Networks, decision support systems, and related graphical user interface (GUI) applications for medical image analysis

Abstract

The present application relates to networks, decision support systems and related graphical user interface (GUI) applications for medical image analysis. A platform and supporting graphical user interface (GUI) decision making tools for use by medical practitioners and / or their patients are described herein, for example to aid in the process of making decisions regarding a course of cancer treatment and / or to track the treatment and / or progression of a disease.

Description

[0001] Information about divisional applications

[0002] This application is a divisional application of the Chinese invention patent application with application number 201780064203.9, application date October 26, 2017, and invention name “Network, decision support system and related graphical user interface (GUI) application for medical image analysis”.

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS

[0004] This application claims the benefit of U.S. Provisional Application No. 62 / 413,936, filed October 27, 2016, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0005] The present invention generally relates to systems and methods for the creation, analysis and / or presentation of medical image data. More particularly, in certain embodiments, the present invention relates to a cloud-based platform and supporting GUI decision-making tools for use by medical practitioners and / or their patients, e.g., to facilitate the process of making decisions regarding a course of cancer treatment and / or to track the treatment and / or progression of a disease. Background Art

[0006] Targeted image analysis involves the use of radiolabeled small molecules that bind to specific receptors, enzymes, and proteins in the body that change during the evolution of the disease. After being administered to the patient, these molecules circulate in the blood until they find the intended target. The bound radiopharmaceutical is retained at the site of the disease, while the remaining agent is cleared from the body. The radioactive portion of the molecule is used as a beacon so that images depicting the location and concentration of the disease can be obtained using commonly available nuclear medicine cameras (called single photon emission computed tomography (SPECT) or positron emission tomography (PET) cameras, which are visible in most hospitals in the world). Doctors can then use this information to determine the presence and extent of the disease in the patient. Doctors can use this information to provide patients with a recommended course of treatment and track the progression of the disease.

[0007] There are a variety of software-based analysis techniques available for analysis and enhancement of PET and SPECT images, which can be used by radiologists or physicians. There are also many radiopharmaceuticals available for imaging specific types of cancer. For example, the small molecule diagnostic 1404 targets the extracellular domain of prostate-specific membrane antigen (PSMA), a protein amplified on the surface of >95% of prostate cancer cells and a validated target for detecting primary and metastatic prostate cancer. 1404 is labeled with technetium-99m, a gamma-emitting isotope that is widely available, relatively inexpensive, lends itself to efficient preparation, and has spectral properties that are attractive for nuclear medicine imaging applications.

[0008] Another exemplary radiopharmaceutical is PyL TM (Also known as 18 F]DCFPyL), a clinical-stage fluorinated PSMA-targeted PET imaging agent for prostate cancer. A proof-of-concept study published in the April 2015 issue of the Journal of Molecular Imaging and Biology showed that PyL TM PET imaging performed demonstrated PyL in sites of presumed metastatic disease and primary tumors. TM The uptake level was high, which suggests its potential for high sensitivity and specificity in detecting prostate cancer.

[0009] An oncologist may use images from a targeted PET or SPECT study of a patient as input to their assessment of whether the patient has a particular disease (e.g., prostate cancer), what apparent stage the disease is at, what course of treatment, if any, is recommended, whether surgery is indicated, and the likely prognosis. An oncologist may use a radiologist's report in this assessment. A radiologist's report is a technical evaluation of the PET or SPECT images prepared by a radiologist for the physician requesting the imaging study and contains, for example, the type of study performed, the clinical history, comparisons between the images, the techniques used to perform the study, the radiologist's observations and findings, and the overall impression and recommendations that the radiologist may have based on the results of the imaging study. The signed radiologist's report is sent to the physician who ordered the study for the physician's review, followed by a discussion between the physician and the patient regarding the results and treatment recommendations.

[0010] Thus, the process involves having a radiologist perform an imaging study on the patient, analyzing the images obtained, creating a radiologist report, forwarding the report to the requesting physician, having the physician make evaluation and treatment recommendations, and having the physician communicate the results, recommendations, and risks to the patient. The process may also involve repeating the imaging study due to inconclusive results, or ordering further testing based on the initial results.

[0011] If the imaging studies suggest that the patient has a specific disease or condition (for example, cancer), the doctor will discuss various treatment options (including surgery) and the risks of doing nothing or taking a watchful waiting or active surveillance approach (without surgery).

[0012] There are limitations associated with this process from both the physician's perspective and from the patient's perspective. While the radiologist's report is certainly helpful, the physician ultimately must rely on his or her experience in developing an assessment and recommendation for his or her patient. Furthermore, the patient must have a great deal of trust in his or her physician. The physician can show the patient his or her PET / SPECT images and can inform the patient of the numerical risks associated with various treatment options or the likelihood of a particular prognosis, but the patient may have difficulty understanding this information. Furthermore, the patient's family may have questions, especially if cancer is diagnosed but the patient chooses not to have surgery. The patient and / or their family members may search online for additional information and may be misled about the risks of the diagnosed condition. A difficult ordeal may become even more painful.

[0013] Thus, there remains a need for systems and methods for improving the analysis of medical imaging studies and the communication of these results, diagnoses, prognoses, treatment recommendations, and associated risks to patients. Summary of the invention

[0014] Presented herein is a cloud-based platform and supporting graphical user interface (GUI) decision-making tool for use by healthcare practitioners and / or their patients, for example to aid in the process of making decisions regarding a course of cancer treatment and / or to track treatment and / or progression of a disease.

[0015] For example, a web-based (e.g., cloud-based) support platform is proposed herein that allows multiple users to store, access, analyze, and / or provide feedback on a given image dataset of a patient; the platform supports software tools for automatically analyzing targeted PET / SPECT / or other images, generating radiologist reports, and applying machine learning algorithms to update the process of analyzing images (e.g., updating segmentation and / or classification routines based on a growing image database). In some embodiments, PyL TM and / or 1404 as a radiopharmaceutical to obtain targeted PET / SPECT images. In certain embodiments, multiple (authorized) users may access the information, for example to participate in data interpretation.

[0016] A software tool (e.g., a mobile application) is also presented herein, characterized by a graphical user interface (GUI) element having controls for adjusting the presentation of a 3D risk image corresponding to a patient's organ (and / or other tissue) for comparison with a reference image (e.g., for communicating the results to the patient as a decision-making support). For example, the tool can be supported by the above web-based support platform. The tool can provide an easy-to-understand, user-friendly, interactive, controllable graphical display to convey information about the patient's condition to the patient (and / or to the doctor, or to the patient's family with the patient's permission). For example, a patient who is detected to be at risk for cancer can display a graph indicating the risk area and / or degree, and can compare this risk graph with the risk graphs of other people who are recommended a given course of treatment. For example, the tool can help a patient decide whether to undergo surgery (e.g., for the risk of prostate cancer detected). The patient can visually compare his risk graph with a graph representing the typical risk level of the recommended surgery, and if it is lower than this graph, it may be reasonable to choose not to undergo surgery and to perform observational waiting or active monitoring. Thus, a low-risk patient who is told by a physician that he or she has a non-zero risk of cancer can find comfort in a visually controlled comparison between his or her own situation and that of someone else (e.g., a comparison reference that can adjust the patient's risk situation for the patient's age, weight, and / or other risk factors).

[0017] In one aspect, the present invention relates to a network-based (e.g., cloud-based) decision support system, comprising: a processor (e.g., of a network or Internet host server); and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to perform one or more of the following functions (i) to (v): (i) receiving and storing in a database medical images [e.g., including one or more of the following: targeted PET images, targeted SPECT images, computed tomography (CT) images, magnetic resonance (MR) images, ultrasound (US) images, gamma camera (i.e., scintillation camera) images, and combinations, fusions, or derivatives of any of the above] [e.g., wherein one or more radiopharmaceuticals (e.g., [18F]DCFPyL and / or 1404 and / or a composition comprising technetium 99m {e.g., technetium 99m methylene diphosphonate ( 99m(ii) upon user request (e.g., after automatically verifying that the user is properly authenticated to receive the requested image and / or data), access one or more of the medical images and / or related data associated with the specific patient from a database for transmission to the user for display on the user's computing device; (iii) automatically analyzing one or more of the medical images [e.g., to generate a risk index (e.g., BSI) and / or a risk map, such as a visual representation (e.g., 3D representation) of a tissue (e.g., an organ or other part of the body) with a graphical representation (e.g., texture or color coding) marking risk areas for current disease or risk of recurrence of disease (e.g., cancer), for example, where the risk map is displayed as an overlay of a PET / SPECT / CT / MRI / US / combined / derivative / fused image of the tissue, or in place of an image of the tissue]; (iv) automatically analyzing one or more of the medical images based on the patient's medical history; one or more of the medical images; and (v) applying a machine learning algorithm to update a process for automatically analyzing one or more of the medical images using the accumulated image data in the database [e.g., wherein the automatic analysis of one or more of the medical images in (iii) and / or (v) above includes any one or more of the following (a) to (d): (a) automatic fusion of images of tissue (e.g., PET, SPECT, CT, MRI and / or US); (b) geographical identification of one or more organs, organ structures, sub-organs, organ regions and / or other regions of the imaged tissue of the patient, and generating a 3D image of the geographically identified tissue by superimposing the PET, SPECT, CT, MRI and / or US data; (c) calculating risk information using data from the database, the image of the tissue and / or the 3D image in (b), which includes one or more risk indices, risk regions or risk maps; and (d) using the risk information calculated in (c) (e.g., data from the database) to generate a 3D risk picture of the patient].

[0018] In some embodiments, the medical images in the database include a series of medical images of a first patient taken over time (e.g., over the course of multiple follow-up visits with one or more physicians), and wherein the instructions cause the processor to determine a value of at least a first risk index for each medical image in the series, thereby tracking the determined value of at least the first risk index for the first patient over time.

[0019] In some embodiments, the medical image comprises administering to a first patient comprising 99mThe instructions include a single photon emission computed tomography (SPECT) scan of a first patient obtained after administration of a Tc-labeled 1404 imaging agent (e.g., for identifying one or more hot spots), and a computed tomography (CT) scan of the first patient (e.g., for identifying anatomical features), wherein the instructions cause the processor to overlay the SPECT scan with the CT scan to create a composite image (SPECT-CT) of the first patient.

[0020] In some embodiments, the medical image comprises administering to a first patient a dose of [18F]DCFPyL (using 18 The instructions provide a positron emission tomography (PET) scan of a first patient obtained after administration of an imaging agent (DCFPyL) labeled with F (e.g., for identifying one or more hot spots) and a CT scan of the first patient, wherein the instructions cause the processor to overlay the PET scan with the CT scan to create a composite image (PET-CT) of the first patient.

