TECHNICAL FIELD

This invention relates generally to systems and methods for creation, analysis, and/or presentation of medical image data. More particularly, in certain embodiments, the invention relates to a cloud-based platform and supported GUI decision-making tools for use by medical practitioners and/or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and/or to track treatment and/or the progress of a disease.

BACKGROUND

Targeted image analysis involves the use of radiolabeled small molecules that bind to specific receptors, enzymes and proteins in the body that are altered during the evolution of disease. After administration to a patient, these molecules circulate in the blood until they find their intended target. The bound radiopharmaceutical remains at the site of disease, while the rest of the agent clears from the body. The radioactive portion of the molecule serves as a beacon so that an image may be obtained depicting the disease location and concentration using commonly available nuclear medicine cameras, known as single-photon emission computerized tomography (SPECT) or positron emission tomography (PET) cameras, found in most hospitals throughout the world. Physicians can then use this information to determine the presence and the extent of disease in a patient. The physician can use this information to provide a recommended course of treatment to the patient and to track the progression of disease.

There are a variety of software-based analytical techniques available for analysis and enhancement of PET and SPECT images that can be used by a radiologist or physician. There are also a number of radiopharmaceuticals available for imaging particular kinds 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 the detection of primary and metastatic prostate cancer. 1404 is labeled with technetium-99 m, a gamma-emitter isotope that is widely available, relatively inexpensive, facilitates efficient preparation, and has spectrum characteristics attractive for nuclear medicine imaging applications.

Another example radiopharmaceutical is PyL™ (also known as [18F]DCFPyL), which is 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 demonstrated that PET imaging with PyL™ showed high levels of PyL™ uptake in sites of putative metastatic disease and primary tumors, suggesting the potential for high sensitivity and specificity in detecting prostate cancer.

An oncologist may use images from a targeted PET or SPECT study of a patient as input in her assessment of whether the patient has a particular disease, e.g., prostate cancer, what stage of the disease is evident, what the recommended course of treatment (if any) would be, whether surgical intervention is indicated, and likely prognosis. The oncologist may use a radiologist report in this assessment. A radiologist report is a technical evaluation of the PET or SPECT images prepared by a radiologist for a physician who requested the imaging study and includes, for example, the type of study performed, the clinical history, a comparison between images, the technique used to perform the study, the radiologist's observations and findings, as well as overall impressions and recommendations the radiologist may have based on the imaging study results. A signed radiologist report is sent to the physician ordering the study for the physician's review, followed by a discussion between the physician and patient about the results and recommendations for treatment.

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 formulate an assessment and treatment recommendation, 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 tests based on initial results.

If an imaging study shows that the patient has a particular disease or condition (e.g., cancer), the physician discusses various treatment options, including surgery, as well as risks of doing nothing or adopting a watchful waiting or active surveillance approach, rather than having surgery.

There are limitations associated with this process, both from the perspective of the physician and from the perspective of the patient. While the radiologist's report is certainly helpful, the physician must ultimately rely on her experience in formulating an assessment and recommendation for her patient. Furthermore, the patient must place a great deal of trust in his physician. The physician may show the patient his PET/SPECT images and may tell the patient a numerical risk associated with various treatment options or likelihood of a particular prognosis, but the patient may very well struggle to make sense of this information. Moreover, the patient's family will likely have questions, particularly if cancer is diagnosed but the patient opts not to have surgery. The patient and/or his family members may search online for supplemental information and may become misinformed about risks of the diagnosed condition. A difficult ordeal may become more traumatic.

Thus, there remains a need for systems and methods for improved analysis of medical imaging studies and communication of those results, diagnoses, prognoses, treatment recommendations, and associated risks to a patient.

SUMMARY OF THE INVENTION

Presented herein is a cloud-based platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and/or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and/or to track treatment and/or the progress of a disease.

