Methods for predicting clinical implications in breast cancer patients based on tumor infiltrating leukocytes fractal geometry

A method for selecting a breast cancer patient for treatment with a chemotherapeutic agent comprising

• applying, by a computing system having one or more processors, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the breast cancer patient;
• computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample;
• and
• administering a chemotherapeutic agent to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient falls below a predetermined threshold.

A method for selecting a breast cancer patient for surgery, radiation therapy, or immunotherapy comprising

• applying, by a computing system having one or more processors, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the breast cancer patient;
• computing a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample;
• and
• administering surgery, radiation therapy, or immunotherapy to the breast cancer patient, wherein the fractal-geometric metric of the breast cancer patient is at or above a predetermined threshold.

A computer system for predicting prognosis in a breast cancer patient, the computing system comprising a processor and a memory with instructions which, when executed by the processor, cause the processor to:

• apply, by a computing system having one or more processors, a trained model to detect and quantify (a) cancer cells and (b) tumor-infiltrating leukocytes (TILs) in a biomedical image of a resected tumor sample including a tumor stromal region, wherein the resected tumor sample is obtained from the patient;
• compute a fractal-geometric metric based on anatomic distribution of the TILs in the biomedical image of the resected tumor sample;
• and
• determine that the breast cancer patient has a favorable prognosis when the fractal-geometric metric falls below a predetermined threshold, or determining that the breast cancer patient has a negative prognosis when the fractal-geometric metric is at or above a predetermined threshold.