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:
Jan. 26, 2021

Filed:

Feb. 06, 2019
Applicant:

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

Inventors:

Anant Madabhushi, Shaker Heights, OH (US);

Nathaniel Braman, Cleveland, OH (US);

Andrew Janowczyk, East Meadow, NY (US);

Kavya Ravichandran, Cleveland, OH (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); G06T 5/50 (2006.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G16H 30/40 (2018.01); G06N 3/08 (2006.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06K 9/46 (2013.01); G06K 9/4642 (2013.01); G06K 9/628 (2013.01); G06K 9/6228 (2013.01); G06K 9/6267 (2013.01); G06N 3/08 (2013.01); G06N 7/005 (2013.01); G06T 5/50 (2013.01); G16H 30/40 (2018.01); G06K 2209/05 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Embodiments access a pre-neoadjuvant chemotherapy (NAC) radiological image of a region of tissue demonstrating breast cancer (BCa), the region of tissue including a tumoral region, the image having a plurality of pixels; extract a set of patches from the tumoral region; provide the set of patches to a convolutional neural network (CNN) configured to discriminate tissue that will experience pathological complete response (pCR) post-NAC from tissue that will not; receive, from the CNN, a pixel-level localized patch probability of pCR; compute a distribution of predictions across analyzed patches based on the pixel-level localized patch probability; classify the region of tissue as a responder or non-responder based on the distribution of predictions, and display the classification. Embodiments may further generate a probability mask based on the pixel-level localized patch probability; and generate a heatmap of likelihood of response to NAC based on the probability mask and the pre-NAC radiological image.


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