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Cancer Survival Analysis

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Updated: May 28, 2026

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia

Published on: February 16, 2024

Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and

Ha Ye Jin Kang¹, Wooyeong Jang², Minsam Ko³

¹Department of Computer Science, Semyung University, Jecheon-si, Chungcheongbuk-do, Republic of Korea.

|May 26, 2026

Summary

This summary is machine-generated.

Machine learning models accurately predict mortality in young gastric cancer (GC) patients. These tools can identify high-risk individuals for tailored treatment, improving outcomes for this growing patient group.

Keywords:

gastric cancer machine learning mortality risk predictive modeling survival prediction model young patients with gastric cancer

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Area of Science:

Oncology
Machine Learning
Biostatistics

Background:

Gastric cancer (GC) incidence is declining globally, yet cases in younger individuals are rising.
Developing accurate mortality prediction models for young GC patients is crucial.

Purpose of the Study:

To develop and evaluate machine learning-based survival models for predicting 3- and 5-year mortality in young GC patients (≤50 years).

Main Methods:

Utilized data from 813 young GC patients from the Gastric Cancer Public Staging Database (2013-2015).
Applied Random Survival Forest, Gradient Boosting, Extra Survival Tree, and Cox proportional hazards models.
Assessed model performance using the concordance index (C-index).

Main Results:

Random Survival Forest achieved a 95.89% C-index for 3-year and 91.82% for 5-year mortality prediction.
Extra Survival Tree model showed high performance with a 94.60% C-index for 5-year mortality.
Tumor stage and size were key predictors, with other variables showing varied contributions.

Conclusions:

Machine learning models demonstrate high accuracy in predicting mortality for young GC patients.
These models can aid in identifying high-risk individuals for more aggressive treatment strategies.