110, the physician server 121, the hospital server 122, the clinical trial server 123, the research laboratory server 124, and / or the laboratory information system 125. The target image capture module 136 may receive a target image corresponding to the target sample. The sample detection module 137 may apply a machine learning model to the target image to determine characteristics of the target sample. For example, the sample detection module 137 may detect a sample type of the target sample. The sample detection module 137 may also apply the machine learning model to the target image to determine a quality score for the target image. Furthermore, the sample detection module 137 may apply the machine learning model to the target sample to determine whether the target sample is pre-treatment or post-treatment.

[0051] The output interface 138 may be used to output the target image and information about the target sample (eg, to a screen, monitor, storage device, web browser, etc.).

[0052] 2A is a flowchart illustrating an exemplary method for predicting biomarkers and at least one genomic panel element using a machine learning system, according to one or more exemplary embodiments of the present disclosure. For example, exemplary method 200 (i.e., steps 202-212) may be performed by slide analysis tool 101 automatically or in response to a request from a user.

[0053] According to one embodiment, an exemplary method 200 for predicting biomarkers and at least one genomic panel element may include one or more of the following steps: In step 202, the method may include receiving one or more digital images associated with a tissue sample, an associated case, a patient, and / or clinical information. The tissue sample may comprise a histology sample, while the patient information may include sample type, case and patient ID, body site within the case, gross description, etc. The clinical information may include the attending pathologist, whether the associated sample is available for testing, etc. The digital image may be received into a digital storage device (e.g., a hard drive, a network drive, cloud storage, random access memory (RAM), etc.).

[0054] In step 204, the method may include determining predictions and / or visualizations for one or more digital images using a machine learning system, the machine learning system being trained to predict biomarkers and at least one genomic panel element using a plurality of training images. The machine learning system may additionally output recommendations and / or data to an electronic storage device.

[0055] At step 206, the method may include generating a notification to the user indicating that a prediction and / or visualization is available. The notification may comprise a visual display, a pop-up window, or other suitable alert.

[0056] In step 208, the method may include generating options for the user to review the predictions and / or visualizations. The options may include an exemplary screen display such as that illustrated in FIG. 6, discussed below.

[0057] In step 210, the method may include generating at least one display of at least one recommended treatment pathway based on the prediction and / or visualization. The at least one recommended treatment pathway may include a validated treatment pathway, a new treatment pathway, a clinical treatment pathway, etc., or a next step (e.g., a clinical trial, a specialized physician visit, etc.) based on the generated prediction. Visualization of digital immunohistochemistry or genomic panel results may be accomplished using several methods, including, but not limited to: a. Overlay at least one region of interest on top of the original image b. Juxtaposed visualization c. Reporting with quantified measurements d. Overview of the digital testing process with results

[0058] The visualization of the recommendations may comprise an interactive web interface, allowing the user (e.g., pathologist, oncologist, patient, etc.) to learn more about specific recommendations (e.g., clinical trials recruiting, hospitals / doctors specializing in treatment, etc.) via direct links in the interface and sources (e.g., websites, literature, etc.). Alternatively, the visualization may comprise a report, allowing the user to view a summarized, unmodifiable report that may include, but is not limited to, the following elements: a. Patient history b. Case explanation c. Diagnostic explanation d. Digital and / or "manual" test results e. Suggested next steps for patients based on digital exam results

[0059] The method may group similar patients (e.g., patients with similar morphological patterns, similar biomarker expression, similar genomic profiles, similar treatment pathways, or other similarities) together as a reference to a given case to aid in the decision-making process for a particular case. Visualization of similar patients may or may not be directed to a recommended treatment pathway for the case, depending on the context. Users (e.g., pathologists, oncologists, patients, etc.) may learn more about specific patients and their outcomes (e.g., from clinical trials, drugs, etc.). Results may be visualized by an interactive web interface (e.g., methods for filtering, sharing, saving, etc.) or by reporting, as disclosed above.

