Two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD) and their collaborators: “Predicting response to anti-vascular endothelial growth factor therapy with computer-extracted morphology and spatial arrangement features of leakage patterns on baseline fluorescein angiography in diabetic macular edema” and “Methods and apparatus for predicting benefit from immunotherapy using tumoral and peritumoral radiomic features”. CCIPD inventors on these patents are Anant Madabhushi, PhD, Donnell Institute Professor and Director of CCIPD, and CCIPD alumni Niha Beig, PhD, Prateek Prasanna, PhD, and Mahdi Orooji, PhD.
Read details about the patents below.
United States Serial Number (USSN): 10,943,348, March 9th, 2021.
Inventors: Madabhushi; Anant, Prasanna; Prateek, Ehlers; Justis, Srivastava; Sunil
Abstract: Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.
United States Serial Number (USSN): 10,950,351, March 16, 2021.
Inventors: Madabhushi; Anant, Orooji; Mahdi, Beig; Niha, Velcheti; Vamsidhar
Abstract: Methods, apparatus, and other embodiments predict response to immunotherapy from computed tomography (CT) images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a CT image of a region of tissue demonstrating cancerous pathology, a tumoral definition circuit that generates a tumoral surface boundary that defines a tumoral volume, a peritumoral segmentation circuit that generates a peritumoral region based on the tumoral surface boundary, and that segments the peritumoral region into a plurality of annular bands, a radiomics circuit that extracts a set of discriminative features from the tumoral volume and at least one of the plurality of annular bands, and a classification circuit that classifies the ROI as a responder or a non-responder, based, at least in part, on the set of discriminative features.
