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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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Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell

Meng Qing Xu¹, Zhi Sheng Jiang², Wan Yu Liao^1,3

¹Department of Gastroenterology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 305, Zhongshan East Road, Nanjing, 210002, China, 86 17826080919.

|October 23, 2025

Summary

This study developed a support vector machine (SVM) model to predict recurrence in esophageal squamous cell carcinoma (ESCC) patients after surgery. The SVM model accurately identifies high-risk patients, improving clinical decision-making for ESCC recurrence.

Keywords:

risk factors AI ESCC SVM adjuvant chemotherapy artificial intelligence chemotherapy esophageal cancer esophageal squamous cell carcinoma malignancy morbidity mortality rates nomogram oncology postoperative recurrence support vector machine surgery

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Area of Science:

Oncology
Machine Learning in Medicine
Surgical Outcomes Research

Background:

Esophageal squamous cell carcinoma (ESCC) survival prediction models are common, but postoperative recurrence prediction is less developed.
Accurate prediction of recurrence is crucial for effective management of ESCC patients post-surgery.

Purpose of the Study:

To develop and validate a support vector machine (SVM)-based model for predicting postoperative recurrence risk in ESCC.
To identify key factors associated with recurrence in ESCC patients following surgical intervention.

Main Methods:

Retrospective analysis of clinical data from 311 ESCC patients undergoing surgery.
Development and validation of SVM algorithms to stratify patients into high- or low-recurrence-risk groups.
Performance evaluation using sensitivity, specificity, Youden index, and calibration curves across test and validation cohorts.

Main Results:

The SVM7 model, incorporating TNM stage, adjuvant therapy, differentiation, tumor size, and complications, showed superior recurrence prediction sensitivity.
A composite model (SVM6+8) achieved high prediction sensitivities (up to 94%) and specificities across cohorts.
SVM-based risk stratification correlated with significantly longer disease-free survival, highlighting its clinical utility.

Conclusions:

The developed SVM-based model provides accurate prediction of postoperative recurrence in ESCC patients.
The model demonstrates high sensitivity, specificity, and discriminative power for clinical risk stratification.
This tool aids clinicians in identifying patients at high risk for recurrence, facilitating personalized treatment strategies.