[0060] The results may be in the form of a consolidated report (e.g., PDF) that includes report predictions and related information. An exemplary report may contain one or more of the following elements: a. Patient history b.Patient overview c.Case explanation d. Completed digital exams e. Digital test results f. A synthesized description of the results and what they may mean for the patient g. Visualization of outcomes statistics based on similar patients (e.g., infographics, interactive websites, etc.) h. Description of relevant and / or recent literature i. Proposed next steps (e.g., clinical trials, drugs, chemotherapy, etc.)

[0061] In step 212, the method may include determining, based on the prediction and / or visualization, whether to log the output and at least one visualization region as part of a case history in a clinical reporting system. The method may also include integrating the recommendation and visualization into a final diagnostic report for the sample.

[0062] 2B is a flowchart illustrating an exemplary method for training a machine learning system to predict biomarkers and at least one genomic panel element, according to one or more exemplary embodiments of the present disclosure. For example, exemplary method 220 (i.e., steps 221-235) may be performed by slide analysis tool 101 automatically or in response to a request from a user.

[0063] According to one embodiment, an exemplary method 220 is shown for training a machine learning system to predict biomarkers and at least one genomic panel element. In step 221, the method may include receiving one or more digital images associated with a tissue sample, an associated case, a patient, and / or clinical information. The tissue sample may comprise a histology sample, while the patient information may include sample type, case and patient ID, body site within the case, gross description, etc. The clinical information may include the attending pathologist, whether the associated sample is available for testing, etc. The digital image may be received into a digital storage device (e.g., a hard drive, a network drive, cloud storage, random access memory (RAM), etc.).

[0064] In step 223, the method may include deploying a system to store and archive a plurality of processed images associated with a plurality of patient data.

[0065] In step 225, the method may include storing the plurality of processed images in a digital storage device, which may comprise a hard drive, a network drive, cloud storage, RAM, or the like.

[0066] In step 227, the method may include generating at least one recommendation for a treatment pathway based on the plurality of processed images. The treatment pathway may comprise a clinical trial, a treatment, etc. The recommendation may be patient-related and may be based on at least one relevant characteristic of the plurality of stored images and patient data (e.g., patient diagnosis, history, demographics, etc.). The treatment pathway recommendation may comprise or be based on clinical practice guidelines, which may be customized based on patient demographics, pre-approved drugs or therapies, clinical practice, etc.

[0067] In step 229, the method may include generating predictions regarding the biomarkers and the at least one genomic panel element.

[0068] In step 231, the method may include generating a list of at least one recommended treatment pathway based on the prediction. The list of at least one recommended treatment pathway may comprise drug treatments, clinical trials, etc., and associated information (e.g., success rates, locations for treatment, etc.) based on the predicted biomarker and genomic panel elements.

[0069] In step 233, the method may include converting the prediction and at least one recommended treatment pathway into a form that can be visualized and interpreted by a user (e.g., a pathologist, a patient, an oncologist, etc.). The method may additionally include outputting or displaying the at least one result in a variety of effective formats (e.g., interactive, structured, templated, static, etc.) depending on the user and use case.

[0070] In step 235, the method may include outputting one or more predictive values ​​and treatment pathway recommendations to a user interface. Outputting or displaying the results may be in a variety of effective formats (e.g., interactive, structured, templated, static, etc.) depending on the user and use case.

[0071] 3 is a flowchart illustrating an exemplary method for using and training a machine learning system to visualize positive biomarker foci, according to one or more exemplary embodiments of the present disclosure. Visualization of biomarkers (e.g., IHC markers, genomic panels) can assist pathologists in understanding how a computer assay is behaving. Exemplary methods 300 and 320 may be used to visually display detected positive biomarker foci. Exemplary methods 300 and 320 (i.e., steps 301-313 and steps 321-333) may be performed by slide analysis tool 101 automatically or in response to a request from a user.

