System and method for tumor characterization

Patent metadata

Field	Value
Publication number	US-12019674-B2
Application number	US-202016887036-A
Country	US
Kind code	B2
Filing date	May 29, 2020
Priority date	May 29, 2019
Publication date	Jun 25, 2024
Grant date	Jun 25, 2024

Abstract

A method of treating a subject comprises administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject, classifying each of the plurality of cells into categories, dividing the at least one image into a plurality of patches, calculating values for a plurality of morphological features based on the patches, and calculating a distant metastatic recurrence probability based on the values. A computer-implemented method of training a neural network and a system for characterizing a cancer in a subject are also described.

First claim

What is claimed is:

1. A method of treating a subject by administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising:
acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject;
classifying each of the plurality of cells into categories;
dividing the at least one image into a plurality of patches, each patch comprising multiple cells of the plurality of cells;
assigning cells in each of the plurality of patches to one or more clusters using a clustering algorithm to produce clustering data for each patch, the clustering data comprising a cluster size;
calculating a cell density of each of the plurality of patches;
calculating values for a plurality of morphological features based on the patches; and
calculating a distant metastatic recurrence probability based on the values, the cell density, and the clustering data.

2. The method of claim 1 , wherein the patches are acquired at least in part by random sampling.

3. The method of claim 1 , wherein the categories comprise tumor cells, non-tumor cells, or immune cells.

4. The method of claim 1 , further comprising identifying a subset of the patches of the image meeting a criterion selected from the group consisting of no cells appearing in the patch and no tumor cells appearing in the patch; and
discarding the subset of patches.

5. The method of claim 1 , wherein the distant metastatic recurrence probability is calculated by at least one neural network.

6. The method of claim 1 , wherein the patches are selected by calculating a cell density and comparing the cell density to a threshold.

7. The method of claim 1 , wherein the morphological features comprise at least one of (Count of immune cells in “large” cluster)/(Total count of immune cells); (Count of immune cells in “large” cluster)/(Total count of tumor+immune); (Count of immune cells in “large” cluster)/(Count of tumor cells in “large” cluster); (Count of immune cells in “large” cluster)/(Count of immune cells in “small” cluster); (Total count of immune cells)/(Total count of tumor+immune); (Immune cell total area)/(Tumor cell total area); and (Immune cell total area)/(Total Immune area+Tumor area).

8. The method of claim 1 , wherein the distant metastatic recurrence probability is calculated by aggregating a set of votes for each patch in the plurality of patches based on the values.

9. The method of claim 1 , wherein the at least one image comprises at least first and second images, the first and second images acquired from different body regions of the subject.

10. The method of claim 1 , wherein the treatment is selected from the group consisting of Nivolumab, Perbrolizumab, Ipilimumab, Dabrafenib, Trametinib, Vermurafenib, high-dose interferon alfa, chemotherapy, surgical excision, and immunotherapy.

Assignees

• Univ New York

Inventors

• Wang Jing
• Kulkarni Prathamesh
• Robinson Eric

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06V20/698

  Matching; Classification · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title