In the last month, two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD): “Predicting recurrence in early stage non-small cell lung cancer (NSCLC) using spatial arrangement of clusters of tumor infiltrating lymphocytes and cancer nuclei”, and “Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-VEGF therapy in diabetic macular edema”.
Congratulations to Anant Madabhushi, PhD, Donnell Institute Professor, biomedical engineering, and director of CCIPD; CCIPD alumni Xiangxue Wang, PhD, and Prateek Prasanna, PhD; and collaborators Vamsidhar Velcheti, MD; Justis Elhers, MD; and Sunil Srivastava, MD.
Read details about the patents below.
United States Serial Number (USSN): 10,956,795, March 23, 2021.
Inventors: Madabhushi; Anant, Wang; Xiangxue, Velcheti; Vamsidhar
Abstract: Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
United States Serial Number (USSN): 10,970,838, April 6, 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 or RVO 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 vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.