[0072] According to one embodiment, an exemplary method 300 for training a machine learning system to visualize positive biomarker foci may include one or more of the following steps: In step 301, the method may include receiving one or more digital images and corresponding information associated with a tissue sample. The one or more digital images may comprise a histology slide. The corresponding information may include relevant information (e.g., sample type, available body site, gross description, etc.), clinical information (e.g., diagnosis, biomarker information, etc.), and patient information (e.g., demographics, gender, etc.).

[0073] In step 303, the method may include deploying a system for storing and archiving a plurality of digital images and corresponding patient data. The corresponding patient data may comprise images from screening, follow-up, outcomes, etc.

[0074] In step 305, the method may include storing the plurality of digital images and corresponding patient data in a digital storage device. The digital storage device may comprise a hard drive, a network drive, cloud storage, RAM, etc.

[0075] In step 307, the method may include generating at least one recommendation regarding a treatment pathway based on at least one relevant feature of the plurality of digital images. The treatment pathway may include clinical trials, treatments, etc. for the patient based on at least one relevant factor (e.g., patient diagnosis, medical history, demographics, etc.).

[0076] In step 309, the method may include predicting at least one biomarker and genomic panel element.

[0077] In step 311, the method may include generating a list of at least one recommended treatment pathway based on the predicted biomarkers and genomic panel elements. The recommended treatment pathway (e.g., drugs, clinical trials, etc.) and any associated information (e.g., success rates, locations for treatment, etc.) may be based on the predicted biomarkers and genomic panel elements.

[0078] In step 313, the method may include converting one or more predictive values ​​or recommendations into a form that can be visualized or interpreted by a user (e.g., a pathologist, a patient, an oncologist, etc.).

[0079] In step 321, the method may include receiving one or more digital images associated with the tissue sample, a plurality of associated cases, and patient information from a clinical system. The pathology sample (e.g., histology sample), associated case and patient information (e.g., sample type, case and patient ID, body site within the case, gross description, etc.), and information from the clinical system (e.g., attending pathologist, samples available for testing, etc.) are stored in a digital storage device (e.g., hard drive, network drive, cloud storage, RAM, etc.).

[0080] In step 323, the method may include generating at least one of a prediction, a recommendation, and / or a plurality of data regarding the one or more digital images.

[0081] At step 325, the method may include generating a notification indicating that at least one of a prediction, a recommendation, and / or a plurality of data is available. In addition, visualization for either an immunohistochemistry or a genomic panel may be available.

[0082] In step 327, the method may include providing options for the user to select a visualization and / or report for review. The user may be a pathologist.

[0083] In step 329, the method may include generating a visualization of a recommended treatment pathway based on at least one of the prediction, recommendation, and / or plurality of data. A treatment pathway (e.g., validated, novel, clinical, etc.) or next step (e.g., clinical trial, specialty physician visit, etc.) may be based on the output / generated prediction. Visualization of digital immunohistochemistry or genomic panel results can include one or more of the following: a. Overlay of positive regions of interest on the original image (e.g., contours, gradients with color mapping to algorithm predictions, etc.) b. Side-by-side comparison of images with and without digital IHC or genomic panel predictions c. A prioritized list of all positive foci identified as positive areas for the biomarker or mutation of interest (e.g., a slideshow of image cropping, an interface allowing the user to jump from one foci to another, etc.) d. Reporting, either summarizing all tests in one final output (e.g., scores, results, recommendations, etc.) or listing the final outputs for each digital test.

[0084] In step 331, the method may include logging the visualization as part of the case history in a clinical reporting system.

[0085] In step 333, the method may include integrating the one or more test results into a final diagnostic report associated with the tissue sample.

