US 12,424,321 B2
Systems and methods to process electronic images to predict biallelic mutations
Christopher Kanan, Pittsford, NY (US); and Jorge S. Reis-Filho, New York, NY (US)
Assigned to Paige.AI, Inc., New York, NY (US)
Filed by PAIGE.AI, Inc., New York, NY (US)
Filed on Jul. 7, 2022, as Appl. No. 17/811,090.
Claims priority of provisional application 63/219,668, filed on Jul. 8, 2021.
Prior Publication US 2023/0008197 A1, Jan. 12, 2023
Int. Cl. G16H 50/20 (2018.01); G16H 30/20 (2018.01)
CPC G16H 50/20 (2018.01) [G16H 30/20 (2018.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for diagnosing invasive lobular carcinoma, the method comprising:
receiving one or more digital images into a digital storage device, the one or more digital images including images of breast tissue of a patient;
applying a trained machine learning module to detect a presence or absence of CDH1 biallelic genetic inactivation and/or CDH1 biallelic mutation from the received one or more digital images, the trained machine learning module having been trained using labels of one or more training digital images, wherein the labels correspond to a presence or absence of CDH1; and
determining whether the patient has invasive lobular carcinoma using the detected presence or absence of the CDH1 biallelic genetic inactivation and/or CDH1 biallelic mutation as ground truth, the trained machine learning module having been trained using a plurality of digital images of breast tissue from a plurality of patients and associated mutation data, the associated mutation data comprising integrated mutation profiling of actionable cancer targets (MSK-IMPACT) targeted sequencing data.