[0021] In certain embodiments, the medical image comprises administering to a first patient a drug comprising technetium-99m methylene diphosphonate ( 99m A first whole body scan (eg, including anterior and posterior views) of the patient is generated using a gamma camera after administration of an imaging agent (eg, Tc MDP) to the patient.

[0022] In certain embodiments, the medical image comprises a composite image of a first patient, the composite image comprising a CT scan superimposed with a nuclear medicine image (e.g., a SPECT scan; e.g., a PET scan), the nuclear medicine image being obtained substantially simultaneously with the CT scan and after administration of an imaging agent comprising a prostate-specific membrane antigen (PSMA) binding agent (including, e.g., labeled with, a radionuclide) to the first patient, wherein the instructions cause the processor to automatically analyze the composite image by: (a) using the composite image to geographically identify 3D boundaries of each of one or more regions of imaged tissue [e.g., organs (e.g., prostate; e.g., liver; e.g., lungs; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a skeletal region of a patient), e.g., a target region] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image that fall within and / or outside of the 3D boundaries can be distinguished from one another); and (c) calculating (i) a value for each of one or more risk indices and / or (ii) a risk map using the nuclear medicine image having the identified 3D boundaries of the one or more regions.

[0023] In some embodiments, the instructions cause the processor to calculate, for at least one of the one or more risk indices, a value of the risk index by: determining, for each of the one or more regions, a corresponding cancer tissue level within the region based on intensity values ​​of the nuclear medicine image within the 3D boundary of the region (e.g., by identifying multiple hot spots within the 3D boundary of the region in the nuclear medicine image and calculating the total number and / or total volume of the identified hot spots); and calculating the value of the risk index based on the determined cancer tissue level within the one or more regions.

[0024] In certain embodiments, the nuclear medicine image is a SPECT scan.

[0025] In certain embodiments, the imaging agent comprises a metal chelated to a PSMA-binding agent, and wherein the metal is a radionuclide [e.g., wherein the metal is a radioisotope of technetium (Tc) (e.g., wherein the metal is technetium-99m ( 99m Tc)); for example, wherein the metal is a radioisotope of rhenium (Re) (for example, wherein the metal is rhenium-188 ( 188 Re); for example, where the metal is rhenium-186 ( 186 Re)); for example, wherein the metal is a radioactive isotope of yttrium (Y) (for example, wherein the metal is 90 Y); for example, wherein the metal is a radioactive isotope of lutetium (Lu) (for example, wherein the metal is 177 Lu); for example, where the metal is a radioactive isotope of gallium (Ga) (for example, where the metal is 68 Ga; for example, where the metal is 67 Ga); for example, where the metal is a radioactive isotope of indium (e.g., 111 In); for example, where the metal is a radioactive isotope of copper (Cu) (for example, where the metal is 67 Cu)].

[0026] In certain embodiments, the imaging agent comprises 99m Tc-MIP-1404.

[0027] In certain embodiments, the nuclear medicine image is a PET scan.

[0028] In certain embodiments, the radionuclide is a radioisotope of a halogen [e.g., a radioisotope of fluorine (e.g., 18 F); for example, radioactive isotopes of iodine (e.g., 123 I; for example, 124 I; for example, 125 I; for example, 126 I; for example, 131 I); for example, radioactive isotopes of bromine (e.g., 75 Br; for example,76 Br; for example, 77 Br; for example, 80 Br; for example, 80m Br; for example, 82 Br; for example, 83 Br), for example, radioactive isotopes of astatine (e.g. 211 At)].

[0029] In certain embodiments, the imaging agent comprises [18F]DCFPyL (with 18 F-labeled DCFPyL).

[0030] In certain embodiments, the radionuclide is a radioisotope of gallium (Ga) (e.g., 68 Ga).

[0031] In some embodiments, the medical image includes a step of administering to a first patient a radionuclide (e.g., 99m Tc) imaging agent (for example, wherein the imaging agent comprises 99m Tc MDP) of a first patient (e.g., a whole body scan generated with a gamma camera), wherein the instructions cause the processor to automatically analyze the nuclear medicine image by: (a) geographically identifying boundaries (e.g., 2D boundaries; e.g., 3D boundaries) of each of one or more regions of imaged tissue [e.g., organs (e.g., prostate; e.g., liver; e.g., lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a skeletal region of the patient), e.g., a target region] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image that fall within and / or outside the boundaries can be distinguished from each other); and (c) calculating (i) a value for each of one or more risk indices and / or (ii) a risk map using the nuclear medicine image with the identified boundaries of the one or more regions.

[0032] In some embodiments, the instructions cause the processor to calculate, for at least one of the one or more risk indices, a value of the risk index by: determining, for each of the one or more regions, a corresponding cancer tissue level within the region based on an intensity value of the nuclear medicine image within the boundary of the region (e.g., by identifying a plurality of hot spots within the boundary of the region in the nuclear medicine image and calculating the total number and / or total volume of the identified hot spots); and calculating the value of the risk index based on the determined cancer tissue level within the one or more regions.

[0033] In certain embodiments, the system is a cloud-based system.

[0034] In some embodiments, the processor is the processor of one or more network or Internet host servers.

[0035] In another aspect, the present invention relates to a method comprising any one or more of the following (i) to (v): (i) receiving by a processor of a server computing device and storing in a database (e.g., received from a client computing device via a network) medical images [e.g., comprising one or more of the following: targeted PET images, targeted SPECT images, computed tomography (CT) images, magnetic resonance (MR) images, ultrasound (US) images, gamma camera (i.e., scintillation camera) images, and combinations, fusions, or derivatives of any of the above] [e.g., wherein one or more radiopharmaceuticals (e.g., [18F]DCFPyL and / or 1404 and / or a composition comprising technetium 99m (e.g., technetium 99m methylene diphosphonate ( 99m (ii) upon a user's request (e.g., after automatically verifying that the user is properly authenticated to receive the requested images and / or data), accessing by the processor one or more of the medical images and / or related data associated with the specific patient from a database for display on a user computing device; (iii) automatically analyzing by the processor one or more of the medical images [e.g., to generate a risk map, such as a visual representation (e.g., a 3D representation) of a tissue (e.g., an organ or other part of the body) with a graphical representation (e.g., texture or color coding) marking risk areas for current disease or risk of recurrence of disease (e.g., cancer), for example, where the risk map is displayed as an overlay of a PET / SPECT / CT / MRI / US / combined / derivative / fused image of the tissue, or in place of an image of the tissue]; and (iv) automatically analyzing by the processor one or more of the medical images based on the patient's image(s) in the medical images. one or more of generating a radiologist report for the patient; and (v) applying a machine learning algorithm by a processor to update a process for automatically analyzing one or more of the medical images using accumulated image data in a database, [for example, wherein the automatic analysis of one or more of the medical images in (iii) and / or (v) above includes any one or more of the following (a) to (d): (a) automatically fusing (e.g., PET, SPECT, CT, MRI and / or US) images of tissue; (b) geographically identifying one or more organs, organ structures, sub-organs, organ regions and / or other regions of the imaged tissue of the patient, and generating a 3D image of the geographically identified tissue by superimposing the PET, SPECT, CT, MRI and / or US data; (c) calculating risk information using data from the database, the image of the tissue and / or the 3D image in (b), which includes one or more risk indices, risk regions or risk maps; and (d) using the risk information calculated in (c) (e.g., data from the database) to generate a 3D risk picture of the patient].

[0036] In some embodiments, the medical images in the database include a series of medical images of a first patient taken over time (e.g., over the course of multiple follow-up visits with one or more physicians), and wherein the method includes determining a value of at least a first risk index for each medical image in the series, thereby tracking the determined value of at least the first risk index over time.

[0037] In certain embodiments, receiving and storing medical images includes repeatedly receiving and storing multiple medical images of the first patient over time (each medical image is obtained at a different time, such as during different follow-up visits with one or more physicians) to obtain the series of medical images of the first patient.

[0038] In some embodiments, the medical image comprises administering to a first patient comprising 99m A single photon emission computed tomography (SPECT) scan of a first patient obtained after administration of a Tc-labeled 1404 imaging agent (e.g., for identifying one or more hot spots), and a computed tomography (CT) scan of the first patient (e.g., for identifying anatomical features), wherein the method includes superimposing the SPECT scan with the CT scan to create a composite image of the first patient (SPECT-CT).

[0039] In some embodiments, the medical image comprises administering to a first patient a dose of [18F]DCFPyL (using 18 The method comprises a positron emission tomography (PET) scan of a first patient obtained after administration of an imaging agent (DCFPyL) labeled with F, and a CT scan of the first patient, wherein the method comprises superimposing the PET scan with the CT scan to create a composite image of the first patient (PET-CT).

[0040] In certain embodiments, the medical image comprises administering to a first patient a drug comprising technetium-99m methylene diphosphonate ( 99m A first whole body scan (eg, including anterior and posterior views) of the patient is generated using a gamma camera after administration of an imaging agent (eg, Tc MDP) to the patient.

[0041] In certain embodiments, the medical image comprises a composite image of a first patient, the composite image comprising a CT scan superimposed with a nuclear medicine image (e.g., a SPECT scan; e.g., a PET scan), the nuclear medicine image being acquired substantially simultaneously and after administration of an imaging agent comprising a prostate-specific membrane antigen (PSMA) binding agent (comprising a radionuclide) to the first patient, wherein the method comprises automatically analyzing the composite image by: (a) using the composite image to geographically identify 3D boundaries of each of one or more regions of imaged tissue [e.g., organs (e.g., prostate; e.g., liver; e.g., lungs; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a skeletal region of a patient), e.g., a target region] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image that fall within and / or outside of the 3D boundaries can be distinguished from one another); and (c) calculating (i) a value for each of one or more risk indices and / or (ii) a risk map using the nuclear medicine image having the identified 3D boundaries of the one or more regions.

[0042] In some embodiments, step (c) includes, for at least one of the one or more risk indices, calculating a value of the risk index by: determining, for each of the one or more regions, a corresponding cancer tissue level within the region based on an intensity value of the nuclear medicine image within a 3D boundary of the region (e.g., by identifying a plurality of hot spots within the 3D boundary of the region in the nuclear medicine image and calculating the total number and / or total volume of the identified hot spots); and calculating a value of the risk index based on the determined cancer tissue level within the one or more regions.

[0043] In certain embodiments, the nuclear medicine image is a SPECT scan.

