SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO IDENTIFY TUMOR SUBCLONES AND RELATIONSHIPS AMONG SUBCLONES

1 . A computer-implemented method for detecting tumor subclones, the method comprising:

receiving one or more digital images into a digital storage device, the one or more digital images including images of a tumor of a patient;

detecting one or more neoplasms in the one or more received digital images for each patient;

determining a score for each detected one or more neoplasms;

determining a hierarchy dendrogram based on the detected one or more neoplasms and the determined score for each of the detected one or more neoplasms;

determining one or more leaf nodes based on the determined hierarchy dendrogram;

determining, based on the determined hierarchy dendrogram, whether there are two or more neoplasms among the detected one or more neoplasms that originated independently; and

displaying and/or storing the determined two or more neoplasms.

2 . The method of claim 1 , further comprising determining a spatial location for each neoplasm or a determined neoplasm region, wherein determining the hierarchy dendrogram is further based on the determined spatial location.

3 . The method of claim 1 , wherein determining the score includes applying a trained scoring system.

4 . The method of claim 1 , wherein the score corresponds to an outcome or treatment response.

5 . The method of claim 1 , further comprising using hierarchical clustering on the determined score for each detected neoplasm, wherein determining the hierarchy dendrogram is further based on the hierarchical clustering.

6 . The method of claim 1 , further comprising determining a type for each detected neoplasm.

7 . The method of claim 1 , wherein detecting one or more neoplasms in the one or more received digital images for each patient is performed using a trained machine learning model.

8 . The method of claim 1 , further comprising outputting the hierarchy dendrogram on a display.

9 . The method of claim 1 , further comprising determining effective treatment for the patient.

10 . The method of claim 9 , wherein determining effective treatment for the patient comprises:

providing neoplasms determined to have originated independently to a machine learning system trained to infer optimal treatment for each neoplasm determined to have originated independently;

generating a list of viable treatments for each neoplasm determined to have originated independently;

selecting the treatment that is predicted to be effective across neoplasms determined to have originated independently.

11 . A system for detecting tumor subclones, the system comprising:

a data storage device storing instructions for detecting tumor subclones in an electronic storage medium; and

a processor configured to execute the instructions to perform a method including:

receiving one or more digital images into a digital storage device, the one or more digital images including images of a tumor of a patient;

detecting one or more neoplasms in the one or more received digital images for each patient;

determining a score for each detected one or more neoplasms;

determining a hierarchy dendrogram based on the detected one or more neoplasms and the determined score for each of the detected one or more neoplasms;

determining one or more leaf nodes based on the determined hierarchy dendrogram;

determining, based on the determined hierarchy dendrogram, whether there are two or more neoplasms among the detected one or more neoplasms that originated independently; and

displaying and/or storing the determined two or more neoplasms.

12 . The system of claim 11 , further comprising determining a spatial location for each neoplasm or a determined neoplasm region, wherein determining the hierarchy dendrogram is further based on the determined spatial location.

13 . The system of claim 11 , wherein detecting one or more neoplasms in the one or more received digital images for each patient is performed using a trained machine learning model.

14 . The system of claim 11 , wherein determining the score includes applying a trained scoring system.

15 . The system of claim 11 , wherein the score corresponds to an outcome or treatment response.

16 . A non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform a method for detecting tumor subclones, the method including:

receiving one or more digital images into a digital storage device, the one or more digital images including images of a tumor of a patient;

detecting one or more neoplasms in the one or more received digital images for each patient;

determining a score for each detected one or more neoplasms;

determining a hierarchy dendrogram based on the detected one or more neoplasms and the determined score for each of the detected one or more neoplasms;

determining one or more leaf nodes based on the determined hierarchy dendrogram;

determining, based on the determined hierarchy dendrogram, whether there are two or more neoplasms among the detected one or more neoplasms that originated independently; and

displaying and/or storing the determined two or more neoplasms.

17 . The non-transitory machine-readable medium of claim 16 , the method further comprising determining a spatial location for each neoplasm or a determined neoplasm region, wherein determining the hierarchy dendrogram is further based on the determined spatial location.

18 . The non-transitory machine-readable medium of claim 16 , wherein detecting one or more neoplasms in the one or more received digital images for each patient is performed using a trained machine learning model.

19 . The non-transitory machine-readable medium of claim 16 , wherein determining the score includes applying a trained scoring system.

20 . The non-transitory machine-readable medium of claim 16 , wherein the score corresponds to an outcome or treatment response.