[0086] 4 is a flowchart illustrating an exemplary method for using and training a machine learning system to visualize tumor regions and guide molecular pathologists, according to one or more exemplary embodiments of the present disclosure. Visualization of regions of malignant tissue on digitized pathology slides can assist molecular pathologists in assessing optimal downstream tests. Exemplary embodiments may be used to select optimal regions for downstream tests. Exemplary methods 400 and 420 may be used to visualize tumor regions and guide molecular pathologists. Exemplary methods 400 and 420 (i.e., steps 401-413 and steps 421-433) may be performed by slide analysis tool 101 automatically or in response to a request from a user.

[0087] According to one embodiment, an exemplary method 400 for training a machine learning system to visualize tumor regions and guide a molecular pathologist may include one or more of the following steps: In step 401, the method may include receiving one or more digital images and corresponding information associated with a tissue sample. The one or more digital images may comprise a histology slide. The corresponding information may include relevant information (e.g., sample type, available body site, gross description, etc.), clinical information (e.g., diagnosis, biomarker information, etc.), and patient information (e.g., demographics, gender, etc.).

[0088] In step 403, the method may include deploying a system for storing and archiving a plurality of digital images and corresponding patient data. The corresponding patient data may comprise images from screening, follow-up, outcomes, etc.

[0089] In step 405, the method may include storing the plurality of digital images and corresponding patient data in a digital storage device. The digital storage device may comprise a hard drive, a network drive, cloud storage, RAM, etc.

[0090] In step 407, the method may include generating at least one recommendation regarding a treatment pathway based on at least one relevant feature of the plurality of digital images. The treatment pathway may include clinical trials, treatments, etc. for the patient based on at least one relevant factor (e.g., patient diagnosis, medical history, demographics, etc.).

[0091] In step 409, the method may include predicting tumor regions on the plurality of digital images.

[0092] In step 411, the method may include generating a list of at least one recommended treatment pathway based on the predicted tumor area. The recommended treatment pathway (e.g., drugs, clinical trials, etc.) and any associated information (e.g., success rate, location for treatment, etc.) may be based on the predicted tumor area.

[0093] In step 413, the method may include converting one or more predictive values ​​or recommendations into a form that can be visualized or interpreted by a user (e.g., a pathologist, a patient, an oncologist, etc.).

[0094] In step 421, the method may include receiving one or more digital images associated with the tissue sample, a plurality of associated cases, and patient information from a clinical system. The pathology sample (e.g., histology sample), associated case and patient information (e.g., sample type, case and patient ID, body site within the case, gross description, etc.), and information from the clinical system (e.g., attending pathologist, samples available for testing, etc.) are stored in a digital storage device (e.g., hard drive, network drive, cloud storage, RAM, etc.).

[0095] In step 423, the method may include generating at least one of a prediction, a recommendation, and / or a plurality of data regarding the one or more digital images.

[0096] At step 425, the method may include generating a notification indicating that at least one of a prediction, a recommendation, and / or a plurality of data is available. In addition, visualization of either the immunohistochemistry or genomic panel may be available.

[0097] In step 427, the method may include providing options for the user to select a visualization and / or report for review. The user may be a pathologist.

[0098] At step 429, the method may include generating a visualization of a recommended treatment pathway based on at least one of the prediction, recommendation, and / or plurality of data. A treatment pathway (e.g., validated, novel, clinical, etc.) or next step (e.g., clinical trial, specialty physician visit, etc.) may be based on the output / generated prediction. Visualization of the Digital Tumor Profiler results can include one or more of the following: Overlay of positive regions of interest onto the original image (e.g., contours, gradients with color mapping to algorithm predictions, etc.). The overlay may be registered onto subsequent images to guide the user in scraping the tumor for sequencing. b. Side-by-side comparison of images with and without predictive display c. A prioritized list of top regions (e.g., tumors with the highest mutant burden). The prioritized list may include a report outlining all body sites analyzed for tumor-specific characteristics (e.g., tumor mutant burden) along with a prediction.

[0099] In step 431, the method may include logging the visualization as part of the case history in a clinical reporting system.

