Systems and methods for assessing disease burden and progression

A method for automated analysis of a time series of medical images of a subject, the method comprising:

• (a) receiving and/or accessing, by a processor of a computing device, the time series of medical images of the subject;
• and
• (b) identifying, by the processor, a plurality of hotspots within each of the medical images and determining, by the processor, one, two, or all three of (i), (ii), and (iii) as follows:
• (i) a change in the number of identified lesions (ii) a change in an overall volume of identified lesions, and (iii) a change in PSMA weighted total volume.

A method for analyzing a plurality of medical images of a subject, the method comprising:

• (a) receiving and/or accessing, by a processor of a computing device, the plurality of medical images of the subject and obtaining, by the processor, a plurality of 3D hotspot maps, each corresponding to a particular medical image and identifying one or more hotspots within the particular medical image;
• (b) for each particular one of the plurality of medical images, determining, by the processor, using a machine learning module, a corresponding 3D anatomical segmentation map that identifies a set of organ regions within the particular medical image, thereby generating a plurality of 3D anatomical segmentation maps;
• (c) determining, by the processor, using (i) the plurality of 3D hotspot maps and (ii) the plurality of 3D anatomical segmentation maps, an identification of one or more lesion correspondences, each identifying two or more corresponding hotspots within different medical images and determined to represent a same underlying physical lesion within the subject;
• and
• (d) determining, by the processor, based on the plurality of 3D hotspot maps and the identification of the one or more lesion correspondences, values of one or more metrics.

A method for analyzing a plurality of medical images of a subject, the method comprising:

• (a) obtaining, by a processor of a computing device, a first 3D hotspot map for the subject;
• (b) obtaining, by the processor, a first 3D anatomical segmentation map associated with the first 3D hotspot map;
• (c) obtaining, by the processor, a second 3D hotspot map for the subject;
• (d) obtaining, by the processor, a second 3D anatomical segmentation map associated with the second 3D hotspot map;
• (e) determining, by the processor, a registration field using/based on the first 3D anatomical segmentation map and the second 3D anatomical segmentation map;
• (f) registering, by the processor, the first 3D hotspot map and the second 3D hotspot map, using the determined registration field, thereby generating a co-registered pair of 3D hotspot maps;
• (g) determining, by the processor, an identification one or more lesion correspondences using the co-registered pair of 3D hotspot maps;
• and
• (h) storing and/or providing, by the processor, the identification of the one or more lesion correspondences for display and/or further processing.

A method for analyzing a plurality of medical images of a subject, the method comprising:

• (a) receiving and/or accessing, by a processor of a computing device, the plurality of medical images of the subject;
• (b) for each particular one (medical image) of the plurality of medical images, determining, by the processor, using a machine learning module, a corresponding 3D anatomical segmentation map that identifies a set of organ regions within the particular medical image, thereby generating a plurality of 3D anatomical segmentation maps;
• (c) determining, by the processor, using the plurality of 3D anatomical segmentation maps, one or more registration fields and applying the one or more registration fields to register the plurality of medical images, thereby creating a plurality of registered medical images;
• (d) determining, by the processor, for each particular one of the plurality of registered medical images, a corresponding registered 3D hotspot map identifying one or more hotspots within the particular registered medical image, thereby creating a plurality of registered 3D hotspot maps;
• (e) determining, by the processor, using the plurality of 3D registered hotspot maps, an identification of one or more lesion correspondences, each identifying two or more corresponding hotspots within different medical images and determined to represent a same underlying physical lesion within the subject;
• and
• (f) determining, by the processor, based on the plurality of 3D hotspot maps and the identification of the one or more lesion correspondences, values of one or more metrics.

A method for analyzing a plurality of medical images of a subject, the method comprising:

• (a) obtaining, by a processor of a computing device, a first 3D anatomical image and a first 3D functional image of the subject;
• (b) obtaining, by the processor, a second 3D anatomical image and a second 3D functional image of the subject;
• (c) obtaining, by the processor, a first 3D anatomical segmentation map based on the first 3D anatomical image;
• (d) obtaining, by the processor, a second 3D anatomical segmentation map based on the second 3D anatomical image;
• (e) determining, by the processor, a registration field using/based on the first 3D anatomical segmentation map and the second 3D anatomical segmentation map;
• (f) registering, by the processor, the second 3D functional image to first 3D functional image using the registration field, thereby generating a registered version of the second 3D functional image;
• (g) obtaining, by the processor a first 3D hotspot map associated with the first functional image;
• (h) determining, by the processor, a second 3D hotspot map using the registered version of the second 3D functional image, the second 3D hotspot map thereby being registered with the first 3D hotspot map;
• (i) determining, by the processor, an identification one or more lesion correspondences using the first 3D hotspot map and the second 3D hotspot map registered thereto;
• and
• (j) storing and/or providing, by the processor, the identification of the one or more lesion correspondences for display and/or further processing.

A method of automated or semi-automated whole-body evaluation of a subject with metastatic prostate cancer to assess disease progression and/or treatment efficacy, the method comprising:

• (a) receiving, by a processor of a computing device, a first prostate-specific membrane antigen (PSMA) targeting positron emission tomography (PET) image (the first PSMA-PET image) of the subject and a first 3D anatomical image of the subject, wherein the first 3D anatomical image of the subject is obtained simultaneously with or immediately subsequent to or immediately prior to the first PSMA PET image such that the first 3D anatomical image and the first PSMA PET image correspond to a first date, and wherein the images depict a large enough area of the subject's body to cover regions of the body to which the metastatic prostate cancer has spread;
• (b) receiving, by the processor, a second PSMA-PET image of the subject and a second 3D anatomical image of the subject, both obtained on a second date subsequent to the first date;
• (c) automatically determining, by the processor, a registration field using landmarks automatically identified within the first and second 3D anatomical images, and using, by the processor, the determined registration field to align the first and second PSMA-PET images;
• and
• (d) using the thusly aligned first and second PSMA-PET images to automatically detect, by the processor, a change in the disease from the first date to the second date.