The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 12, 2026

Filed:

Jul. 05, 2023
Applicants:

Case Western Reserve University, Cleveland, OH (US);

The Cleveland Clinic Foundation, Cleveland, OH (US);

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Xiangxue Wang, Cleveland Heights, OH (US);

Cristian Barrera, Cleveland, OH (US);

Vamsidhar Velcheti, Pepper Pike, OH (US);

Assignees:

Case Western Reserve University, Cleveland, OH (US);

The Cleveland Clinic Foundation, Cleveland, OH (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/12 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06T 7/11 (2017.01); G06T 7/40 (2017.01); G06T 7/62 (2017.01); G06V 10/26 (2022.01); G06V 20/69 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/12 (2017.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/24 (2023.01); G06T 7/11 (2017.01); G06T 7/40 (2013.01); G06T 7/62 (2017.01); G06V 10/26 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01);
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 nuclearradiomicfeatures 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 nuclearradiomicfeatures 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 nuclearradiomicfeatures 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.


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