For example, presented herein is a network-based (e.g., cloud-based) support platform allowing multiple users to store, access, analyze, and/or provide feedback regarding a given set of image data for a patient; platform supports software tools for automated analysis of targeted PET/SPECT/or other image(s), generation of radiologist reports, and application of machine learning algorithms to update process by which images are analyzed (e.g. updating segmentation and/or classification routines based on growing image database). In certain embodiments, the targeted PET/SPECT image(s) may be obtained using PyL™ and/or 1404 as the radiopharmaceutical(s). In certain embodiments, multiple (accredited) users can access the information, e.g., to weigh in on data interpretation.

Also presented herein is a software tool (e.g., mobile app) featuring a graphical user interface (GUI) element with controls for adjusting presentation of a 3D risk image corresponding to a patient organ (and/or other tissue) for comparison with reference images (e.g., for use in communication of results to patient as a decision-making support). For example, the tool may be supported by the network-based support platform above. This tool can provides an easily-understood, user-friendly, interactive, controllable pictorial display to communicate information about a patient's condition to the patient (and/or to the physician, or to the patient's family with the patient's permission). For example, a patient for whom a risk of cancer is detected can display a map indicating areas and/or degrees of risk and can compare this risk map with those of others for whom a given course of treatment is recommended. For instance, this tool can help a patient in his decision whether or not to have surgery (e.g., for a detected risk of prostate cancer). The patient can visually compare his risk map with a map representing a typical risk-level for which surgery would be recommended, below which it may be reasonable to opt not to have surgery and engage in watchful waiting or active surveillance. Thus, a low-risk patient who is told by his physician that he has a non-zero risk of cancer may find comfort in a visual, controllable comparison between his situation and that of someone (e.g., where the reference to which the patient's risk situation is compared can be tuned for age, weight, and/or other risk factors of the patient).

In one aspect, the invention is directed 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 functions (i) to (v) as follows: (i) receive and store 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] in a database [e.g., wherein the targeted PET/SPECT/gamma camera image(s) are obtained using one or more radiopharmaceuticals (e.g., [18F]DCFPyL and/or 1404 and/or a composition comprising technetium 99m, {e.g., technetium 99m methylenediphosphonate (99mTc MDP)}), and/or wherein the medical images are obtained using non-radioactive agents or no agents], each medical image associated with a particular patient; (ii) access one or more of the medical images and/or related data associated with a particular patient from the database upon user request (e.g., following automated verification that the user is properly credentialed for receiving the requested images and/or data) for transmission to the user for display on a user computing device; (iii) automatically analyze one or more of the medical images [e.g., to generate a risk index (e.g., BSI) and/or a risk map, e.g., a visual representation (e.g., 3D representation) of tissue (e.g., an organ or other part of the body) with graphical denotations (e.g., texture- or color-coding) marking regions of risk of current disease or risk of recurrence of disease, e.g., cancer, e.g., wherein the risk map is displayed as an overlay of the PET/SPECT/CT/MRI/US/combined/derived/fused image of the tissue, or is in place of the image of the tissue]; (iv) generate a radiologist report for a patient according to one or more of the medical images for the patient; and (v) apply a machine learning algorithm to update a process for automatically analyzing one or more of the medical images using accumulated image data in the database [e.g., wherein the automatic analysis of the one or more medical images in (iii) and/or (v) above comprises any one or more of (a) to (d) as follows: (a) automated fusion of the (e.g., PET, SPECT, CT, Mill, and/or US) image(s) of the tissue; (b) geographic identification of one or more organs, organ structures, sub-organs, organ regions, and/or other regions of the imaged tissue of the patient and production of a 3D image of the geographically identified tissue with PET, SPECT, CT, MM, and/or US data overlaid; (c) computation of risk information comprising one or more risk indices, a risk field, or a risk map using data from the database, the image(s) of the tissue, and/or the 3D image in (b); and (d) use of the risk information computed in (c) (e.g., and data from the database) to produce a 3D risk picture for the patient].

