US 12,465,311 B2
Systems and methods for automated diagnosis and prognosis support using radiomics
Haruka Itakura, Stanford, CA (US)
Assigned to The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed by The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed on May 23, 2023, as Appl. No. 18/322,477.
Claims priority of provisional application 63/365,194, filed on May 23, 2022.
Prior Publication US 2023/0404509 A1, Dec. 21, 2023
Int. Cl. A61B 6/00 (2024.01); A61B 6/50 (2024.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 20/00 (2018.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 30/20 (2018.01)
CPC A61B 6/502 (2013.01) [A61B 6/5205 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 20/00 (2018.01); G16H 30/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30068 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01); G16H 30/20 (2018.01)] 18 Claims
OG exemplary drawing
 
1. A method for non-invasively identifying triple negative breast cancer, comprising:
obtaining a magnetic resonance imaging scan of a patient's breast;
generating a tumor segmentation mask for the scan of the patient using a first machine learning model;
extracting a plurality of radiomic features from the segmented scan of the patient;
providing the plurality of radiomic features to a second machine learning model; and
obtaining, from the second machine learning model, a likelihood that the patient has triple negative breast cancer.