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:
Sep. 24, 2024

Filed:

Mar. 02, 2022
Applicants:

Ventana Medical Systems, Inc., Tucson, AZ (US);

The Board of Trustees of the Leland Stanford Junior University, Palo Alto, CA (US);

Inventors:

Michael Barnes, San Francisco, CA (US);

Srinivas Chukka, San Jose, CA (US);

David Knowles, Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); C12Q 1/6886 (2018.01); G06F 18/211 (2023.01); G06F 18/25 (2023.01); G06V 10/771 (2022.01); G06V 10/80 (2022.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); C12Q 1/6886 (2013.01); G06F 18/211 (2023.01); G06F 18/253 (2023.01); G06V 10/771 (2022.01); G06V 10/806 (2022.01); G06V 20/698 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30072 (2013.01); G06T 2207/30096 (2013.01);
Abstract

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.


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