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:
Oct. 11, 2022

Filed:

Mar. 22, 2019
Applicant:

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

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Pranjal Vaidya, Cleveland, OH (US);

Kaustav Bera, Cleveland, OH (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2006.01); G06T 7/00 (2017.01); G06K 9/62 (2022.01); A61B 6/03 (2006.01); G16H 30/40 (2018.01); G06N 20/00 (2019.01); G06T 7/12 (2017.01);
U.S. Cl.
CPC ...
A61B 6/5217 (2013.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); G06K 9/6228 (2013.01); G06K 9/6234 (2013.01); G06K 9/6262 (2013.01); G06K 9/6277 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/12 (2017.01); G16H 30/40 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30064 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.


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