Population-specific prediction of prostate cancer recurrence based on stromal morphology features

A method of assessing prostate cancer for a patient, comprising:

• accessing an image of a prostate of a patient, wherein the image comprises a region of interest associated with prostate cancer;
• generating a first probability map that indicates a likelihood that each pixel in the image is part of a nucleus;
• determining nuclear coordinates using the first probably map;
• generating a second probability map representing a likelihood that each pixel in the image is part of a stromal compartment;
• combining information from the second probability map with the nuclear coordinates to determine coordinates of nuclei of intra-tumoral stromal cells;
• identifying boundaries of the nuclei of the intra-tumoral stromal cells and the stromal compartment within the region of interest;
• extracting, from the region of interest of the image, one or more features describing a shape, an orientation, and an arrangement of nuclei within the intra-tumoral stromal cells, wherein the boundaries of the nuclei of the intra-tumoral stromal cells and the stromal compartment are used in extracting the one or more features;
• and
• generating, via a model based at least on the one or more features, a risk score associated with prostate cancer for the patient.

An apparatus for assessing prostate cancer within a patient, comprising:

• a memory configured to store an image of a prostate of a patient, wherein the image comprises a region of interest associated with prostate cancer;
• a one or more circuits configured to:
• generate a first probability map that indicates a likelihood that each pixel in the image is part of a nucleus;
• determine nuclear coordinates using the first probably map;
• generate a second probability map representing a likelihood that each pixel in the image is part of a stromal compartment;
• combine information from the second probability map with the nuclear coordinates to determine coordinates of a nuclei of intra-tumoral stromal cells;
• identify boundaries of the nuclei of the intra-tumoral stromal cells and the stromal compartment within the region of interest using convolutional neural network to represent a likelihood each pixel in the image is part of the stromal compartment;
• extract, from the region of interest of the image, one or more features describing a structure of the intra-tumoral stromal cells, wherein the boundaries of the nuclei of the intra-tumoral stromal cells and the stromal compartment are used in extracting the one or more features;
• and
• generate, via a model based at least on the one or more features, a risk score associated with prostate cancer within the patient.