Two patents were recently awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD): “Predicting biochemical recurrence and metastasis with computer extracted features from nuclei of tumor and benign regions” and "Predicting prostate cancer biochemical recurrence using combined nuclear NF-KB/P65 localization and gland morphology". Congratulations to Anant Madabhushi, PhD, F. Alex Nason Professor II, biomedical engineering and Director of CCIPD; Andrew Janowczyk, PhD, Research Assistant Professor, biomedical engineering and CCIPD, Patrick Leo, PhD student in CCIPD; George Lee, PhD, alumnus of CCIPD; and collaborators Anna Gawlik and Sanjay Gupta, PhD. Read details about the patents below.
United States Serial Number (USSN): 10,776,607, September 15th, 2020.
Inventors: Madabhushi; Anant (Shaker Heights, OH), Gawlik; Anna (St. Charles, IL), Lee; George (Parlin, NJ)
Abstract: Embodiments predict biochemical recurrence (BCR) or metastasis by accessing a set of images of a region of tissue demonstrating cancerous pathology, including a tumor region and a tumor adjacent benign (TAB) region, the set of images including a first stain type image, and a second stain type image; segmenting cellular nuclei represented in the first and second image; generating a combined feature set by extracting at least one feature from each of a tumor region and TAB region represented in the first image, and a tumor region and TAB region represented in the second image, providing the combined feature set to a machine learning classifier; receiving, from the classifier, a probability that the region of tissue will experience BCR or metastasis; and generating a classification of the region of tissue as likely to experience BCR or metastasis, or unlikely to experience BCR or metastasis.
United States Serial Number (USSN): 10,769,783, September 8, 2020.
Inventors: Madabhushi; Anant (Shaker Heights, OH), Leo; Patrick (Honoeye Falls, NY), Janowczyk; Andrew (East Meadow, NY), Gupta; Sanjay (Mayfield Heights, OH)
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.