In certain embodiments, the medical images in the database comprise a series of medical images of a first patient taken over time (e.g., over the course of multiple visits to one or more doctors), and wherein the instructions cause the processor to determine a value of at least a first risk index for each medical image of the series, thereby tracking determined values of at least the first risk index for the first patient over time.

In certain embodiments, the medical images comprise a single-photon emission computerized tomography (SPECT) scan of a first patient obtained (e.g., to identify one or more hotspots) following administration to the first patient of an imaging agent comprising 1404 labeled with99mTc, and a computed tomography (CT) scan (e.g., to identify anatomical features) of the first patient, 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.

In certain embodiments, the medical images comprise a positron emission tomography (PET) scan of a first patient obtained (e.g., to identify one or more hotspots) following administration to the first patient of an imaging agent comprising [18F]DCFPyL (DCFPyL labeled with18F), 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.

In certain embodiments, the medical images comprise a whole-body scan (e.g., including anterior and posterior views) of a first patient made with a gamma camera following administration to the first patient of an imaging agent comprising technetium 99m methylenediphosphonate (99mTc MDP).

In certain embodiments, the medical images comprise a composite image of a first patient, the composite image comprising a CT scan overlaid with a nuclear medicine image (e.g., a SPECT scan; e.g., a PET scan) obtained at substantially the same time as the CT scan and following administration to the first patient of an imaging agent comprising a Prostate Specific Membrane Antigen (PSMA) binding agent comprising (e.g., labelled with) a radionuclide, and the instructions cause the processor to automatically analyze the composite image by: (a) using the composite image to geographically identify a 3D boundary for each of one or more regions of imaged tissue [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region of the patient), e.g., regions of interest] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundaries can be differentiated from each other); and (c) computing (i) a value of each of one or more risk indices and/or (ii) a risk map using the nuclear medicine image with the identified 3D boundaries of the one or more region(s).

In certain embodiments, the instructions cause the processor to, for at least one risk index of the one or more risk indices, compute the value of the risk index by: determining, for each of the one or more regions, a corresponding cancerous 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 within the nuclear medicine image, a plurality of hotspots within the 3D boundary of the region and computing a total number of and/or a total volume of the identified hotspots); and computing the value of the risk index based on the determined cancerous tissue levels within the one or more regions.

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

In certain embodiments, the imaging agent comprises a metal chelated to the PSMA binding agent, and wherein the metal is the radionuclide [e.g., wherein the metal is a radioisotope of technetium (Tc) (e.g., wherein the metal is technetium-99m (99mTc)); e.g., wherein the metal is a radioisotope of rhenium (Re) (e.g., wherein the metal is rhenium-188 (188Re); e.g., wherein the metal is rhenium-186 (186Re)); e.g., wherein the metal is a radioisotope of yttrium (Y) (e.g., wherein the metal is90Y); e.g., wherein the metal is a radioisotope of lutetium (Lu)(e.g., wherein the metal is177Lu); e.g., wherein the metal is a radioisotope of gallium (Ga) (e.g., wherein the metal is68Ga; e.g., wherein the metal is67Ga); e.g., wherein the metal is a radioisotope of indium (e.g.,111In); e.g., wherein the metal is a radioisotope of copper (Cu) (e.g., wherein the metal is67Cu)].

In certain embodiments, the imaging agent comprises99mTc-MIP-1404.

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

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

In certain embodiments, the imaging agent comprises [18F]DCFPyL (DCFPyL labeled with18F).

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

In certain embodiments, the medical images comprise a nuclear medicine image (e.g., a whole-body scan made with a gamma camera) of a first patient following administration to the first patient of an imaging agent comprising a radionuclide (e.g.,99mTc)(e.g., wherein the imaging agent comprises99mTc MDP), wherein the instructions cause the processor to automatically analyze the nuclear medicine image by: (a) geographically identifying a boundary (e.g., a 2D boundary; e.g., a 3D boundary) for each of one or more regions of imaged tissue [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region of the patient), e.g., regions of interest] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the boundaries can be differentiated from each other); and (c) computing (i) a value of 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 region(s).

