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. 08, 2022

Filed:

Dec. 30, 2019
Applicant:

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

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Patrick Leo, Honeoye Falls, NY (US);

Andrew Janowczyk, East Meadow, NY (US);

Kaustav Bera, Cleveland, OH (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01); G16H 70/20 (2018.01); A61B 34/10 (2016.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 34/10 (2016.02); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G16H 70/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30081 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.


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