US 11,055,844 B2
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)
Anant Madabhushi, Shaker Heights, OH (US); Xiangxue Wang, Cleveland Heights, OH (US); Cristian Barrera, Cleveland, OH (US); and Vamsidhar Velcheti, Pepper Pike, OH (US)
Assigned to Case Western Reserve University, Cleveland, OH (US); and The Cleveland Clinic Foundation, Cleveland, OH (US)
Filed by Case Western Reserve University, Cleveland, OH (US)
Filed on Feb. 21, 2019, as Appl. No. 16/281,324.
Claims priority of provisional application 62/633,342, filed on Feb. 21, 2018.
Prior Publication US 2019/0259154 A1, Aug. 22, 2019
Int. Cl. G06K 9/00 (2006.01); G06T 7/00 (2017.01); G06K 9/46 (2006.01); G06T 7/40 (2017.01); G06T 7/62 (2017.01); G06K 9/62 (2006.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06T 7/0012 (2013.01) [G06K 9/0014 (2013.01); G06K 9/00147 (2013.01); G06K 9/469 (2013.01); G06K 9/6257 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); G06T 7/62 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06K 2209/05 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30096 (2013.01)] 20 Claims
OG exemplary drawing
1. A non-transitory computer-readable storage device storing computer-executable instructions that, in response to execution, cause a processor to perform operations comprising:
accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology, where the ROT includes a plurality of cellular nuclei, where the digitized image includes a plurality of pixels, a pixel having an intensity;
segmenting a plurality of cellular nuclei represented in the digitized image;
extracting a set of nuclear radiomic features from the plurality of segmented cellular nuclei;
generating at least one nuclear cell graph (CG) based on the plurality of segmented cellular nuclei;
computing a set of CG features based on the at least one nuclear CG;
providing the set of nuclear radiomic features and the set of CG features to a machine learning classifier;
receiving, from the machine learning classifier, a probability that the ROT will respond to immunotherapy, where the machine learning classifier computes the probability based, at least in part, on the set of nuclear radiomic features and the set of CG features;
generating a classification of the ROT as a responder or non-responder based on the probability; and
displaying the classification.