[0044] In certain embodiments, the imaging agent comprises a metal chelated to a PSMA-binding agent, and wherein the metal is a radionuclide [e.g., wherein the metal is a radioisotope of technetium (Tc) (e.g., wherein the metal is technetium-99m ( 99m Tc)); for example, wherein the metal is a radioisotope of rhenium (Re) (for example, wherein the metal is rhenium-188 ( 188 Re); for example, where the metal is rhenium-186 ( 186 Re)); for example, wherein the metal is a radioactive isotope of yttrium (Y) (for example, wherein the metal is 90 Y); for example, wherein the metal is a radioactive isotope of lutetium (Lu) (for example, wherein the metal is 177 Lu); for example, where the metal is a radioactive isotope of gallium (Ga) (for example, where the metal is 68 Ga; for example, where the metal is 67Ga); for example, where the metal is a radioactive isotope of indium (e.g., 111 In); for example, where the metal is a radioactive isotope of copper (Cu) (for example, where the metal is 67 Cu)].

[0045] In certain embodiments, the imaging agent comprises 99m Tc-MIP-1404.

[0046] In certain embodiments, the nuclear medicine image is a PET scan.

[0047] In certain embodiments, the radionuclide is a radioisotope of a halogen [e.g., a radioisotope of fluorine (e.g., 18 F); for example, radioactive isotopes of iodine (e.g., 123 I; for example, 124 I; for example, 125 I; for example, 126 I; for example, 131 I); for example, radioactive isotopes of bromine (e.g., 75 Br; for example, 76 Br; for example, 77 Br; for example, 80 Br; for example, 80m Br; for example, 82 Br; for example, 83 Br), for example, radioactive isotopes of astatine (e.g. 211 At)].

[0048] In certain embodiments, the imaging agent comprises [18F]DCFPyL (with 18 F-labeled DCFPyL).

[0049] In certain embodiments, the radionuclide is a radioisotope of gallium (Ga) (e.g., 68 Ga)

[0050] In some embodiments, the medical image includes a step of administering to a first patient a radionuclide (e.g., 99m Tc) imaging agent (for example, wherein the imaging agent comprises 99mTc MDP) of a first patient (e.g., a whole body scan generated with a gamma camera), wherein the method includes automatically analyzing the nuclear medicine image by: (a) geographically identifying boundaries (e.g., 2D boundaries; e.g., 3D boundaries) of each of one or more regions of imaged tissue [e.g., organs (e.g., prostate; e.g., liver; e.g., lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a skeletal region of the patient), e.g., a target region] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image that fall within and / or outside the 3D boundaries can be distinguished from each other); and (c) calculating (i) a value for each of the one or more risk indices and / or (ii) a risk map using the nuclear medicine image with the identified boundaries of the one or more regions.

[0051] In some embodiments, step (c) includes, for at least one of the one or more risk indices, calculating a value of the risk index by: determining, for each of the one or more regions, a corresponding cancer tissue level within the region based on an intensity value of the nuclear medicine image within the boundary of the region (for example, by identifying a plurality of hot spots within the 3D boundary of the region in the nuclear medicine image and calculating the total number and / or total volume of the identified hot spots); and calculating a value of the risk index based on the determined cancer tissue level within the one or more regions.

[0052] In certain embodiments, the processor is a processor of a cloud-based system.

[0053] In some embodiments, the processor is the processor of one or more network or Internet host servers.

[0054] In another aspect, the present invention relates to a system comprising: a processor (e.g., of a network or Internet host server or a portable computing device); and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to generate and enable display of an interactive graphical user interface (GUI) element (e.g., enabling display of the GUI element on a laptop computer or on a remote computing device, such as via a mobile application), the GUI element having a user-selectable and / or user-adjustable graphical control (e.g., a slider bar, option button, text bar, drop-down box, window, animation, and / or any other GUI widget) for selecting and / or adjusting a digital representation of a patient's 3D risk picture for comparison with a reference image enabled for display by the processor (e.g., for communicating the results to the patient as decision-making support) (e.g., wherein the reference image displayed to the user or presented for selection by the user is adjustable based on one or more predetermined variables associated with the patient (e.g., patient age, time since diagnosis, previous treatments, and / or future treatments)).

[0055] In some embodiments, interactive GUI elements are generated from medical images of the patient [e.g., including one or more of the following: targeted PET images, targeted SPECT images, magnetic resonance (MR) images, ultrasound (US) images, gamma camera (i.e., scintillation camera) images, and any combination, fusion, or derivative of the foregoing] and / or other images or information (e.g., other images received and stored in a database of a network-based decision support system of any aspect and / or embodiment described herein).

[0056] In another aspect, the present invention relates to a method for tracking the progression and efficacy of prostate cancer treatment in one or more patients over time, the method comprising: (a) repeatedly receiving and storing in a database a plurality of medical images of each of the one or more patients by a processor of a computing device (e.g., a server computing device) over time to obtain a series of medical images taken over time (e.g., during multiple follow-up visits with one or more physicians) for each of the one or more patients; and (b) automatically analyzing, by the processor, for each of the one or more patients, the series of medical images of the patient to determine a value of one or more risk indexes for each medical image of the series. For example, corresponding to values ​​of one or more risk indices indicating the status and / or progression of prostate cancer in a patient (e.g., a value identifying a particular cancer stage; for example, corresponding to a value corresponding to a determined overall survival rate for the patient)], thereby tracking the determined values ​​of the one or more risk indices during the progression and treatment of the patient's prostate cancer; and (c) for each of the one or more patients, storing by the processor the determined values ​​of the patient's one or more risk indices so that the processor can further process a graphical representation of the determined values ​​of the patient's one or more risk indices and / or enable their display (e.g., enabling the display of a graph showing changes in the determined values ​​of the patient's one or more risk indices over time).

[0057] In some embodiments, the series of medical images of a particular patient among the one or more patients includes: (i) a first subseries of images including images of the particular patient prior to administration of a first radiopharmaceutical (e.g., 99m Tc-MIP-1404) using a first nuclear imaging modality (e.g., a SPECT scan; e.g., a synthetic SPECT-CT image) (e.g., where the first radiopharmaceutical facilitates imaging of localized disease, such as localized prostate cancer); and (ii) a second subset of images, which include images acquired after administration of a second radiopharmaceutical (e.g., [18F]DCFPyL; e.g., 99mTcMDP) using a second nuclear imaging modality (e.g., wherein the second radiopharmaceutical facilitates imaging of metastatic disease, such as metastatic prostate cancer), such that the values ​​of the one or more risk indices determined for the particular patient in step (b) include a first subseries of values ​​for the first risk index determined by automated analysis of the first subseries of images and a second subseries of values ​​for the second risk index determined by automated analysis of the second subseries of images.

[0058] In some embodiments, the medical images of the first image sub-series are obtained during a first time period when the prostate cancer of the particular patient is localized (e.g., substantially confined to the prostate of the particular patient), and the medical images of the second image sub-series are obtained during a second time period when the prostate cancer of the particular patient is metastatic (e.g., has spread to areas outside of the patient's prostate).

[0059] In some embodiments, the first image subseries includes one or more synthetic SPECT-CT images, each synthetic SPECT-CT image including a CT scan superimposed with a SPECT scan acquired substantially simultaneously; the second image subseries includes one or more synthetic PET-CT images, each synthetic PET-CT image including a CT scan superimposed with a PET scan acquired substantially simultaneously; and step (b) includes: automatically analyzing each of the one or more synthetic SPECT-CT images by: using the synthetic SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to the patient's prostate) within the SPECT scan of the synthetic SPECT-CT image (e.g., so that portions of the nuclear medicine image that fall within and / or outside the 3D boundary of the prostate region can be distinguished from each other); and calculating a value of the first risk index using the SPECT scan with the identified 3D boundary of the prostate region. (e.g., calculated based on an area of ​​a SPECT scan corresponding to an identified 3D boundary of a prostate region); and automatically analyzing each of the one or more synthetic PET-CT images by: using the synthetic PET-CT image to geographically identify the 3D boundaries of one or more metastatic regions within the PET scan of the synthetic PET-CT image, the one or more metastatic regions comprising regions corresponding to patient tissue locations outside the prostate [e.g., organs (e.g., prostate; e.g., liver; e.g., lungs; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a bone region corresponding to a patient's skeleton), e.g., a target region] (e.g., so that portions of the PET imaging scan that fall within and / or outside the 3D boundaries of the one or more metastatic regions can be distinguished from each other); and calculating a value of a second risk index using the PET scan having the identified 3D boundaries of the one or more metastatic regions.

[0060] In certain embodiments, the first image subseries includes one or more synthetic SPECT-CT images, each synthetic SPECT-CT image including a CT scan superimposed with a SPECT scan acquired substantially simultaneously; the second image subseries includes one or more whole body scans; and step (b) includes: automatically analyzing each of the one or more synthetic SPECT-CT images by: using the synthetic SPECT-CT images to geographically identify a 3D boundary of a prostate region (e.g., corresponding to the patient's prostate) within the SPECT scan of the synthetic SPECT-CT image (e.g., such that portions of the nuclear medicine image that fall within and / or outside of the 3D boundary of the prostate region can be distinguished from one another); and using the identification of the prostate region to geographically identify the 3D boundary of the prostate region. The method further comprises the step of calculating a value of a first risk index based on a SPECT scan with an identified 3D boundary corresponding to a prostate region; and automatically analyzing each of the one or more whole body scans by: geographically identifying boundaries of one or more metastatic regions within the whole body scan, the one or more metastatic regions including regions corresponding to patient tissue locations outside the prostate [(e.g., one or more specific bones; e.g., a bone region corresponding to a patient's skeleton), e.g., a target region] (e.g., such that portions of the whole body scan that fall within and / or outside the boundaries of the one or more metastatic regions can be distinguished from each other); and calculating a value of a second risk index using a PET scan with an identified 3D boundary corresponding to the one or more metastatic regions.

[0061] In another aspect, the present invention relates to a system for tracking the progression of prostate cancer and the efficacy of treatment of one or more patients over time, the system comprising: a processor (e.g., of a network or Internet host server); and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) repeatedly receive and store in a database a plurality of medical images of each of one or more patients over time to obtain a series of medical images taken over time (e.g., during multiple follow-up visits with one or more physicians) for each of the one or more patients; and (b) automatically analyze, for each of the one or more patients, the series of medical images of the patient to determine a medical history of each of the series. (c) storing, for each of the one or more patients, the determined values ​​of the one or more risk indices of the patient so that a processor can further process the determined values ​​of the one or more risk indices of the patient for graphical representation and / or display (e.g., display of a graph showing changes in the determined values ​​of the one or more risk indices of the patient over time).