In certain embodiments, the instructions cause the processor to, for at least one risk index of the one or more risk indices, compute the value of the risk index by: determining, for each of the one or more regions, a corresponding cancerous tissue level within the region based on intensity values of the nuclear medicine image within the boundary of the region (e.g., by identifying within the nuclear medicine image, a plurality of hotspots within the boundary of the region and computing a total number of and/or a total volume of the identified hotspots); and

computing the value of the risk index based on the determined cancerous tissue levels within the one or more regions.

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

In certain embodiments, the processor is a processor of one or more network or Internet host servers.

In another aspect, the invention is directed to a method comprising any one or more of (i) to (v) as follows: (i) receiving and storing, by a processor of a server computing device (e.g., received over a network from a client computing device) 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] in a database [e.g., wherein the targeted PET/SPECT/gamma camera image(s) are obtained using one or more radiopharmaceuticals, e.g., [18F]DCFPyL and/or 1404 and/or a composition comprising technetium 99m, e.g., technetium 99m methylenediphosphonate (99mTc MDP), and/or wherein the medical images are obtained using non-radioactive agents or no agents], each medical image associated with a particular patient; (ii) accessing, by the processor, one or more of the medical images and/or related data associated with a particular patient from the database upon user request (e.g., following automated verification that the user is properly credentialed for receiving the requested images and/or data) for transmission to the user 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, e.g., a visual representation (e.g., 3D representation) of tissue (e.g., an organ or other part of the body) with graphical denotations (e.g., texture- or color-coding) marking regions of risk of current disease or risk of recurrence of disease, e.g., cancer, e.g., wherein the risk map is displayed as an overlay of the PET/SPECT/CT/MM/US/combined/derived/fused image of the tissue, or is in place of the image of the tissue]; (iv) generating, by the processor, a radiologist report for a patient according to one or more of the medical images for the patient; and (v) applying, by the processor, a machine learning algorithm to update a process for automatically analyzing one or more of the medical images using accumulated image data in the database, [e.g., wherein the automatically analyzing the one or more medical images in (iii) and/or (v) above comprises any one or more of (a) to (d) as follows: (a) automatically fusing the (e.g., PET, SPECT, CT, MRI, and/or US) image(s) of the 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 production of a 3D image of the geographically identified tissue with PET, SPECT, CT, MRI, and/or US data overlaid; (c) computing risk information comprising one or more risk indices, a risk field, or a risk map using data from the database, the image(s) of the tissue, and/or the 3D image in (b); and (d) using the risk information computed in (c) (e.g., and data from the database) to produce a 3D risk picture for the patient].

In certain embodiments, the medical images in the database comprise a series of medical images of a first patient taken over time (e.g., over the course of multiple visits to one or more doctors), and wherein the method comprises determining a value of at least a first risk index for each medical image of the series, thereby tracking determined values of at least the first risk index over time.

In certain embodiments, the receiving and storing of the medical images comprises repeatedly receiving and storing, over time, a plurality of medical images of the first patient, each obtained at a different time (e.g., at a different visit to one or more doctors), to obtain the series of medical images of the first patient.

In certain embodiments, the medical images comprise a single-photon emission computerized tomography (SPECT) scan of a first patient obtained (e.g., to identify one or more hotspots) following administration to the first patient of an imaging agent comprising 1404 labeled with99mTc, and a computed tomography (CT) scan (e.g., to identify anatomical features) of the first patient, wherein the method comprises overlaying the SPECT scan with the CT scan to create a composite image (SPECT-CT) of the first patient.

In certain embodiments, the medical images comprises a positron emission tomography (PET) scan of a first patient obtained following administration to the first patient of an imaging agent comprising [18F]DCFPyL (DCFPyL labeled with18F), and a CT scan of the first patient, wherein the method comprises overlaying the PET scan with the CT scan to create a composite image (PET-CT) of the first patient.

