Abstract: Embodiments discussed herein facilitate determination of one of a probability of prostate cancer recurrence-free survival or a risk factor associated with prostate cancer based on intra-tumor stromal morphology. Example embodiments can perform operations comprising: accessing a digitized histological image of a prostate of a patient, wherein the histological image comprises a region of interest associated with prostate cancer; identifying nuclei of intra-tumoral stromal cells within the region of interest; extracting, for the region of interest of the digitized histological image, one or more features describing the structure of the intra-tumoral stromal cells; and generating, via a model based at least on the one or more features, one of a probability of prostate cancer recurrence-free survival or a risk score associated with prostate cancer for the patient based at least on the extracted one or more features.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Embodiments discussed herein facilitate determination of a likelihood of biochemical recurrence (BCR) of cancer (e.g., prostate cancer, etc.). One example embodiment is a method, comprising: accessing at least a portion of a digitized stained histology slide comprising a tumor; automatically segmenting, via a trained deep learning (DL) model, cribriform morphology in connection with the tumor on the at least the portion of the digitized stained histology slide; determining a cribriform-to-tumor area ratio (CAR) based at least in part on an area of the segmented cribriform morphology and an area of the tumor; and determining a risk of biochemical recurrence (BCR) of a cancer associated with the tumor based at least in part on the CAR.

Abstract: Embodiments discussed herein facilitate determination of risk of relapse of AML post-transplant. One example embodiment is a method, comprising: accessing a digital whole slide image (WSI) comprising a post-transplant bone marrow aspirate from a patient that has acute myeloid leukemia (AML); segmenting one or more myeloblasts on the digital WSI; extracting one or more features from the segmented one or more myeloblasts; providing the one or more features extracted from the segmented one or more myeloblasts to a trained machine learning model; and receiving, from the trained machine learning model, an indication of a risk of relapse of the AML.

Abstract: Embodiments discussed herein facilitate determination of one of a probability of prostate cancer recurrence-free survival or a risk factor associated with prostate cancer based on intra-tumor stromal morphology. Example embodiments can perform operations comprising: accessing a digitized histological image of a prostate of a patient, wherein the histological image comprises a region of interest associated with prostate cancer; identifying nuclei of intra-tumoral stromal cells within the region of interest; extracting, for the region of interest of the digitized histological image, one or more features describing the structure of the intra-tumoral stromal cells; and generating, via a model based at least on the one or more features, one of a probability of prostate cancer recurrence-free survival or a risk score associated with prostate cancer for the patient based at least on the extracted one or more features.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Embodiments include controlling a processor to perform operations for predicting biochemical recurrence (BCR) in prostate cancer (PCa), including accessing a first digitized pathology slide having a first stain channel of a region of tissue demonstrating PCa; accessing a second digitized pathology slide having a second, different stain channel of the region of tissue; extracting morphology features from the first stain channel; extracting stain intensity features from the second stain channel, where a stain intensity feature quantifies an amount of a molecular biomarker present in a cellular nucleus; controlling a first machine learning classifier to generate a first probability of BCR based on the morphology features; controlling a second machine learning classifier to generate a second, different probability of BCR based on the stain intensity features; computing an aggregate probability of BCR based on the first probability and the second probability; and displaying the aggregate probability.

Abstract: Embodiments include controlling a processor to perform operations for predicting biochemical recurrence (BCR) in prostate cancer (PCa), including accessing a first digitized pathology slide having a first stain channel of a region of tissue demonstrating PCa; accessing a second digitized pathology slide having a second, different stain channel of the region of tissue; extracting morphology features from the first stain channel; extracting stain intensity features from the second stain channel, where a stain intensity feature quantifies an amount of a molecular biomarker present in a cellular nucleus; controlling a first machine learning classifier to generate a first probability of BCR based on the morphology features; controlling a second machine learning classifier to generate a second, different probability of BCR based on the stain intensity features; computing an aggregate probability of BCR based on the first probability and the second probability; and displaying the aggregate probability.