Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Stomach cancer patient survival risk prediction method based on histopathology image and gene expression data

A gene expression and risk prediction technology, applied in medical images, neural learning methods, pathological reference, etc., can solve problems such as ignoring clinical information of patients, improve prediction effect and robustness, improve model prediction accuracy, and improve prediction ability Effect

Pending Publication Date: 2022-01-07
俞章盛
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, GSCNN ignores the clinical information of patients
To the best of our knowledge, there is no gastric cancer survival prediction model that integrates histopathological images, clinical data, and gene expression data

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Stomach cancer patient survival risk prediction method based on histopathology image and gene expression data
  • Stomach cancer patient survival risk prediction method based on histopathology image and gene expression data
  • Stomach cancer patient survival risk prediction method based on histopathology image and gene expression data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0032] The present invention relates to a method for predicting the survival risk of gastric cancer patients based on histopathological images and gene expression data. The method aims to provide a fully automated deep convolutional neural network model to directly predict the survival risk of patients from gastric cancer histopathological images (DeepCox-SC model). Using the gastric cancer data in TCGA (The cancer genome atlas, cancer genome data set), it was found that the prognostic accuracy of DeepCox-SC exceeded the accuracy of manual annotation by pathologists. By further integrating histopathological images, clinical data and high-dimensional gene expression data to construct a DeepCox-SC multimodal fusion model, the prediction accuracy is further improved. The realization scheme of the inventive method specifically comprises the following contents:

[0033] 1. Data set preparation

[0034]Histopathological images, clinical data, and gene expression data of gastric ca...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a stomach cancer patient survival risk prediction method based on a histopathology image and gene expression data. The method comprises the steps: carrying out the cell nucleus segmentation of a histopathology image, and selecting an image small block with the largest number of cell nuclei as the input of a neural network; and integrating the histopathology image and the structured demographic and gene expression data to construct a multi-modal fusion model. Compared with the prior art, the method has the advantages of improving the prediction precision and the like.

Description

technical field [0001] The invention relates to the technical field of cancer risk prediction model construction, in particular to a survival risk prediction method for gastric cancer patients based on histopathological images and gene expression data. Background technique [0002] Histopathological images serve as the clinical gold standard for tumor diagnosis and prognosis, guiding clinicians to make more precise treatment decisions. Pathologists grade tumor cells by evaluating them under a microscope for features such as their morphology. However, manual evaluation of histopathology images is time-consuming, subjective, and not reproducible, especially for pathologists working in remote locations. Therefore, fully automated models for predicting survival risk of cancer patients directly from histopathological images have attracted great attention. This computer-aided tool can be used to improve the efficiency and accuracy of pathologists and ultimately provide better tr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G16H50/50G16H50/30G16H30/00G16H70/60G16B25/00G16B40/00G06N3/04G06N3/08
CPCG16H50/50G16H50/30G16H30/00G16H70/60G16B25/00G16B40/00G06N3/08G06N3/045
Inventor 俞章盛魏婷
Owner 俞章盛
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com