In certain embodiments, the medical images comprise a whole-body scan (e.g., including anterior and posterior views) of a first patient made with a gamma camera following administration to the first patient of an imaging agent comprising technetium 99m methylenediphosphonate (99mTc MDP).

In certain embodiments, the medical images comprise a composite image of a first patient, the composite image comprising a CT scan overlaid with a nuclear medicine image (e.g., a SPECT scan; e.g., a PET scan) acquired at substantially the same time and following administration to the first patient of an imaging agent comprising a Prostate Specific Membrane Antigen (PSMA) binding agent comprising a radionuclide, wherein the method comprises automatically analyzing the composite image by: (a) using the composite image to geographically identify a 3D boundary for each of one or more regions of imaged tissue [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region of the patient), e.g., regions of interest] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundaries can be differentiated from each other); and (c) computing (i) a value of each of one or more risk indices and/or a (ii) risk map using the nuclear medicine image with the identified 3D boundaries of the one or more region(s).

In certain embodiments, step (c) comprises, for at least one risk index of the one or more risk indices, computing the value of the risk index by: determining, for each of the one or more regions, a corresponding cancerous 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 within the nuclear medicine image, a plurality of hotspots within the 3D boundary of the region and computing a total number of and/or a total volume of the identified hotspots); and computing the value of the risk index based on the determined cancerous tissue levels within the one or more regions.

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

In certain embodiments, the imaging agent comprises a metal chelated to the PSMA binding agent, and wherein the metal is the radionuclide [e.g., wherein the metal is a radioisotope of technetium (Tc) (e.g., wherein the metal is technetium-99m (99mTc)); e.g., wherein the metal is a radioisotope of rhenium (Re) (e.g., wherein the metal is rhenium-188 (188Re); e.g., wherein the metal is rhenium-186 (186Re)); e.g., wherein the metal is a radioisotope of yttrium (Y) (e.g., wherein the metal is90Y); e.g., wherein the metal is a radioisotope of lutetium (Lu)(e.g., wherein the metal is177Lu); e.g., wherein the metal is a radioisotope of gallium (Ga) (e.g., wherein the metal is68Ga; e.g., wherein the metal is67Ga); e.g., wherein the metal is a radioisotope of indium (e.g.,111In); e.g., wherein the metal is a radioisotope of copper (Cu) (e.g., wherein the metal is67Cu)].

In certain embodiments, the imaging agent comprises99mTc-MIP-1404.

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

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

In certain embodiments, the imaging agent comprises [18F]DCFPyL (DCFPyL labeled with18F).

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

In certain embodiments, the medical images comprise a nuclear medicine image (e.g., a whole-body scan made with a gamma camera) of a first patient obtained following administration to the first patient of an imaging agent comprising a radionuclide (e.g.,99mTc)(e.g., wherein the imaging agent comprises99mTc MDP), wherein the method comprises automatically analyzing the nuclear medicine image by: (a) geographically identifying a boundary (e.g., a 2D boundary; e.g., a 3D boundary) for each of one or more regions of imaged tissue [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region of the patient), e.g., regions of interest] within the nuclear medicine image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundaries can be differentiated from each other); and (c) computing (i) a value of 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 region(s).

In certain embodiments, step (c) comprises, for at least one risk index of the one or more risk indices, computing the value of the risk index by: determining, for each of the one or more regions, a corresponding cancerous tissue level within the region based on intensity values of the nuclear medicine image within the boundary of the region (e.g., by identifying within the nuclear medicine image, a plurality of hotspots within the 3D boundary of the region and computing a total number of and/or a total volume of the identified hotspots); and computing the value of the risk index based on the determined cancerous tissue levels within the one or more regions.

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

In certain embodiments, the processor is a processor of one or more network or Internet host servers.