[0062] In some embodiments, the series of medical images of a particular patient among the one or more patients includes: (i) a first subseries of images including images of the particular patient prior to administration of a first radiopharmaceutical (e.g., 99m Tc-MIP-1404) using a first nuclear imaging modality (e.g., a SPECT scan; e.g., a synthetic SPECT-CT image) (e.g., where the first radiopharmaceutical facilitates imaging of localized disease, such as localized prostate cancer); and (ii) a second subset of images, which include images acquired after administration of a second radiopharmaceutical (e.g., [18F]DCFPyL; e.g., 99m TcMDP) using a second nuclear imaging modality (e.g., wherein the second radiopharmaceutical facilitates imaging of metastatic disease, such as metastatic prostate cancer), such that the values ​​of the one or more risk indices determined for the particular patient in step (b) include a first subseries of values ​​for the first risk index determined by automated analysis of the first subseries of images and a second subseries of values ​​for the second risk index determined by automated analysis of the second subseries of images.

[0063] In some embodiments, the medical images of the first image sub-series are obtained during a first time period when the prostate cancer of the particular patient is localized (e.g., substantially confined to the prostate of the particular patient), and the medical images of the second image sub-series are obtained during a second time period when the prostate cancer of the particular patient is metastatic (e.g., has spread to areas outside of the patient's prostate).

[0064] In some embodiments, the first image subseries includes one or more synthetic SPECT-CT images, each synthetic SPECT-CT image including a CT scan superimposed with a SPECT scan acquired substantially simultaneously; the second image subseries includes one or more synthetic PET-CT images, each synthetic PET-CT image including a CT scan superimposed with a PET scan acquired substantially simultaneously; and in step (b), the instructions cause the processor to: automatically analyze each of the one or more synthetic SPECT-CT images by: using the synthetic SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to the patient's prostate) within the SPECT scan of the synthetic SPECT-CT image (e.g., so that portions of the nuclear medicine image that fall within and / or outside the 3D boundary of the prostate region can be distinguished from each other); and calculate a first risk factor using the SPECT scan with the identified 3D boundary of the prostate region. A value of a second risk index (e.g., calculated based on an area of ​​a SPECT scan corresponding to an identified 3D boundary of a prostate region); and automatically analyzing each of the one or more synthetic PET-CT images by: using the synthetic PET-CT image to geographically identify 3D boundaries of one or more metastatic regions within a PET scan of the synthetic PET-CT image, the one or more metastatic regions comprising regions corresponding to patient tissue locations outside the prostate [e.g., organs (e.g., prostate; e.g., liver; e.g., lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and / or other regions (e.g., one or more specific bones; e.g., a bone region corresponding to a patient's skeleton), e.g., a target region] (e.g., so that portions of the PET imaging scan that fall within and / or outside the 3D boundaries of the one or more metastatic regions can be distinguished from each other); and calculating a value of a second risk index using the PET scan having the identified 3D boundaries of the one or more metastatic regions.

[0065] In certain embodiments, the first image subset includes one or more synthetic SPECT-CT images, each synthetic SPECT-CT image including a CT scan superimposed with a SPECT scan acquired substantially simultaneously; the second image subset includes one or more whole body scans; and in step (b), the instructions cause the processor to: automatically analyze each of the one or more synthetic SPECT-CT images by: using the synthetic SPECT-CT images to geographically identify a 3D boundary of a prostate region (e.g., corresponding to the patient's prostate) within the SPECT scan of the synthetic SPECT-CT image (e.g., such that portions of the nuclear medicine image that fall within and / or outside of the 3D boundary of the prostate region can be distinguished from each other); and using the 3D boundary of the prostate region to geographically identify a 3D boundary of the prostate region (e.g., corresponding to the patient's prostate) within the SPECT scan of the synthetic SPECT-CT image. The method further comprises calculating a value of a first risk index using a SPECT scan having an identified 3D boundary of a domain (e.g., calculated based on a region of the SPECT scan corresponding to the identified 3D boundary of a prostate region); and automatically analyzing each of the one or more whole body scans by: geographically identifying boundaries of one or more metastatic regions within the whole body scan, the one or more metastatic regions including regions corresponding to patient tissue locations outside the prostate [(e.g., one or more specific bones; e.g., a bone region corresponding to the patient's skeleton), e.g., a target region] (e.g., so that portions of the whole body scan that fall within and / or outside the boundaries of the one or more metastatic regions can be distinguished from each other); and calculating a value of a second risk index using a PET scan having an identified 3D boundary of the one or more metastatic regions.

[0066] Embodiments described with respect to one aspect of the invention may be applicable to another aspect of the invention (e.g., features of embodiments described with respect to one independent claim (e.g., a method claim) are intended to be applicable to other embodiments of other independent claims (e.g., a system claim), and vice versa). BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The foregoing and other objects, aspects, features and advantages of the present disclosure will become more apparent and better understood by referring to the following description in conjunction with the accompanying drawings, in which:

[0068] Figure 1 is a screenshot of a graphical user interface (GUI) showing three mobile application icons for cloud-based services in accordance with an illustrative embodiment.

[0069] Figure 2 is a schematic diagram showing the relationship between a platform and a computing device (eg, a personal computer or a mobile computing device such as a smart phone) that runs an application, according to an illustrative embodiment of the invention.

[0070] Figure 3is a screenshot of a GUI window (displayed to a user) in a BSI cloud application that allows the user to enter information about a patient and upload and / or access medical images of the patient, such as a series of images acquired over a period of time, according to an illustrative embodiment.

[0071] Figure 4 is a screenshot of a GUI window in a BSI cloud application showing a representative whole-body gamma camera image showing hot spots automatically identified by the system and the corresponding total calculated BSI value for a particular set of images acquired at a given time, according to an illustrative embodiment.

[0072] Figure 5 is a screenshot of a GUI window in a BSI cloud application showing an automatically or semi-automatically generated radiologist report that may be signed and dated by the radiologist, according to an illustrative embodiment.

[0073] Figure 6 is a block diagram illustrating a set of functionality provided by the cloud-based platform and supporting graphical user interface (GUI) decision-making tools described herein, according to one illustrative embodiment.

[0074] Figure 7 is a block diagram of an exemplary cloud computing environment used in certain embodiments.

[0075] Figure 8 is a block diagram of an exemplary computing device and an exemplary mobile computing device used in certain embodiments.

[0076] Fig. 9 is a block diagram of an exemplary architecture for implementing the cloud-based platform described herein, according to one illustrative embodiment.

[0077] Fig.10 is a schematic diagram showing the stages of prostate cancer progression and various treatments and diagnostic imaging modalities appropriate for various disease states, according to an illustrative embodiment.

[0078] Fig.11 is a block flow diagram of a process for tracking prostate cancer progression and treatment efficacy over time according to one illustrative embodiment.

[0079] The features and advantages of the present disclosure will become more apparent from the detailed description given below in conjunction with the accompanying drawings, in which like reference numerals identify corresponding elements throughout. In the accompanying drawings, like reference numerals generally indicate identical, functionally similar, and / or structurally similar elements. DETAILED DESCRIPTION

[0080] It is contemplated that the systems, devices, methods, and processes of the claimed invention encompass variations and adaptations developed using information from the embodiments described herein. Adaptations and / or modifications of the systems, devices, methods, and processes described herein may be made by a person of ordinary skill in the relevant art.

[0081] Throughout the specification, when articles, devices, and systems are described as having, containing, or including particular components, or when processes and methods are described as having, containing, or including particular steps, it is contemplated that there are additionally articles, devices, and systems of the invention consisting of or consisting essentially of the components, and that there are processes and methods of the invention consisting of or consisting essentially of the processing steps.

[0082] Should be understood that, as long as the present invention is still operable, the order of steps or the order in which certain actions are performed is not important. In addition, two or more steps or actions may be performed simultaneously.

[0083] Reference to any publication herein (e.g., in the Background section) is not an admission that the publication is prior art with respect to any claim presented herein. The Background section is presented for clarity purposes and is not intended as a description of prior art with respect to any claim.

[0084] Headers are provided for the convenience of the reader - the presence and / or placement of headers is not intended to limit the scope of the subject matter described herein.

[0085] A. Medical imaging modalities, associated radiopharmaceuticals, and calculated risk indices

[0086] Figure 1 Mobile application icons 100 for three cloud-based services according to an illustrative embodiment are shown. As described below, the cloud-based services of the platform described herein provide processing and analysis of medical images in a fully automated manner and / or in combination with user interaction (e.g., in a semi-automated manner). The medical images include nuclear medicine images obtained using nuclear imaging modalities, such as whole body scans using a gamma camera, positron emission tomography (PET) scans, and single photon emission tomography (SPECT) scans.

[0087] In certain embodiments, nuclear medicine images use imaging agents that include radiopharmaceuticals. Nuclear medicine images are obtained after the radiopharmaceutical is administered to a patient and provide information about the distribution of the radiopharmaceutical in the patient. A radiopharmaceutical is a compound that includes a radionuclide.

[0088] As used herein, "radionuclide" refers to a moiety that includes a radioactive isotope of at least one element. Exemplary suitable radionuclides include, but are not limited to, those described herein. In some embodiments, the radionuclide is a radionuclide used for positron emission tomography (PET). In some embodiments, the radionuclide is a radionuclide used for single photon emission computed tomography (SPECT). In some embodiments, a non-limiting list of radionuclides includes 99m Tc, 111 In, 64 Cu, 67 Ga, 68 Ga, 186 Re, 188 Re, 153 Sm, 177 Lu, 67 Cu, 123 I. 124 I. 125 I. 126 I. 131 I. 11 C. 13 N. 15 O. 18 F. 153 Sm, 166 Ho, 177 Lu, 149 Pm, 90 Y. 213 Bi, 103 Pd, 109 Pd, 159 Gd, 140 La, 198 Au, 199 Au, 169 Yb, 175 Yb, 165 Dy, 166 Dy, 105 Rh, 111 Ag, 89 Zr, 225 Ac, 82 Rb, 75 Br, 76 Br, 77 Br, 80 Br, 80m Br, 82 Br, 83 Br, 211 At and 192 Ir.

[0089] As used herein, the term "radiopharmaceutical" refers to a compound that includes a radionuclide. In certain embodiments, the radiopharmaceutical is used for diagnostic and / or therapeutic purposes. In certain embodiments, the radiopharmaceutical comprises a small molecule labeled with one or more radionuclides, an antibody labeled with one or more radionuclides, and an antigen-binding portion of an antibody labeled with one or more radionuclides.

