On July 6, 2021 and Aug 31, 2021, patents titled “Predicting response to immunotherapy using computer extracted features of cancer nuclei from hematoxylin and eosin (HandE) stained images of non-small cell lung cancer (NSCLC)” and “Sequential integration of adversarial networks with handcrafted features (SANwicH): identifying sites of prognostic significance for predicting cancer recurrence” were awarded to CCIPD.
Cheers to the inventors on both patents, Anant Madabhushi, PhD, and Xiangxue Wang, PhD, and to the inventors on the second patent, Cristian Barrera and Vamsidhar Velcheti, MD.
Read details about the patents below:
United States Serial Number (USSN): 11,055,844, July 6, 2021.
Inventors: Madabhushi; Anant, Wang; Xiangxue, Barrera; Cristian, Velcheti; Vamsidhar
Abstract: Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.
United States Serial Number (USSN): 11,107,583, August 31, 2021.
Inventors: Madabhushi; Anant, Wang; Xiangxue
Abstract: Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.
