Esophageal squamous carcinoma patient survival risk prediction method based on convolutional neural network

Abstract

The invention provides an esophageal squamous carcinoma patient survival risk prediction method based on a convolutional neural network. The method comprises the steps of: collecting M clinical phenotype indexes and survival information of esophageal squamous carcinoma patients as original data; performing research by using a Kaplan-Meier method and a log-rank method to obtain a relationship between clinical phenotype indexes and lifetime information of the esophageal cancer patients; analyzing clinical phenotype indexes influencing survival prognosis of the patients by using Univariate Cox hazard analysis; extracting clinical phenotype indexes with higher correlation with the survival risk of the patients through a Relief feature selection algorithm and Pearson correlation analysis; and finally, constructing a survival risk prediction model of the esophageal squamous carcinoma patients by using a convolutional neural network and using clinical phenotype indexes with higher correlation, and further judging the prognosis survival risk of the patients. According to the method, the postoperative survival condition of the esophageal squamous carcinoma patients is accurately predicted, the prognosis risk prediction capability is improved, and the prognosis risk prediction cost is reduced.

Description

technical field [0001] The invention relates to the technical field of cancer risk assessment, in particular to a convolutional neural network-based survival risk prediction method for patients with esophageal squamous cell carcinoma. Background technique [0002] Esophageal cancer is one of the major malignant tumors that threaten the health of all human beings. Its incidence rate ranks 8th among malignant tumors in the world, and its mortality rate ranks 6th. The number of people who die from esophageal cancer in the world exceeds 300,000 each year. Esophageal cancer is mainly Can be divided into esophageal adenocarcinoma and esophageal squamous cell carcinoma. Esophageal adenocarcinoma is mainly distributed in Europe and America, mainly in the United States, and esophageal squamous cell carcinoma is mainly distributed in Asia, mainly in China. my country is one of the regions with a high incidence of esophageal cancer in the world, and esophageal cancer has become an imp...

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Problems solved by technology

[0005] Aiming at the deficiencies in the existing background technology, the present invention proposes a convolutional neural network-based survival risk prediction method for patients with esophageal squamous cell carcinoma, which solves the problem that the existing evaluation model has poor prediction effect and cannot help patients judge the prognosis effect. technical problem

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