[0090] Nuclear medicine images (e.g., PET scans; e.g., SPECT scans; e.g., whole body scans; e.g., synthetic PET-CT images; e.g., synthetic SPECT-CT images) detect radiation emitted from the radionuclide of the radiopharmaceutical to form an image. The distribution of a particular radiopharmaceutical in a patient's body can be determined by biological mechanisms (e.g., blood flow or perfusion) and by specific enzyme or receptor binding interactions. Different radiopharmaceuticals can be designed to exploit different biological mechanisms and / or particular specific enzyme or receptor binding interactions, so that when administered to a patient, they selectively concentrate in specific types of tissues and / or regions within the patient's body. Greater amounts of radiation are emitted from regions of the patient's body that have higher concentrations of the radiopharmaceutical than other regions, causing these regions to appear brighter in the nuclear medicine image. Therefore, intensity variations within a nuclear medicine image can be used to map the distribution of the radiopharmaceutical within the patient's body. This mapped distribution of the radiopharmaceutical within the patient's body can be used, for example, to infer the presence of cancerous tissue within various regions of the patient's body.

[0091] For example, when administered to a patient, technetium-99m methylene diphosphonate ( 99m Tc MDP) selectively accumulates in bone regions of patients, particularly in areas with abnormal bone formation associated with malignant bone lesions. The selective concentration of the radiopharmaceutical in these areas creates identifiable hot spots - localized areas of high intensity in nuclear medicine images. Therefore, the presence of malignant bone lesions associated with metastatic prostate cancer can be inferred by identifying these hot spots in a patient's whole body scan. As described below, the presence of malignant bone lesions associated with metastatic prostate cancer can be determined based on the administration of the radiopharmaceutical to the patient. 99m Automatic analysis of intensity changes in whole-body scans obtained after Tc MDP is used to calculate a risk index associated with patient overall survival and other prognostic indicators (indicative of disease status, progression, treatment efficacy, etc.). In certain embodiments, it is also possible to use 99m Tc MDP is used in a similar manner to other radiopharmaceuticals.

[0092] In certain embodiments, the particular radiopharmaceutical used depends on the particular nuclear medicine imaging modality used. For example, 18 Sodium fluoride (NaF) also accumulates in bone lesions, similar to 99mTc MDP, but it can be used with PET imaging. In certain embodiments, PET imaging can also utilize a radioactive form of the vitamin choline, which is readily taken up by prostate cancer cells.

[0093] In certain embodiments, radiopharmaceuticals may be used that selectively bind to specific target proteins or receptors, particularly those whose expression is increased in cancer tissues. Such target proteins or receptors include, but are not limited to, tumor antigens such as CEA (which is expressed in colorectal cancer), Her2 / neu (which is expressed in a variety of cancers), BRCA1 and BRCA2 (expressed in breast and ovarian cancers); and TRP-1 and -2 (expressed in melanoma).

[0094] For example, human prostate specific membrane antigen (PSMA) is upregulated in prostate cancer (including metastatic disease). PSMA is expressed by almost all prostate cancers, and its expression is further increased in poorly differentiated, metastatic and hormone-resistant cancers. Therefore, a radiopharmaceutical corresponding to a PSMA binding agent (e.g., a compound with high affinity to PSMA) labeled with one or more radionuclides can be used to obtain a nuclear medicine image of the patient, and the presence and / or state of prostate cancer in various regions (e.g., including but not limited to bone regions) of the patient can be assessed according to the nuclear medicine image. In certain embodiments, when the disease is in a localized state, a nuclear medicine image obtained using a PSMA binding agent is used to identify the presence of cancerous tissue in the prostate. In certain embodiments, when the disease is metastatic, a nuclear medicine image obtained using a radiopharmaceutical including a PSMA binding agent is used to identify the presence of cancerous tissue in multiple regions, and the region not only includes the prostate, but also includes other organs and tissue regions, such as related lungs, lymph nodes and bones.

[0095] In particular, when administered to a patient, radionuclide-labeled PSMA-binding agents selectively accumulate in cancer tissue based on their affinity for PSMA. 99m In a manner similar to that described for Tc MDP, selective concentration of radionuclide-labeled PSMA binders at specific sites within a patient's body creates detectable hot spots in nuclear medicine images. Because the PSMA binders concentrate within various cancerous tissues and areas of the body expressing PSMA, localized cancer within a patient's prostate and / or metastatic cancer in various areas of the patient's body can be detected and evaluated. As described below, risk indices associated with patient overall survival and other prognostic indicators (indicative of disease status, progression, efficacy of treatment, etc.) can be calculated based on automated analysis of intensity changes in nuclear medicine images obtained after administration of a PSMA binder radiopharmaceutical to a patient.

[0096] Various radionuclide-labeled PSMA binding agents can be used as radiopharmaceutical imaging agents for nuclear medicine imaging to detect and evaluate prostate cancer. In certain embodiments, the specific radionuclide-labeled PSMA binding agent used depends on factors such as the specific imaging mode (e.g., PET; e.g., SPECT) and the specific area (e.g., organ) of the patient to be imaged. For example, certain radionuclide-labeled PSMA binding agents are suitable for PET imaging, while other PSMA binding agents are suitable for SPECT imaging. For example, certain radionuclide-labeled PSMA binding agents help image the patient's prostate and are used primarily when the disease is localized, while other PSMA binding agents help image organs and regions throughout the patient's body and can be used to evaluate metastatic prostate cancer.

[0097] Various PSMA-binding agents and radionuclide-labeled versions thereof are described in U.S. Pat. Nos. 8,778,305, 8,211,401, and 8,962,799, each of which is incorporated herein by reference in its entirety.

[0098] Ai PET imaging of radionuclide-labeled PSMA binders

[0099] In certain embodiments, the radionuclide-labeled PSMA binding agent is a radionuclide-labeled PSMA binding agent suitable for PET imaging.

[0100] In certain embodiments, radionuclide-labeled PSMA-binding agents include [18F]DCFPyL (also known as PyL TM ; also known as DCFPyL-18F):

[0101]

[0102] or a pharmaceutically acceptable salt thereof.

[0103] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises [18F]DCFBC:

[0104]

[0105] or a pharmaceutically acceptable salt thereof.

[0106] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 68 Ga-PSMA-HBED-CC (also known as 68 Ga-PSMA-11):

[0107]

[0108] or a pharmaceutically acceptable salt thereof.

[0109] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises PSMA-617:

[0110]

[0111] or a pharmaceutically acceptable salt thereof. In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 68 Ga-PSMA-617, which is used 68 Ga-labeled PSMA-617, or a pharmaceutically acceptable salt thereof. In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 177 Lu-PSMA-617, which is used 177 Lu-labeled PSMA-617, or a pharmaceutically acceptable salt thereof.

[0112] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises PSMA-I&T:

[0113]

[0114] or a pharmaceutically acceptable salt thereof. In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 68 Ga-PSMA-I&T, which is used 68 Ga-labeled PSMA-I&T, or a pharmaceutically acceptable salt thereof.

[0115] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises PSMA-1007:

[0116]

[0117] or a pharmaceutically acceptable salt thereof. In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 18 F-PSMA-1007, which is used 18 F-labeled PSMA-1007, or a pharmaceutically acceptable salt thereof.

[0118] A.ii SPECT imaging of radionuclide-labeled PSMA binders

[0119] In certain embodiments, the radionuclide-labeled PSMA binding agent is a radionuclide-labeled PSMA binding agent suitable for SPECT imaging.

[0120] In certain embodiments, the radionuclide-labeled PSMA-binding agent includes 1404 (also known as MIP-1404):

[0121]

[0122] or a pharmaceutically acceptable salt thereof.

[0123] In certain embodiments, the radionuclide-labeled PSMA-binding agent includes 1405 (also known as MIP-1405):

[0124]

[0125] or a pharmaceutically acceptable salt thereof.

[0126] In certain embodiments, the radionuclide-labeled PSMA-binding agent includes 1427 (also known as MIP-1427):

[0127]

[0128] or a pharmaceutically acceptable salt thereof.

[0129] In certain embodiments, the radionuclide-labeled PSMA-binding agent includes 1428 (also known as MIP-1428):

[0130]

[0131] or a pharmaceutically acceptable salt thereof.

[0132] In certain embodiments, a PSMA-binding agent is prepared by chelating it to a radioisotope of a metal [e.g., a radioisotope of technetium (Tc) (e.g., technetium-99m ( 99m Tc)); for example, a radioactive isotope of rhenium (Re) (e.g., rhenium-188 ( 188 Re); for example, rhenium-186 ( 186 Re)); for example, radioactive isotopes of yttrium (Y) (e.g., 90 Y); for example, radioactive isotopes of lutetium (Lu) (e.g., 177 Lu); for example, radioactive isotopes of gallium (Ga) (e.g., 68 Ga; for example, 67 Ga); for example, radioactive isotopes of indium (e.g., 111 In); for example, radioactive isotopes of copper (Cu) (e.g., 67 Cu)] is labeled with a radionuclide.

[0133] In certain embodiments, 1404 is labeled with a radionuclide (e.g., chelated with a radioisotope of a metal). In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 99m Tc-MIP-1404, which is used 99mTc-labeled (eg, chelated) 1404:

[0134]

[0135] or a pharmaceutically acceptable salt thereof. In certain embodiments, 1404 can be chelated to other metal radioisotopes [e.g., radioisotopes of rhenium (Re) (e.g., rhenium-188 ( 188 Re); for example, rhenium-186 ( 186 Re)); for example, radioactive isotopes of yttrium (Y) (e.g., 90 Y); for example, radioactive isotopes of lutetium (Lu) (e.g., 177 Lu); for example, radioactive isotopes of gallium (Ga) (e.g., 68 Ga; for example, 67 Ga); for example, radioactive isotopes of indium (e.g., 111 In); for example, radioactive isotopes of copper (Cu) (e.g., 67 Cu)] to form a 99m Compounds with structures similar to those shown in Tc-MIP-1404, in which other metal radioisotopes are substituted 99m Tc.

[0136] In certain embodiments, 1405 is labeled with a radionuclide (e.g., chelated with a radioisotope of a metal). In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 99m Tc-MIP-1405, which is used 99m Tc-labeled (eg, chelated) 1405:

[0137]

[0138] or a pharmaceutically acceptable salt thereof. In certain embodiments, 1405 can be chelated to other metal radioisotopes [e.g., radioisotopes of rhenium (Re) (e.g., rhenium-188 ( 188 Re); for example, rhenium-186 ( 186 Re)); for example, radioactive isotopes of yttrium (Y) (e.g., 90 Y); for example, radioactive isotopes of lutetium (Lu) (e.g., 177 Lu); for example, radioactive isotopes of gallium (Ga) (e.g., 68 Ga; for example, 67 Ga); for example, radioactive isotopes of indium (e.g., 111 In); for example, radioactive isotopes of copper (Cu) (e.g., 67 Cu)] to form a 99mCompounds with structures similar to those shown in Tc-MIP-1405, in which other metal radioisotopes are substituted 99m Tc.