In another aspect, the invention is directed to a system comprising: a processor (e.g., of a network or Internet host server or of 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 cause display of an interactive graphical user interface (GUI) element (e.g., cause display of the GUI element on a laptop computer or on a remote computing device, e.g., via a mobile app), the GUI element having user-selectable and/or user-adjustable graphical controls (e.g., slider bars, option buttons, text bars, drop-down boxes, windows, animations, and/or any other GUI widget) for selecting and/or adjusting a digital presentation of a 3D risk picture of a patient for comparison with reference images (e.g., for use in communication of results to patient as a decision-making support) caused to be displayed by the processor (e.g., wherein the reference images displayed to the user or presented for selection by the user are tunable according to one or more predetermined variables associated with the patient, e.g., patient age, time since diagnosis, prior treatment, and/or future treatment).

In certain embodiments, the interactive GUI element is produced from medical images of the patient [e.g., comprising 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 combinations, fusions, or derivatives of any of the above] and/or other images or information (e.g., other images received and stored in the database of the network-based decision support system of any of the aspects and/or embodiments described herein).

In another aspect, the invention is directed to a method for tracking prostate cancer progression and treatment efficacy over time, for one or more patient(s), the method comprising: (a) repeatedly receiving and storing in a database, over time, by a processor of a computing device (e.g., a server computing device), a plurality of medical images for each of the one or more patient(s) to obtain, for each of the one or more patient(s), a series of medical images taken over time (e.g., over the course of multiple visits to one or more doctors); (b) for each of the one or more patient(s), automatically analyzing, by the processor, the series of medical images for the patient to determine values of one or more risk indices [e.g., the values of the one or more risk indices corresponding to numeric values indicative of prostate cancer state and/or progression in the patient (e.g., numeric values identifying a particular cancer stage; e.g., numeric values corresponding to a determined overall survival rate for the patient)] for each medical image of the series, thereby tracking determined values of the one or more risk indices over a course prostate cancer progression and treatment for the patient; and (c) for each of the one or more patient(s), storing, by the processor, the determined values of the one or more risk indices for the patient for further processing and/or causing, by the processor, display of a graphical representation of the determined values of the one or more risk indices for the patient (e.g., causing display of a graph showing variation in the determined values of the one or more risk indices for the patient over time).

In certain embodiments, the series of medical images for a particular patient of the one or more patient(s) comprises: (i) a first image subseries comprising one or more medical images obtained using a first nuclear imaging modality (e.g., SPECT scans; e.g., composite SPECT-CT images) each following administration to the particular patient of a first radiopharmaceutical (e.g.,99mTc-MIP-1404) (e.g., wherein the first radiopharmaceutical facilitates imaging of localized disease, e.g., localized prostate cancer); and (ii) a second image subseries comprising one or more medical images obtained using a second nuclear imaging modality (e.g., PET scans; e.g., composite PET-CT images; e.g., whole-body scans) each following administration to the particular patient of a second radiopharmaceutical (e.g., [18F]DCFPyL; e.g.,99mTc MDP) (e.g., wherein the second radiopharmaceutical facilitates imaging of metastatic disease, e.g., metastatic prostate cancer), such that the values of the one or more risk indices determined in step (b) for the particular patient comprise a first subseries of values of a first risk index determined by automated analysis of the first image subseries and a second subseries of values of a second risk index determined by automated analysis of the second image subseries.

In certain embodiments, the medical images of first image subseries are obtained over a first period of time, when prostate cancer of the particular patient is localized (e.g., substantially localized to a prostate of the particular patient), and the medical images of the second image subseries are obtained over a second period of time, when prostate cancer of the particular patient is metastatic (e.g., having spread to regions of the patient outside of the prostate).

