US 12,094,571 B2
Systems and methods for predictive molecular biomarker identification and quantification from morphology changes in histopathology tissue
Parag Jain, Palo Alto, CA (US); Rajat Roy, Saratoga, CA (US); and Bijay Shankar Jaiswal, San Mateo, CA (US)
Assigned to PATHOMIQ INC., Saratoga, CA (US)
Filed by DHRISTI INC., Saratoga, CA (US)
Filed on Feb. 11, 2021, as Appl. No. 17/174,310.
Claims priority of provisional application 62/975,172, filed on Feb. 11, 2020.
Prior Publication US 2021/0249101 A1, Aug. 12, 2021
Int. Cl. G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16B 20/10 (2019.01); G16B 20/20 (2019.01); G16B 40/30 (2019.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 10/00 (2006.01)
CPC G16B 20/20 (2019.02) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G16B 20/10 (2019.02); G16B 40/30 (2019.02); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 10/0041 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30204 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A computer-implemented method of directly predicting molecular changes in one or more disease tissue sections from a subject, the method comprising:
analyzing molecular markers, which are predictive of tumor response and have no defined correlation with morphological features, such that the method is carried out in unsupervised manner; and
using artificial intelligence to correlate morphometric changes with critical gene modifications and molecular changes, wherein using the artificial intelligence comprises:
(i) collecting whole-slide images (WSIs) of tissue sections obtained from the subject;
(ii) feature extracting the whole-slide images into patches and corresponding vectors to generate clusters of vectors with morphologically similar patterns;
(iii) sampling the patches uniformly across each cluster to generate a batch of vectors that represent the subject's images;
(iv) generating a score between 0 and 1 for each patch based on gene status; and
(v) generating an outcome morphometric score for the subject by combining selected patches;
(vi) identifying regions of interest (ROIs) corresponding to morphological features correlated with disease outcome;
(vii) probe the ROIs with genes and protein specific probes;
(viii) identifying and quantifying RNA and proteins expressed in regions of interest (ROIs);
(ix) performing a correlation analysis between the RNA and the proteins expressed in the ROIs; and
(x) identifying biomarkers in the ROIs based on their expression or non-expression in other tissues, thereby identifying ROIs that are predictive of disease outcome.