[0139] In certain embodiments, 1427 is labeled with (eg, chelated to) a radioactive isotope of a metal to form a compound according to the formula:

[0140]

[0141] or a pharmaceutically acceptable salt thereof, wherein M is a metal radioisotope [e.g., a radioisotope of technetium (Tc) (e.g., technetium-99m ( 99m Tc)); for example, a radioactive isotope of rhenium (Re) (e.g., rhenium-188 ( 188 Re); for example, rhenium-186 ( 186 Re)); for example, radioactive isotopes of yttrium (Y) (e.g., 90 Y); for example, radioactive isotopes of lutetium (Lu) (e.g., 177 Lu); for example, radioactive isotopes of gallium (Ga) (e.g., 68 Ga; for example, 67 Ga); for example, radioactive isotopes of indium (e.g., 111 In); for example, radioactive isotopes of copper (Cu) (e.g., 67 Cu)], 1427 was marked with it.

[0142] In certain embodiments, 1428 is labeled with (eg, chelated with) a radioactive isotope of a metal to form a compound according to the formula:

[0143]

[0144] or a pharmaceutically acceptable salt thereof, wherein M is a metal radioisotope [e.g., a radioisotope of technetium (Tc) (e.g., technetium-99m ( 99m Tc)); for example, a radioactive isotope of rhenium (Re) (e.g., rhenium-188 ( 188 Re); for example, rhenium-186 ( 186 Re)); for example, radioactive isotopes of yttrium (Y) (e.g., 90 Y); for example, radioactive isotopes of lutetium (Lu) (e.g., 177 Lu); for example, radioactive isotopes of gallium (Ga) (e.g., 68 Ga; for example, 67 Ga); for example, radioactive isotopes of indium (e.g., 111 In); for example, radioactive isotopes of copper (Cu) (e.g., 67Cu)], 1428 is marked with it.

[0145] In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises PSMA I&S:

[0146]

[0147] or a pharmaceutically acceptable salt thereof. In certain embodiments, the radionuclide-labeled PSMA-binding agent comprises 99m Tc-PSMA I&S, which is used 99m Tc-labeled PSMA I&S, or a pharmaceutically acceptable salt thereof.

[0148] A.iii Whole body bone scan

[0149] Go to Figure 1 BSI Cloud 130 refers to a cloud-based decision support system that implements BSI values. BSI refers to bone scan index, which is obtained by deriving bone scan data from the bone marrow of patients who were treated with the radionuclide technetium-99m methylene diphosphonate ( 99m The BSI is a value calculated from a method for detecting bone lesions using a whole body scan (and anterior and posterior views) performed with a gamma camera after Tc MDP. Further explanation of BSI is provided, for example, in U.S. Patent No. 8,855,387, which is incorporated herein by reference in its entirety, and in U.S. Patent Application No. 15 / 282422, filed on September 30, 2016, which is incorporated herein by reference in its entirety.

[0150] In particular, the BSI is calculated from a whole body scan by segmenting the front and back views of the whole body scan of the patient to geographically identify the boundaries of regions within the views corresponding to various parts of the patient's skeleton. Segmentation of the patient's skeleton can be performed using various methods, such as methods based on active shape models and the atlas image registration method described in U.S. Patent No. 8,855,387 (which registers the front and back views of a whole body scan, where the reference front and back views of a reference whole body scan have been segmented (referred to as atlas images)). Other methods based on machine learning techniques (e.g., artificial neural networks (ANN); e.g., convolutional neural networks (CNN)) can also be used.

[0151] The BSI value is calculated based on the intensity values ​​of the whole body scan within the identified boundaries of each bone region. As discussed above, hot spots corresponding to localized areas of high intensity within the views (front view and / or back view) of the whole body scan can be detected. Various methods (including machine learning techniques such as ANN as described in U.S. Patent No. 8,855,387) can be used to detect hot spots and / or classify them as corresponding to cancerous lesions (e.g., metastases).

[0152] Once detected, hot spots corresponding to cancerous tissue lesions within the patient's bones can be used to determine a risk index that provides a measure of the patient's disease state. For example, the level of cancerous tissue within one or more regions (e.g., a specific bone and / or an overall bone region) can be determined based on the characteristics of the detected hot spots (e.g., detected hot spots classified as metastases). For example, the level of cancerous tissue within a region (e.g., a specific bone; e.g., an overall bone region) can be determined based on the total number of hot spots detected within the region, the total volume of hot spots detected within the region, the average intensity of the hot spots detected, the maximum intensity of the hot spots detected, etc., and combinations thereof (e.g., as a function thereof). Properties of one or more regions, such as their area or volume, can also be used. For example, the total number and / or total volume of hot spots detected can be normalized (e.g., divided by the volume and / or area) with the volume and / or area of ​​the region in which they are detected. The risk index can be determined directly based on the determined cancer tissue levels in one or more regions (e.g., as the cancer tissue levels in a single region), or determined based on the cancer tissue levels in multiple regions (e.g., as an average; e.g., as a scaled sum; e.g., as a ratio; etc.), or even determined using machine learning methods.

[0153] For example, a BSI value is a risk index that is a numerical value that quantifies the portion of a patient's total bone that is involved by cancerous tissue (e.g., a tumor) based on detected hot spots. BSI values ​​can be compared between different patients and used as an objective measure of a particular patient's disease status and risk. Notably, because the BSI is calculated in an automated fashion, variability caused by human factors (e.g., radiologists' interpretation of images) is avoided.

[0154] In addition, a variety of actionable information can be obtained from a patient's BSI value. For example, the BSI can be associated with a prognostic value that provides a measure of a patient's disease state, progression, life expectancy (e.g., overall survival), treatment efficacy, etc. Thus, a patient's BSI value can be used and tracked over time (e.g., over the course of multiple follow-up visits with one or more doctors) to provide them or their doctors with objective indicators of what state their cancer is in, how quickly it is progressing, what their outlook is, and whether one or more specific treatments are proving effective.

[0155] A.iv Positron Emission Tomography (PET) Scan

[0156] PyL Cloud 120 refers to the use of the drug PyL TM A cloud-based system for acquiring medical images, the Pharmacy PyL TM It is used 18F-labeled DCFPyL ([18F]DCFPyL). After injection of the imaging agent, the patient underwent a positron emission tomography (PET) scan to identify hot spots, and a CT scan. About PyL TM Further information on imaging agents is provided above, for example in US Pat. No. 8,778,305, which is incorporated herein by reference in its entirety.

[0157] In certain embodiments, PET scans and CT scans are combined to form a composite image including a CT scan superimposed with a PET scan. As used herein, superimposing an image (e.g., a PET scan) with another image (e.g., a CT scan) refers to establishing a mapping between coordinates and / or pixels or voxels of two images representing the same physical location (e.g., in a patient's body). CT scans provide accurate anatomical information in the form of detailed three-dimensional (3D) images of internal organs, bones, soft tissues, and blood vessels. Therefore, the 3D boundaries of specific regions of imaging tissues can be accurately identified by analysis of CT scans. For example, the automatic segmentation of CT scans can be performed to identify the 3D boundaries of other regions of specific organs (e.g., prostate, lymph nodes, lungs), sub-organs, organ regions, and imaging tissues (e.g., specific bones and overall skeletal regions of patients). The automatic segmentation of CT scans can be accomplished by various methods, including machine learning techniques [e.g., ANN-based methods (including, for example, convolutional neural networks (CNN))], atlas image registration, and combinations thereof. In certain embodiments, manual segmentation of CT images may also be utilized, either alone or in combination with automatic segmentation methods (eg, to refine 3D boundaries identified via automatic segmentation; eg, to provide an initial starting point for automatic segmentation methods).

[0158] Once the 3D boundaries of various regions are identified within the CT scan of the composite image, the identified 3D boundaries can be transferred to the PET image by means of a mapping between the CT scan of the composite image and the PET scan. Thus, regions of the PET image that fall within and / or outside the identified 3D boundaries can be accurately identified.

[0159] In some embodiments, a composite image including a CT scan superimposed with a PET scan is obtained using a dedicated PET-CT scanning instrument common in many hospitals and uploaded as a medical image to a cloud-based platform described herein. In some embodiments, a PET scan and a corresponding CT scan are obtained separately (but substantially simultaneously) and uploaded to a cloud-based platform described herein. In these cases, the separately obtained PET and CT scans can be automatically fused to create a composite image.

[0160] In some embodiments, once the 3D boundaries of various regions are identified within a PET scan, one or more risk indices may be calculated in a manner similar to that described above with respect to the BSI. In particular, in some embodiments, intensity values ​​of a PET scan associated with the 3D boundaries of the identified region (e.g., inside and / or outside thereof) may be used to determine the level of cancerous tissue within the identified region, for example, based on features about detected hot spots (e.g., detected hot spots corresponding to metastases). A risk index may then be calculated based on the determined level of cancerous tissue. For example, hot spots within a PET scan may be identified and used to calculate one or more risk indices based on features (e.g., their size, number, and distribution relative to the identified region). As with the BSI, the risk index determined from the PET scan may be correlated with a prognostic value and tracked over time (e.g., over the course of multiple follow-up visits to one or more physicians) to provide the patient or their physician with an objective indicator of what state their cancer is in, how quickly it is progressing, what their outlook is, and whether one or more specific treatments are proving effective. The use of PyL TM The methods of PET imaging as a radiopharmaceutical can be applied to a variety of other radiopharmaceuticals, such as those described above, including but not limited to NaF, radioisotopes of choline, and any of the PSMA binders described in Section Ai above.

[0161] Av single photon emission computed tomography (SPECT) scan

[0162] 1404 Cloud 130 refers to the use of drugs 99m Tc-MIP-1404 A cloud-based system for obtaining medical images, as described above, the agent 99m Tc-MIP-1404 is made of Tc 99m Labeled 1404. After injection of the imaging agent, the patient receives a single photon emission computed tomography (SPECT) scan (e.g., to identify hot spots) and a computed tomography (CT) scan (e.g., to identify anatomical features). These images are superimposed to create a composite image (SPECT / CT). 99m Further information on Tc-MIP-1404 imaging agents is provided above, for example in US Pat. Nos. 8,211,401 and 8,962,799, both of which are incorporated herein by reference in their entirety.

