SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES TO DETERMINE ONCOGENIC SIGNALS
Systems and methods are disclosed for generating and predicting behavior of patient-specific oncogenic signaling pathways or networks. In some aspects, patient-specific oncogenic signaling pathways or networks may be generated by receiving one or more digital medical images associated with a patient, providing an unpopulated gene network graph and the one or more digital medical images as input to a trained machine learning system that is trained to populate the gene network graph with gene expression levels specific to the patient based on the one or more digital medical images, and receiving, as output from the trained machine learning system, the gene network graph populated with the gene expression levels specific to the patient.
1 . A method for processing digital medical images to populate gene network graphs, comprising:
receiving one or more digital medical images associated with a patient;
providing an unpopulated gene network graph and the one or more digital medical images as input to a trained machine learning system that is trained to populate the gene network graph with gene expression levels specific to the patient based on the one or more digital medical images; and
receiving, as output from the trained machine learning system, the gene network graph populated with the gene expression levels specific to the patient.
2 . The method of claim 1 , further comprising:
receiving clinical data associated with the patient; and
providing the clinical data as additional input to the trained machine learning system.
3 . The method of claim 1 , wherein the digital medical images include digital whole slide images, digital multiplex immunofluorescent images, or digital multiplex immunohistochemistry images.
4 . The method of claim 1 , wherein the machine learning system is trained by:
receiving, as training data, a plurality of digital medical images associated with a plurality of patients and populated gene network graphs for the plurality of patients; and
training the machine learning system, using the training data, to infer one or more of the populated gene network graphs based on the respective one or more digital medical images.
5 . The method of claim 4 , wherein the training data further includes clinical data associated with a plurality of patients, and the clinical data includes one or more of age, medical history, cancer treatment history, family history, past biopsy or cytology information, tumor sequencing information, mRNA expression levels.
6 . The method of claim 4 , wherein the gene network graphs for the plurality of patients are populated by:
receiving an unpopulated gene network graph, the unpopulated gene network graph comprising a gene network graph without expression levels;
receiving tumor sequencing information associated with the plurality of patients; and
populating the gene network graphs with expression levels for the plurality of patients based on the respective tumor sequencing information.
7 . The method of claim 6 , wherein the population of the gene network graphs for the plurality of patients comprises:
determining whether there are missing values for expression levels in the each of the populated gene network graphs; and
upon determining that there are missing values in one or more of the populated gene network graphs, using one or more label propagation techniques to infer the missing values.
8 . The method of claim 7 , wherein the one or more label propagation techniques include directed label propagation.
9 . A method for training a machine learning system to populate a gene network graph, comprising:
receiving an unpopulated gene network graph, the unpopulated gene network graph comprising a gene network graph without expression levels;
receiving tumor sequencing information associated with each of a plurality of patients;
receiving one or more digital medical images associated with each of the plurality of patients;
populating, for each of the plurality of patients, the gene network graph to include expression levels based on the respective tumor sequencing information; and
training the machine learning system to infer one or more of the populated gene network graphs based on the respective one or more digital medical images.
10 . The method of claim 9 , wherein the digital medical images comprise digital whole slide images, digital multiplex immunofluorescent images, or digital multiplex immunohistochemistry images.
11 . The method of claim 9 , further comprising:
determining, for each populated gene network graph, whether there are missing values for expression levels in the gene network graph; and
upon determining that there are missing values, using one or more label propagation techniques to infer the missing values.
12 . The method of claim 11 , wherein the one or more label propagation techniques include directed label propagation.
13 . The method of claim 9 , further comprising:
receiving clinical data associated with each of the plurality of patients, wherein the machine learning system is further trained to infer the one or more populated gene network graphs based on the respective clinical data, the clinical data including age, medical history, cancer treatment history, family history, past biopsy or cytology information, tumor sequencing information, mRNA expression levels, or any combination thereof.
14 . A system for processing digital medical images to populate gene network graphs, 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 medical images associated with a patient;
providing an unpopulated gene network graph and the one or more digital medical images as input to a trained machine learning system that is trained to populate the gene network graph with gene expression levels specific to the patient based on the one or more digital medical images; and
receiving, as output from the trained machine learning system, the gene network graph populated with the gene expression levels specific to the patient.
15 . The system of claim 14 , further comprising:
receiving clinical data associated with the patient; and
providing the clinical data as additional input to the trained machine learning system.
16 . The system of claim 14 , wherein the digital medical images comprise digital whole slide images, digital multiplex immunofluorescent images, or digital multiplex immunohistochemistry images.
17 . The system of claim 14 , wherein the machine learning system is trained by:
receiving, as training data, a plurality of digital medical images associated with a plurality of patients and populated gene network graphs for the plurality of patients; and
training the machine learning system, using the training data, to infer one or more of the populated gene network graphs based on the respective one or more digital medical images.
18 . The method of claim 17 , wherein the training data further includes clinical data associated with a plurality of patients, and the clinical data includes one or more of age, medical history, cancer treatment history, family history, past biopsy or cytology information, tumor sequencing information, mRNA expression levels.
19 . The method of claim 18 , wherein the population of the gene network graphs for the plurality of patients comprises:
determining whether there are missing values for expression levels in the each of the populated gene network graphs; and
upon determining that there are missing values in one or more of the populated gene network graphs, using one or more label propagation techniques to infer the missing values.
20 . The method of claim 19 , wherein the one or more label propagation techniques include directed label propagation.