US 12,224,064 B2
Deep learning models for tumor evaluation
Mustafa I. Jaber, Los Angeles, CA (US); Christopher W. Szeto, Culver City, CA (US); Liudmila A. Beziaeva, Culver City, CA (US); and Stephen Charles Benz, Culver City, CA (US)
Assigned to NantCell, Inc., Culver City, CA (US); NantOmics, LLC, Culver City, CA (US); and NantHealth, Inc., Culver City, CA (US)
Appl. No. 17/789,590
Filed by NantCell, Inc., Culver City, CA (US); NantOmics, LLC, Culver City, CA (US); and NantHealth, Inc., Culver City, CA (US)
PCT Filed Jan. 11, 2021, PCT No. PCT/US2021/012980
§ 371(c)(1), (2) Date Jun. 28, 2022,
PCT Pub. No. WO2021/142449, PCT Pub. Date Jul. 15, 2021.
Claims priority of provisional application 62/959,931, filed on Jan. 11, 2020.
Prior Publication US 2023/0030506 A1, Feb. 2, 2023
Int. Cl. G16H 50/20 (2018.01); G06F 18/2413 (2023.01); G06T 7/00 (2017.01); G06V 20/69 (2022.01); G16H 30/40 (2018.01)
CPC G16H 50/20 (2018.01) [G06F 18/2413 (2023.01); G06T 7/0012 (2013.01); G06V 20/69 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01)] 16 Claims
OG exemplary drawing
 
1. A method of operating an apparatus including processing circuitry and a convolutional neural network that is trained to determine a lymphocyte distribution of lymphocytes in an area of an image, the method comprising:
executing, by the processing circuitry, instructions that cause the apparatus to:
receive an image depicting at least part of a tumor,
invoke the convolutional neural network to determine a lymphocyte distribution of lymphocytes in respective areas of the tumor based on the image,
apply a classifier to the lymphocyte distribution to classify the tumor, the classifier trained to classify tumors into a class selected from at least two classes associated with lymphocyte distributions, and
determine a clinical value for an individual based on a set of prognosis data corresponding to individuals with tumors in the class into which the classifier classified the tumor,
wherein the convolutional neural network is further trained to classify an area of the image as one or more area types selected from an area type set including a tumor area, a lymphocyte area, and a stroma area,
wherein determining the lymphocyte distribution of lymphocytes in the tumor includes, for respective lymphocyte areas of the image;
determining a distance of the lymphocyte area to one or both of a tumor area or a stroma area; and
based on the distance, characterizing the lymphocyte area as one of tumor-infiltrating lymphocyte area, a tumor-adjacent lymphocyte area, stroma-infiltrating lymphocyte area, and stroma-adjacent lymphocyte area, and
wherein the classifier is configured to further classify the tumor based on the characterizing of the lymphocyte area.