[0163] The SPECT and CT scans can be analyzed in a manner similar to that described above with respect to the PET and CT scans to determine one or more risk indices. In particular, like the PET and CT scans, the SPECT scans and the CT scans can be combined in a composite image (in which the CT scan is superimposed with the SPECT scan) - a composite SPECT-CT image. Like the composite PET-CT image, the composite SPECT-CT image can be directly received by the cloud-based platform described herein as a medical image (e.g., having been obtained by a dedicated SPECT-CT imager in a hospital), or can be created by the cloud-based platform after receiving separate SPECT scans and CT scans.

[0164] Similar to PET-CT images, 3D boundaries of various regions of imaged tissue can be identified within the SPECT image by means of overlay of the CT image with the synthetic image. One or more risk indices can be calculated using intensity variations within the SPECT image associated with the 3D boundaries. As with the method of calculating risk indices from synthetic PET-CT images, this can include determining the level of cancerous tissue within the identified region, such as by detecting hot spots within the 3D boundaries of the identified region. Such risk indices can be correlated with prognostic values ​​and tracked over time (e.g., over the course of multiple follow-up visits with one or more physicians) to provide the patient or their physician with objective indicators of what state their cancer is in, how quickly it is progressing, what their outlook is, and whether one or more specific treatments are proving effective. 99m The method of SPECT imaging of Tc-MIP-1404 as a radiopharmaceutical can be applied to various other radiopharmaceuticals, such as those described above, including but not limited to any of the PSMA binders described in Section A.ii above.

[0165] B. Platform Services and Computing Device Components

[0166] Figure 2 2 is a schematic diagram 200 showing the relationship between a platform and a computing device (e.g., a personal computer or a mobile computing device such as a smart phone) running an application, according to an illustrative embodiment of the invention. The platform performs various services, such as user authentication ("Auth"), image and other data storage ("SliceBox"), etc. - more services than shown may be provided. An application may have a platform component and a device component (e.g., a native application on a client device).

[0167] Fig. 9 An exemplary architecture 900 is shown for implementing the cloud-based platform described herein and providing various services (eg, the BSI Cloud, PyL Cloud, and 1404 Cloud Services described above). Fig. 9The architecture shown in can be used to implement the platform described herein on various data centers, including publicly available data centers. The data centers provide infrastructure in the form of servers and networks, and provide services such as networking, messaging, authentication, logging, and storage. The architecture 900 of the platform uses a series of functional units with limited scope (called microservices). Each microservice handles a set of independent tasks, such as image storage, calculation of risk index, identification of medical image types, and other tasks. Services (e.g., microservices) can communicate with each other using standard protocols such as the Hypertext Transfer Protocol (HTTP). Fig. 9 As shown in architecture 900, organizing the platform into a network of microservices allows parts of the platform to be scaled individually to meet high demand and ensure minimal downtime.

[0168] In certain embodiments, this architecture allows components to be improved or replaced without affecting other parts of the platform. Fig. 9 The exemplary architecture 900 shown in includes a set of microservices 920 that are common to two or more applications in the platform. The left figure 910 and the right figure 930 show microservices in two applications. The microservice network shown in the left figure 910 implements a version of the BSI cloud service as described above, and provides calculation of an automatic BSI index (aBSI), which is a risk index derived from automatic analysis of a whole body scan obtained with a gamma camera. The microservice network shown in the right figure 930 implements a version of the 1404 cloud service as described above, and provides automatic calculation of a SPECT index, which is a risk index derived from automatic analysis of synthetic SPECT-CT images.

[0169] C. Image Collection, Analysis, and Results Presentation

[0170] Figure 3 is a GUI window 300 (displayed to the user) in the BSI cloud application that allows the user to enter information about the patient and upload and / or access medical images of the patient, such as a series of images acquired over a certain period of time.

[0171] Figure 4 4 is a GUI window 400 in the BSI cloud application showing a representative whole body gamma camera image showing hot spots automatically identified by the system and the corresponding total calculated BSI value for a particular set of images acquired at a given time. The chart 410 on the left shows how the BSI value for this particular patient changes (increases) over time.

[0172] Figure 5510, which may be signed and dated by the radiologist. In some embodiments, the automatically identified hot spots may be adjusted by the radiologist (or other medical practitioner seeing the patient), with the changes reflected in the report. For example, the radiologist may deactivate an identified hot spot, or the radiologist may activate a new hot spot, and such changes may affect the calculated BSI value displayed in the report.

[0173] Figure 6 is a block flow diagram of an illustrative network-based (eg, cloud-based) decision support system according to an illustrative embodiment of the invention. Figure 6 Various functions that may be performed by the cloud-based services described herein are shown. These include: (i) receiving and storing multiple sets of medical images in a database; (ii) accessing one or more medical images for transmission to a user for display on a user computing device; (iii) automatically analyzing the medical images by a processor to calculate a risk index (e.g., BSI) and / or generate a risk map; (iv) generating a radiologist report for the patient based on the patient images and / or the risk index / risk map; and applying a machine learning algorithm to update a process for the automatic analysis function (iii).

[0174] Fig.10 10 is a schematic diagram showing clinical stages 1010 of prostate cancer progression and various treatments 1020 and diagnostic imaging modalities 1030 suitable for various disease states. As shown in the schematic diagram, different imaging modalities and / or different radiopharmaceuticals may be appropriate depending on the clinical state of the patient's prostate cancer. Similarly, different risk indices calculated based on different imaging modalities and / or different radiopharmaceuticals may be most appropriate depending on the state of the patient's prostate cancer.

[0175] For example, in certain embodiments, synthetic SPECT-CT imaging may be used when a patient has or is suspected of having localized prostate cancer. SPECT scans for evaluating synthetic SPECT-CT images of localized prostate cancer may be performed after administration of specific radiopharmaceuticals (e.g., 99m Tc-MIP-1404) is obtained after, it contributes to the imaging of localized prostate cancer. Therefore, any SPECT-CT image itself, its derivatives and the risk index calculated from the SPECT-CT image can be used to assess the risk, disease state, progression, treatment efficacy, etc. of localized prostate cancer.

[0176] In certain embodiments, when a patient has or is suspected of having metastatic prostate cancer (which has spread outside the prostate), other imaging techniques may be used. 99mThe whole body scan obtained after Tc-MDP evaluates the tumor burden in the patient's bones. As discussed above, when a patient's prostate cancer has metastasized to his bones, the BSI value calculated from the whole body scan can be used to assess risk, disease status, progression, treatment efficacy, etc. In certain embodiments, certain imaging modalities and / or radiopharmaceuticals can be used to evaluate prostate cancer in both localized and metastatic states. For example, Fig.10 As shown, PET-CT imaging can be used to evaluate prostate cancer in both localized and metastatic states. As shown, these PET-CT images can be imaged using appropriate radiopharmaceuticals (e.g., PyL TM ) is obtained, which helps in imaging localized and metastatic prostate cancer.

[0177] In certain embodiments, the cloud-based platform facilitates the evaluation of prostate cancer progression and treatment efficacy over time. Fig.11 As shown in the exemplary process 1100 of the block flow diagram of , in some embodiments, medical images of a particular patient are repeatedly received and stored over time during multiple follow-up visits of the patient to one or more physicians and / or clinical specialists (e.g., radiologists) 1110. In this way, a series of medical images of the patient is obtained. The series of medical images can be automatically analyzed to determine the value of one or more risk indexes so as to track changes in the determined values ​​over time 1120. The determined risk index values ​​can be stored 1130a (e.g., for further processing). In some embodiments, the process implements the display of a graphical representation (e.g., a chart) of the determined risk index values ​​(e.g., on a user computing device; e.g., via a web-based portal) 1130b.

[0178] Notably, with the ability of the cloud-based platform described herein to receive, store, and analyze a variety of different medical image types (eg, synthetic SPECT-CT images, whole body scans, and synthetic PET-CT images), the medical images do not have to be of the same type.

[0179] For example, when a patient's prostate cancer is localized, a first imaging modality and a first radiopharmaceutical (e.g., using 99m If the patient's prostate cancer progresses to a metastatic state, the second subseries of images may include images acquired via a different second imaging modality and / or a different second radiopharmaceutical. For example, the second subseries of medical images may be images acquired using PyL TM For example, the second sub-series of medical images may be obtained using 99m Whole body scan acquired with Tc-MDP.

[0180] A risk index can be calculated for the first image subseries and the second image subseries to provide a unified picture of the patient's prostate cancer progression and treatment over time. This method can be performed on multiple patients not only for decision-making purposes regarding each patient's disease progression and specific treatment course, but also in the context of clinical trials, for example, to compare the efficacy of a specific treatment with other treatments or controls.

[0181] D. Computer system and network environment

[0182] Figure 7 An illustrative network environment 700 for use with the methods and systems described herein is shown. Briefly summarized, reference is now made to Figure 7 , a block diagram of an exemplary cloud computing environment 700 is shown and described. The cloud computing environment 700 may include one or more resource providers 702a, 702b, 702c (collectively referred to as 702). Each resource provider 702 may include computing resources. In some embodiments, computing resources may include any hardware and / or software for processing data. For example, computing resources may include hardware and / or software capable of executing algorithms, computer programs, and / or computer applications. In some embodiments, exemplary computing resources may include application servers and / or databases with storage and retrieval capabilities. Each resource provider 702 may be connected to any other resource provider 702 in the cloud computing environment 700. In some embodiments, the resource provider 702 may be connected via a computer network 708. Each resource provider 702 may be connected to one or more computing devices 704a, 704b, 704c (collectively referred to as 704) via a computer network 708.

[0183] The cloud computing environment 700 may include a resource manager 706. The resource manager 706 may be connected to the resource providers 702 and the computing devices 704 via a computer network 708. In some implementations, the resource manager 706 may facilitate the provision of computing resources by one or more resource providers 702 to the plurality of computing devices 704. The resource manager 706 may receive a request for computing resources from a particular computing device 704. The resource manager 706 may identify one or more resource providers 702 that can provide the computing resources requested by the computing device 704. The resource manager 706 may select a resource provider 702 to provide the computing resources. The resource manager 706 may facilitate the connection between the resource provider 702 and the particular computing device 704. In some implementations, the resource manager 706 may establish a connection between the particular resource provider 702 and the particular computing device 704. In some implementations, the resource manager 706 may redirect the particular computing device 704 to the particular resource provider 702 with the requested computing resources.

[0184] Figure 8 Examples of computing devices 800 and mobile computing devices 850 that can be used in the methods and systems described in the present disclosure are shown. Computing device 800 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Mobile computing device 850 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions are intended to be examples only, and are intended to be limiting.

