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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Explainable Machine Learning Model for Predicting Postoperative Survival in Patients With Locally Advanced Gastric

Zhijie Gong1,2, Liping Zhou3, Yinghao He1,2

  • 1The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.

Cancer Medicine
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an explainable machine learning model to predict survival in locally advanced gastric cancer (LAGC) patients. The random survival forest model identified lymph node ratio, AJCC stage, and age as key predictors, aiding personalized treatment decisions.

Keywords:
explainable machine learninglocally advanced gastric cancerrandom survival forestsurvival prediction

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Area of Science:

  • Oncology
  • Machine Learning
  • Biostatistics

Background:

  • Gastric cancer remains a significant global health challenge, particularly locally advanced gastric cancer (LAGC).
  • Accurate prediction of postoperative survival is crucial for effective treatment planning and patient management in LAGC.
  • Existing prognostic models may lack the accuracy and interpretability needed for personalized medicine.

Purpose of the Study:

  • To develop and validate an explainable machine learning model for predicting postoperative survival in LAGC patients.
  • To optimize predictive accuracy and ensure clinical applicability for personalized prognostication.
  • To identify key prognostic factors influencing survival in LAGC.

Main Methods:

  • Utilized SEER database (8616 LAGC patients) and external validation cohort (235 patients).
  • Developed and compared five predictive models: CoxPH, RSF, XGBoost, GBM, and DeepSurv.
  • Evaluated models using C-index, AUROC, Brier score, ROC curves, calibration curves, and DCA; interpreted using SurvSHAP and SurvLIME.

Main Results:

  • The Random Survival Forest (RSF) model demonstrated superior predictive performance (C-index 0.732 in validation, 0.723 in external validation).
  • Key prognostic factors identified include lymph node ratio (LNR), AJCC stage, and age.
  • An interactive prediction tool was created for individualized prognosis visualization.

Conclusions:

  • An RSF-based explainable model accurately predicts postoperative survival in LAGC patients.
  • LNR, AJCC stage, and age are significant prognostic indicators.
  • The developed interactive tool enhances clinical utility for personalized treatment decisions.