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:
Nov. 24, 2020

Filed:

Sep. 14, 2018
Applicant:

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

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Xiangxue Wang, Cleveland Heights, OH (US);

Pranjal Vaidya, Cleveland, OH (US);

Vamsidhar Velcheti, Pepper Pike, OH (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/10 (2017.01); G06F 19/00 (2018.01); G06T 7/00 (2017.01); G06K 9/66 (2006.01); G06K 9/62 (2006.01); G06K 9/32 (2006.01); G06K 9/46 (2006.01); G06N 3/04 (2006.01); G06K 9/00 (2006.01); G16H 20/10 (2018.01); G16Z 99/00 (2019.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 20/40 (2018.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06F 19/00 (2013.01); G06K 9/00127 (2013.01); G06K 9/3233 (2013.01); G06K 9/4619 (2013.01); G06K 9/4628 (2013.01); G06K 9/6234 (2013.01); G06K 9/6273 (2013.01); G06K 9/6277 (2013.01); G06K 9/6286 (2013.01); G06K 9/66 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/0016 (2013.01); G06T 7/10 (2017.01); G16H 20/10 (2018.01); G16H 20/40 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16Z 99/00 (2019.02); G06K 9/6218 (2013.01); G06T 2207/10004 (2013.01); G06T 2207/20036 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30096 (2013.01); G16H 50/20 (2018.01);
Abstract

Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.


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