SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO IDENTIFY MUTATIONAL SIGNATURES AND TUMOR SUBTYPES
A method for identifying a mutational signature may include receiving one or more digital images into electronic storage for at least one patient, identifying one or more neoplasms in each received digital image, extracting one or more visual features from each identified neoplasm, and applying a trained machine learning system to identify a mutational signature ratio vector for the one or more extracted visual features.
1 . A computer-implemented method for identifying a mutational signature, comprising:
receiving one or more digital images into electronic storage for at least one patient;
identifying one or more neoplasms in each received digital image;
extracting one or more visual features from each identified neoplasm; and
applying a trained machine learning system to identify a mutational signature ratio vector for the one or more extracted visual features.
2 . The method of claim 1 , wherein the extracted visual features are neoplasm embeddings.
3 . The method of claim 1 , wherein identifying one or more neoplasms includes segmenting each received digital image into subregions.
4 . The method of claim 1 , the method further comprises determining, for each identified mutational signature ratio vector, whether a largest value in the mutational signature ratio vector is below a predetermined certainty threshold.
5 . The method of claim 4 , wherein the method further comprises determining that the largest value in the mutational signature ratio is below the predetermined certainty threshold, and determining that a mutational signature corresponding to the mutational signature ratio is unknown.
6 . The method of claim 5 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and wherein the method further comprises identifying a set of patients among the plurality of patients that have an unknown mutational signature.
7 . The method of claim 6 , further comprising clustering extracted visual features for the identified set of patients.
8 . The method of claim 7 , further comprising:
receiving patient information for each patient among the plurality of patients; and
determining, based on the received patient information and clustered extracted visual features, whether any of the unknown signatures are associated with mutagens.
9 . The method of claim 1 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and the method further includes:
receiving patient information for each patient among the plurality of patients;
determining a set of patients who have similar clinical phenotypes; and
determining disease subtypes based on the identified mutational signature ratio vectors of the determined set of patients.
10 . The method of claim 1 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and the method further includes:
receiving treatment information for each patient among the plurality of patients; and
training a machine learning system that predicts a treatment response based on the identified mutational signature ratio vectors and received treatment information.
11 . The method of claim 1 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and the method further includes:
receiving patient information for each of the plurality of patients;
receiving an indication of a geographic location of each patient; and
determining, based on the received indications of the geographic locations, whether any of the mutational signature ratio vectors are associated with certain geographic locations.
12 . The method of claim 1 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and wherein the method further comprises:
clustering extracted visual features for the plurality of patients; and
determining a mutational signature ratio vector among the identified mutational signature ratio vectors correspond to an unknown mutagen.
13 . A system for processing electronic medical images, the system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
receiving one or more digital images into electronic storage for at least one patient;
identifying one or more neoplasms in each received digital image;
extracting one or more visual features from each identified neoplasm; and
applying a trained machine learning system to identify a mutational signature ratio vector for the one or more extracted visual features.
14 . The system of claim 13 , wherein the operations further comprise determining, for each identified mutational signature ratio vector, whether a largest value in the mutational signature ratio vector is below a predetermined certainty threshold.
15 . The system of claim 14 , wherein the operations further comprise determining that the largest value in the mutational signature ratio is below the predetermined certainty threshold, and determining that a mutational signature corresponding to the mutational signature ratio is unknown.
16 . The system of claim 15 , wherein receiving one or more digital images for at least one patient includes receiving a plurality of digital images for a plurality of patients, wherein applying the trained machine learning system to identify the mutational signature ratio vector includes identifying a plurality of mutational signature ratio vectors, and wherein the operations further comprise identifying a set of patients among the plurality of patients that have an unknown mutational signature.
17 . The system of claim 16 , wherein the operations further comprise clustering extracted visual features for the identified set of patients.
18 . The system of claim 17 , wherein the operations further comprise:
receiving patient information for each patient among the plurality of patients; and
determining, based on the received patient information and clustered extracted visual features, whether any of the unknown signatures are associated with mutagens.
19 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, the operations comprising:
receiving one or more digital images into electronic storage for at least one patient;
identifying one or more neoplasms in each received digital image;
extracting one or more visual features from each identified neoplasm; and
applying a trained machine learning system to identify a mutational signature ratio vector for the one or more extracted visual features.
20 . The computer-readable medium of claim 19 , wherein the operations further comprise determining, for each identified mutational signature ratio vector, whether a largest value in the mutational signature ratio vector is below a predetermined certainty threshold.