[0185] The computing device 800 includes a processor 802, a memory 804, a storage device 806, a high-speed interface 808 connected to the memory 804 and a plurality of high-speed expansion ports 810, and a low-speed interface 812 connected to a low-speed expansion port 814 and the storage device 806. Each of the processor 802, the memory 804, the storage device 806, the high-speed interface 808, the high-speed expansion port 810, and the low-speed interface 812 is interconnected using various buses and can be appropriately mounted on a common motherboard or otherwise mounted. The processor 802 can process instructions for execution within the computing device 800 (including instructions stored in the memory 804 or on the storage device 806) to display graphical information of a GUI on an external input / output device (e.g., a display 816 connected to the high-speed interface 808). In other embodiments, multiple processors and / or multiple buses and multiple memories and memory types can be appropriately used. Moreover, multiple computing devices can be connected, each of which provides a portion of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). Thus, as the term is used herein, when multiple functions are described as being performed by a "processor," this encompasses embodiments in which the multiple functions are performed by any number of processor(s) of any number of computing devices(s). Furthermore, when functions are described as being performed by a "processor," this encompasses embodiments in which the functions are performed by any number of processor(s) of any number of computing devices(s) (e.g., in a distributed computing system).

[0186] The memory 804 stores information within the computing device 800. In some implementations, the memory 804 is a volatile memory unit. In some implementations, the memory 804 is a non-volatile memory unit. The memory 804 may also be another form of computer-readable medium, such as a magnetic disk or an optical disk.

[0187] The storage device 806 can provide mass storage for the computing device 800. In some embodiments, the storage device 806 can be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a magnetic tape device, a flash memory or other similar solid-state memory device, or a device array (a device included in a storage area network or other configuration). Instructions can be stored in an information carrier. The instructions, when executed by one or more processing devices (e.g., processor 802), perform one or more methods, such as those described above. The instructions can also be stored by one or more storage devices, such as a computer or machine-readable medium (e.g., memory 804, storage device 806, or memory on processor 802).

[0188] The high-speed interface 808 manages bandwidth-intensive operations of the computing device 800, while the low-speed interface 812 manages less bandwidth-intensive operations. This functional allocation is merely an example. In some embodiments, the high-speed interface 808 is coupled to the memory 804, the display 816 (e.g., through a graphics processor or accelerator), and to the high-speed expansion port 810, which can accept various expansion cards (not shown). In the described embodiment, the low-speed interface 812 is coupled to the storage device 806 and the low-speed expansion port 814. The low-speed expansion port 814 can include various communication ports (e.g., USB, Ethernet, wireless Ethernet), which can be connected to one or more input / output devices (e.g., keyboards), pointing devices, scanners, or networking devices (e.g., switches or routers), for example, through a network adapter.

[0189] The computing device 800 can be implemented in a variety of different forms, as shown. For example, it can be implemented as a standard server 820, or multiple times in a group of such servers. In addition, it can be implemented in a personal computer (e.g., a laptop computer 822). It can also be implemented as part of a rack server system 824. Alternatively, components from the computing device 800 can be combined with other components in a mobile device (not shown, such as a mobile computing device 850). Each of these devices can contain one or more of the computing device 800 and the mobile computing device 850, and the entire system can be composed of multiple computing devices that communicate with each other.

[0190] The mobile computing device 850 includes a processor 852, a memory 864, an input / output device (e.g., a display 854), a communication interface 866, and a transceiver 868, among other components. The mobile computing device 850 may also be provided with a storage device (e.g., a microdrive or other device) to provide additional storage. Each of the processor 852, the memory 864, the display 854, the communication interface 866, and the transceiver 868 are interconnected using various buses, and several of the components may be appropriately mounted on a common motherboard or otherwise mounted.

[0191] The processor 852 may execute instructions within the mobile computing device 850, including instructions stored in the memory 864. The processor 852 may be implemented as a chipset of chips that includes multiple individual analog and digital processors. The processor 852 may provide, for example, coordination of other components of the mobile computing device 850 (e.g., a user interface, control of applications executed by the mobile computing device 850, and wireless communications performed by the mobile computing device 850).

[0192] The processor 852 can communicate with the user through the control interface 858 and the display interface 856 connected to the display 854. The display 854 can be, for example, a TFT (thin film transistor liquid crystal display) display or an OLED (organic light emitting diode) display, or other suitable display technology. The display interface 856 may include suitable circuits for driving the display 854 to present graphics and other information to the user. The control interface 858 can receive commands from the user and convert them for submission to the processor 852. In addition, the external interface 862 can provide communication with the processor 852 to enable near-area communication of the mobile computing device 850 with other devices. The external interface 862 can provide, for example, wired communication in some embodiments, or wireless communication in other embodiments, and multiple interfaces can also be used.

[0193] The memory 864 stores information within the mobile computing device 850. The memory 864 may be implemented as one or more of a computer-readable medium, a volatile memory unit, or a non-volatile memory unit. An expansion memory 874 may also be provided and connected to the mobile computing device 850 via an expansion interface 872, which may include, for example, a SIMM (single inline memory module) card interface. The expansion memory 874 may provide additional storage space for the mobile computing device 850, or may also store applications or other information of the mobile computing device 850. Specifically, the expansion memory 874 may include instructions for executing or supplementing the above-mentioned processes, and may also include security information. Thus, for example, the expansion memory 874 may be provided as a security module for the mobile computing device 850, and may be programmed with instructions that allow secure use of the mobile computing device 850. In addition, security applications may be provided via a SIMM card, as well as other information, such as placing identification information on the SIMM card in a non-hackable manner.

[0194] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as discussed below. In some embodiments, the instructions are stored in an information carrier and, when executed by one or more processing devices (e.g., processor 852), perform one or more methods, such as those described above. The instructions may also be stored by one or more storage devices, such as one or more computer or machine readable media (e.g., memory 864, expansion memory 874, or memory on processor 852). In some embodiments, the instructions may be received in a propagated signal, such as by transceiver 868 or external interface 862.

[0195] The mobile computing device 850 may communicate wirelessly via a communication interface 866, which may include digital signal processing circuitry, if necessary. The communication interface 866 may provide for communication in various modes or protocols, such as GSM voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Messaging Service) or MMS messaging (Multimedia Messaging Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service). This communication may be performed, for example, using a transceiver 868 that uses radio frequencies. In addition, short-range communications may be possible, such as using Wi-Fi TM Or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 870 can provide additional navigation and location-related wireless data to the mobile computing device 850, which can be used appropriately by applications running on the mobile computing device 850.

[0196] The mobile computing device 850 may also communicate audibly using an audio codec 860, which may receive voice information from a user and convert it into usable digital information. The audio codec 860 may also generate audible sound for the user, such as through a speaker, such as in an earpiece of the mobile computing device 850. This sound may include sound from a voice phone call, may include recorded sound (e.g., voice messages, music files, etc.), and may also include sound generated by applications operating on the mobile computing device 850.

[0197] Mobile computing device 850 can be implemented in many different forms, as shown in the figure. For example, it can be implemented as a cellular phone 880. It can also be implemented as a part of a smart phone 882, a personal digital assistant or other similar mobile device.

[0198] Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementations in one or more computer programs executable and / or interpretable on a programmable system comprising at least one programmable processor, which can be special purpose or general purpose, coupled to receive data and instructions from a storage system and to send data and instructions to a storage system.

[0199] These computer programs (also referred to as programs, software, software applications, or code) contain machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or in assembly / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term machine-readable signal refers to any signal for providing machine instructions and / or data to a programmable processor.

[0200] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) screen) for displaying information to the user, and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, voice, or tactile input.

[0201] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server), or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0202] The computing system may include a client and a server. The client and the server are usually remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by means of computer programs that run on the respective computers and have a client-server relationship with each other. In some embodiments, the modules and / or services described herein (e.g., cloud-based services, such as BSI cloud 110, PyL cloud 120, 1404 cloud 130; for example, any microservices described herein) may be separated, combined or incorporated into a single or combined module and / or service. The modules and / or services depicted in the accompanying drawings are not intended to limit the system described herein to the software architecture shown therein.

[0203] While the present invention has been particularly shown and described with reference to particular preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A web-based system for generating a disease risk map for use as a decision support for assessing cancer risk or cancer recurrence risk, comprising: processor; and A memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to perform the following functions: (i) accessing one or more medical images associated with a particular patient from a database; (ii) analyzing the one or more medical images associated with the particular patient using a machine learning algorithm to generate the risk map, wherein the risk map comprises a visual representation of tissue overlaid with a graphical representation of one or more regions marking a risk of cancer or a risk of cancer recurrence; as well as (iii) displaying the risk map via a graphical user interface (GUI) for a user to present or view the risk map, wherein the analysis of the one or more medical images comprises creating a 3D image of the one or more areas of cancer risk or cancer recurrence risk overlaid on the one or more medical images, and wherein the 3D image comprises geographic identification of one or more specific tissue areas overlaid on the one or more medical images.

2. The system of claim 1, wherein the one or more medical images comprise one or more magnetic resonance images (MIR).

3. The system of claim 1, wherein the one or more medical images comprise (i) one or more PET / CT images or (ii) one or more SPECT / CT images. 4 . The system of claim 1 , wherein the visual representation of the tissue is at least a portion of one of the one or more medical images of the particular patient.

5. The system of claim 1, wherein the cancer is prostate cancer.

6. The system of claim 1, wherein the visual representation of tissue comprises a visual representation of bone.

7. The system of claim 1, wherein the one or more specific tissue regions include one or more selected from the group consisting of an organ, an organ structure, a sub-organ, and an organ region.

8. The system of claim 1, wherein the one or more specific tissue regions include one or more specific bones.

9. The system of claim 1, wherein the instructions cause the processor to generate a radiologist report for the particular patient based at least in part on the analysis of the one or more medical images.

10. The system of claim 9, wherein the radiologist report includes the one or more medical images and the risk map.

11. The system of claim 9, wherein the radiologist report comprises one or more reference images or reference risk maps.

12. The system of claim 1, wherein the instructions cause the processor to apply a machine learning algorithm to update a process for automatically analyzing the one or more medical images using image data accumulated in the database.

13. A method for generating a disease risk map for use as a decision support for assessing cancer risk or cancer recurrence risk, the method comprising: (i) accessing, by a processor, from a database one or more medical images associated with a particular patient; (ii) automatically analyzing, by the processor, the one or more medical images using a machine learning algorithm to generate the risk map, wherein the risk map comprises a visual representation of tissue overlaid with a graphical representation of one or more regions marking a risk of cancer or a risk of cancer recurrence; as well as (iii) displaying the risk map by the processor via a graphical user interface (GUI) for a user to present or view the risk map, wherein step (ii) comprises creating a 3D image of the one or more areas of cancer risk or cancer recurrence risk overlaid on the one or more medical images, and wherein the 3D image comprises geographic identification of one or more specific tissue areas overlaid on the one or more medical images.

Images