In certain embodiments, the first image subseries comprises one or more composite SPECT-CT image(s), each composite SPECT-CT image comprising a CT scan overlaid with a SPECT scan acquired at substantially the same time; the second image subseries comprises one or more composite PET-CT image(s), each composite PET-CT image comprising a CT scan overlaid with a PET scan acquired at substantially the same time; and step (b) comprises: automatically analyzing each of the one or more composite SPECT-CT images by: using the composite SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to a prostate of the patient) within the SPECT scan of the composite SPECT-CT image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundary of the prostate region can be differentiated from each other); and computing a value of the first risk index using the SPECT scan with the identified 3D boundary of the prostate region (e.g., computed based on a region of the SPECT scan corresponding to the identified 3D boundary of the prostate region); and automatically analyzing each of the one or more composite PET-CT images by: using the composite PET-CT image to geographically identify a 3D boundary of one or more metastatic regions within the PET scan of the composite PET-CT image, the one or metastatic regions including regions corresponding to patient tissue locations outside of the prostate [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region corresponding to the patient's skeleton), e.g., regions of interest] (e.g., such that portions of the PET imaging scan falling within and/or outside of the 3D boundaries of the one or more metastatic region(s) can be differentiated from each other); and computing a value of the second risk index using the PET scan with the identified 3D boundaries of the one or more metastatic region(s).

In certain embodiments, the first image subseries comprises one or more composite SPECT-CT image(s), each composite SPECT-CT image comprising a CT scan overlaid with a SPECT scan acquired at substantially the same time; the second image subseries comprises one or more whole-body scan(s); and step (b) comprises: automatically analyzing each of the one or more composite SPECT-CT images by: using the composite SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to a prostate of the patient) within the SPECT scan of the composite SPECT-CT image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundary of the prostate region can be differentiated from each other); and computing a value of the first risk index using the SPECT scan with the identified 3D boundary of the prostate region (e.g., computed based on a region of the SPECT scan corresponding to the identified 3D boundary of the prostate region); and automatically analyzing each of the one or more whole-body scan(s) by: geographically identifying a boundary of one or more metastatic regions within the whole-body scan, the one or metastatic regions including regions corresponding to patient tissue locations outside of the prostate [(e.g., one or more particular bones; e.g., a skeletal region corresponding to the patient's skeleton), e.g., regions of interest] (e.g., such that portions of the whole-body scan falling within and/or outside of the boundaries of the one or more metastatic region(s) can be differentiated from each other); and computing a value of the second risk index using the PET scan with the identified 3D boundaries of the one or more metastatic region(s).

In another aspect, the invention is directed to a system for tracking prostate cancer progression and treatment efficacy over time, for one or more patient(s), 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, over time, a plurality of medical images for each of the one or more patient(s) to obtain, for each of the one or more patient(s), a series of medical images taken over time (e.g., over the course of multiple visits to one or more doctors); (b) for each of the one or more patient(s), automatically analyze the series of medical images for the patient to determine values of one or more risk indices [e.g., the values of the one or more risk indices corresponding to numeric values indicative of prostate cancer state and/or progression in the patient (e.g., numeric values identifying a particular cancer stage; e.g., numeric values corresponding to a determined overall survival rate for the patient)] for each medical image of the series, thereby tracking determined values of the one or more risk indices over a course prostate cancer progression and treatment for the patient; and (c) for each of the one or more patient(s), store the determined values of the one or more risk indices for the patient for further processing and/or cause display of a graphical representation of the determined values of the one or more risk indices for the patient (e.g., causing display of a graph showing variation in the determined values of the one or more risk indices for the patient over time).

In certain embodiments, the series of medical images for a particular patient of the one or more patient(s) comprises: (i) a first image subseries comprising one or more medical images obtained using a first nuclear imaging modality (e.g., SPECT scans; e.g., composite SPECT-CT images) each following administration to the particular patient of a first radiopharmaceutical (e.g.,99mTc-MIP-1404) (e.g., wherein the first radiopharmaceutical facilitates imaging of localized disease, e.g., localized prostate cancer); and (ii) a second image subseries comprising one or more medical images obtained using a second nuclear imaging modality (e.g., PET scans; e.g., composite PET-CT images; e.g., whole-body scans) each following administration to the particular patient of a second radiopharmaceutical (e.g., [18F]DCFPyL; e.g.,99mTc MDP) (e.g., wherein the second radiopharmaceutical facilitates imaging of metastatic disease, e.g., metastatic prostate cancer), such that the values of the one or more risk indices determined in step (b) for the particular patient comprise a first subseries of values of a first risk index determined by automated analysis of the first image subseries and a second subseries of values of a second risk index determined by automated analysis of the second image subseries.

