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Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data.

An imaging data, multi-parameter technology, applied in the field of digital pathology, can solve the problem of spontaneous regression of stability

Pending Publication Date: 2021-02-12
UNIVERSITY OF PITTSBURGH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In contrast, more advanced CRCs are known to exhibit stability and even spontaneous regression

Method used

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  • Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data.
  • Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data.
  • Predicting cancer recurrence from spatial multi-parameter cellular and subcellular imaging data.

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Embodiment Construction

[0029]As used herein, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" include plural forms.

[0030]As used herein, the term "number" refers to an integer of one or greater than one (ie, multiple).

[0031]As used herein, the terms "component" and "system" are intended to refer to computer-related entities, or hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, a processor, an object, an executable file, a thread of execution, a program, and / or a computer running on a processor. For example, both the application running on the server and the server can be components. One or more components may reside in processes and / or threads of execution, and components may be located on one computer and / or distributed between two or more computers. Although certain ways of displaying information are displayed and described as screenshots with respect to certain dra...

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Abstract

A method of predicting cancer recurrence risk for an individual includes receiving patient spatial multi-parameter cellular and sub-cellular imaging data for a tumor of the individual, and analyzing the patient spatial multi-parameter cellular and sub-cellular imaging data using a prognostic model for predicting cancer recurrence risk to determine a predicted cancer recurrence risk for the individual, wherein the joint prognostic model is based on spatial correlation statistics among features derived for a plurality of intra-tumor spatial domains from spatial multi-parameter cellular and sub-cellular imaging data obtained from a plurality of cancer patients.

Description

[0001]Government contract[0002]The present invention was completed with government support from the #CA204826 grant granted by the National Institutes of Health (NIH). The government has certain rights in this invention.Technical field[0003]The disclosed concept relates to digital pathology, and in particular to spatial correlation statistics derived from spatial multi-parameter cellular and subcellular imaging data from primary tumors and by identifying emergency spatial domain networks associated with recurrence Information to predict the system and method of recurrence risk of cancer patients.Background technique[0004]Colorectal cancer (CRC) is the second most common type of cancer in the world and the third leading cause of cancer-related death. Like other cancers, the development and progression of this multifactorial disease is through the selection of epithelial clones that have the potential to confer malignant phenotypes in the context of a tumor microenvironment (TME) with...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G16H50/50G06V10/26G06V10/50G06V10/771G06V10/776
CPCG06V20/698G06V10/26G06V10/50G06V2201/03G06V10/771Y02A90/10G06V10/776G06F18/211G16H50/20G16H30/40G06N20/00G06F18/217
Inventor S·C·彻努博特拉D·L·泰勒S·U·弗努
Owner UNIVERSITY OF PITTSBURGH
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