[0100] In step 433, the method may include integrating the one or more test results into a final diagnostic report associated with the tissue sample.

[0101] FIG. 5 is a flowchart illustrating an exemplary method for using and training a machine learning system to report the predicted development of anti-neoplastic drug resistance, according to one or more exemplary embodiments of the present disclosure. Anti-neoplastic drug resistance occurs when cancer cells resist anti-cancer treatment and survive despite this. This ability can develop within a cancer over the course of treatment. Predicting therapies to which a cancer will have the most difficulty developing resistance can improve patient treatment and survival. Some cancers can develop resistance to multiple drugs over the course of treatment. This can be communicated to identify treatments that are likely to be ineffective. Exemplary embodiments can be used to report on the predicted development of anti-neoplastic drug resistance. Exemplary methods 500 and 520 may be used to predict the development of anti-neoplastic drug resistance. Exemplary methods 500 and 520 (i.e., steps 501-511 and steps 521-533) may be performed automatically by slide analysis tool 101 or in response to a request from a user.

[0102] According to one embodiment, an exemplary method 500 for training a machine learning system to visualize tumor regions and guide a molecular pathologist may include one or more of the following steps: In step 501, the method may include receiving one or more digital images and corresponding information associated with a tissue sample. The one or more digital images may comprise a histology slide. The corresponding information may include relevant information (e.g., sample type, available body site, gross description, etc.), clinical information (e.g., diagnosis, biomarker information, etc.), and patient information (e.g., demographics, gender, etc.).

[0103] In step 503, the method may include deploying a system for storing and archiving a plurality of digital images and corresponding patient data. The corresponding patient data may comprise images from screening, follow-up, outcomes, etc.

[0104] In step 505, the method may include storing the plurality of digital images and corresponding patient data in a digital storage device. The digital storage device may comprise a hard drive, a network drive, cloud storage, RAM, etc.

[0105] In step 507, the method may include predicting current or future resistance to at least one treatment pathway or at least one drug. The prediction may be through the use of AI, testing, etc. The AI ​​may infer this information using a variety of inputs, including population statistics, digital images of (stained) tissue containing the tumor, patient history, etc.

[0106] In step 509, the method may include generating a list of at least one treatment that is predicted to be unlikely to be effective.

[0107] In step 511, the method may include generating a list of at least one recommended treatment pathway based on the predicted tumor area. The recommended treatment pathway (e.g., drugs, clinical trials, etc.) and any associated information (e.g., success rate, location for treatment, etc.) may be based on the predicted tumor area.

[0108] In step 511, the method may include converting one or more predictive values ​​or recommendations into a form that can be visualized or interpreted by a user (e.g., a pathologist, a patient, an oncologist, etc.).

[0109] In step 521, the method may include receiving one or more digital images associated with the tissue sample, a plurality of associated cases, and patient information from a clinical system. The pathology sample (e.g., histology sample), associated case and patient information (e.g., sample type, case and patient ID, body site within the case, gross description, etc.), and information from the clinical system (e.g., attending pathologist, samples available for testing, etc.) are stored in a digital storage device (e.g., hard drive, network drive, cloud storage, RAM, etc.).

[0110] In step 523, the method may include generating at least one validity prediction and / or a plurality of data for one or more digital images.

[0111] In step 525, the method may include generating a notification indicating that a prediction and visualization of at least one treatment that is unlikely to be effective is available.

[0112] In step 527, the method may include providing options for the user to select a visualization and / or report for review. The user may be a pathologist.