In certain embodiments, the medical images of first image subseries are obtained over a first period of time, when prostate cancer of the particular patient is localized (e.g., substantially localized to a prostate of the particular patient), and the medical images of the second image subseries are obtained over a second period of time, when prostate cancer of the particular patient is metastatic (e.g., having spread to regions of the patient outside of the prostate).

In certain embodiments, the first image subseries comprises one or more composite SPECT-CT image(s), each composite SPECT-CT image comprising a CT scan overlaid with a SPECT scan acquired at substantially the same time; the second image subseries comprises one or more composite PET-CT image(s), each composite PET-CT image comprising a CT scan overlaid with a PET scan acquired at substantially the same time; and the instructions cause the processor to, at step (b): automatically analyze each of the one or more composite SPECT-CT images by: using the composite SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to a prostate of the patient) within the SPECT scan of the composite SPECT-CT image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundary of the prostate region can be differentiated from each other); and computing a value of the first risk index using the SPECT scan with the identified 3D boundary of the prostate region (e.g., computed based on a region of the SPECT scan corresponding to the identified 3D boundary of the prostate region); and automatically analyze each of the one or more composite PET-CT images by: using the composite PET-CT image to geographically identify a 3D boundary of one or more metastatic regions within the PET scan of the composite PET-CT image, the one or metastatic regions including regions corresponding to patient tissue locations outside of the prostate [e.g., organs (e.g., a prostate; e.g., a liver; e.g., lungs or a lung; e.g., lymph nodes), organ structures, sub-organs, organ regions, and/or other regions (e.g., one or more particular bones; e.g., a skeletal region corresponding to the patient's skeleton), e.g., regions of interest] (e.g., such that portions of the PET imaging scan falling within and/or outside of the 3D boundaries of the one or more metastatic region(s) can be differentiated from each other); and computing a value of the second risk index using the PET scan with the identified 3D boundaries of the one or more metastatic region(s).

In certain embodiments, the first image subseries comprises one or more composite SPECT-CT image(s), each composite SPECT-CT image comprising a CT scan overlaid with a SPECT scan acquired at substantially the same time; the second image subseries comprises one or more whole-body scan(s); and the instructions cause the processor to, at step (b): automatically analyze each of the one or more composite SPECT-CT images by: using the composite SPECT-CT image to geographically identify a 3D boundary of a prostate region (e.g., corresponding to a prostate of the patient) within the SPECT scan of the composite SPECT-CT image (e.g., such that portions of the nuclear medicine image falling within and/or outside of the 3D boundary of the prostate region can be differentiated from each other); and computing a value of the first risk index using the SPECT scan with the identified 3D boundary of the prostate region (e.g., computed based on a region of the SPECT scan corresponding to the identified 3D boundary of the prostate region); and automatically analyze each of the one or more whole-body scan(s) by: geographically identifying a boundary of one or more metastatic regions within the whole-body scan, the one or metastatic regions including regions corresponding to patient tissue locations outside of the prostate [(e.g., one or more particular bones; e.g., a skeletal region corresponding to the patient's skeleton), e.g., regions of interest] (e.g., such that portions of the whole-body scan falling within and/or outside of the boundaries of the one or more metastatic region(s) can be differentiated from each other); and computing a value of the second risk index using the PET scan with the identified 3D boundaries of the one or more metastatic region(s).

Embodiments described with respect to one aspect of the invention may be, applied to another aspect of the invention (e.g., features of embodiments described with respect to one independent claim, e.g., a method claim, are contemplated to be applicable to other embodiments of other independent claims, e.g., a system claim, and vice versa).