[0113] In step 529, the method may include generating a visualization of at least one treatment that is unlikely to be effective based on the prediction. The visualization of information may be provided via: An interactive web interface that allows users (e.g., pathologists, oncologists, patients, etc.) to learn more about at least one specific recommendation (e.g., clinical trials recruiting, hospitals / physicians specializing in treatment, etc.) via direct links and sources (e.g., websites, literature, etc.) in the interface. b. Reporting: Users can view a summarized, unchangeable report that may include, but is not limited to, the following elements: i.Patient history ii.Case description iii. Diagnostic explanation iv. Digital and / or "manual" test results v. Suggested next steps for patients based on digital exam results

[0114] In step 531, the method may include logging the visualization as part of the case history in a clinical reporting system.

[0115] In step 533, the method may include integrating the one or more test results into a final diagnostic report associated with the tissue sample.

[0116] 6 depicts exemplary options for a user to review visualizations and / or reports according to one or more exemplary embodiments of the present disclosure. In display 60, an exemplary report is shown along with a display of slide scoring results. Display 65 shows an exemplary window with options for a user to order a digital IHC procedure on a slide.

[0117] As shown in Figure 7, device 700 may include a central processing unit (CPU) 720. CPU 720 may be any type of processor device, including, for example, any type of special-purpose or general-purpose microprocessor device. As will be understood by those skilled in the art, CPU 720 may also be a single processor in a multi-core / multi-processor system, such as a system operating alone or within a cluster of computing devices operating within a cluster or server farm. CPU 720 may be connected to a data communications infrastructure 710, for example, a bus, a message queue, a network, or a multi-core message passing scheme.

[0118] The device 700 may also include a main memory 740, e.g., random access memory (RAM), and may also include a secondary memory 730. The secondary memory 730, e.g., read-only memory (ROM), may be, for example, a hard disk drive or a removable storage drive. Such a removable storage drive may comprise, for example, a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, or the like. The removable storage drive, in this embodiment, reads from and / or writes to a removable storage unit in a well-known manner. The removable storage device may comprise a floppy disk, magnetic tape, optical disk, etc., which is read by and written to the removable storage drive. As will be appreciated by those skilled in the art, such removable storage units generally include computer-usable storage media having computer software and / or data stored therein.

[0119] In alternative implementations thereof, secondary memory 730 may include similar means for allowing computer programs or other instructions to be loaded into device 700. Examples of such means may include program cartridges and cartridge interfaces (such as those found in video game devices), removable memory chips (such as EPROMs or PROMs) and associated sockets, and other removable storage units and interfaces that allow software and data to be transferred from removable storage units to device 700.

[0120] Device 700 may also include a communications interface (“COM”) 760. Communications interface 760 allows software and data to be transferred between device 700 and external devices. Communications interface 760 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, or the like. The software and data transferred via communications interface 760 may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface 760. These signals may be provided to communications interface 760 over a communications path in device 700, which may be implemented using, for example, wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, or other communications channel.

[0121] The hardware elements, operating systems, and programming languages ​​of such devices are conventional in nature and are assumed to be sufficiently familiar to those skilled in the art. Device 700 may also include input and output ports 750 for connecting with input and output devices such as a keyboard, mouse, touch screen, monitor, display, etc. Of course, various server functions may be implemented in a distributed manner on several similar platforms to distribute the processing load. Alternatively, the server may be implemented by appropriate programming of one computer hardware platform.

[0122] Throughout this disclosure, references to components or modules generally refer to items that may be logically grouped together to perform a function or group of related functions. Like reference numbers are generally intended to refer to the same or similar components. Components and modules may be implemented in software, hardware, or a combination of software and hardware.

[0123] The tools, modules, and functions described above may be implemented by one or more processors. A "storage" type medium may include any or all of the tangible memory of a computer, processor, or the like, or its associated modules, such as various semiconductor memories, tape drives, disk drives, and the like, which may provide non-transitory storage for software programming from time to time.

[0124] The software may be communicated over the Internet, a cloud service provider, or other telecommunications network. For example, the communication may allow the software to be loaded from one computer or processor into another. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0125] The foregoing general description is exemplary and explanatory only and is not a limitation of the present disclosure. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only.

Claims

[Claim 1] The invention described in this specification.

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