What question did this study set out to answer?

To develop machine learning models that use radiomic features for predicting postoperative survival in esophageal squamous cell carcinoma (ESCC).

March 13, 2026Open Access

Intratumoral and peritumoral radiomics-based machine learning models for the postoperative survival prediction in esophageal squamous cell carcinoma

Key Points

To develop machine learning models that use radiomic features for predicting postoperative survival in esophageal squamous cell carcinoma (ESCC).
Utilized intratumoral and peritumoral radiomic features
Applied machine learning algorithms including GBDT
Analyzed patient data post-surgery for survival outcomes
The GBDT model showed high effectiveness in risk stratification
Models indicated potential for improved survival prediction for ESCC patients

Abstract

ML models using intratumoral and peritumoral radiomic features showed potential for predicting postoperative survival in ESCC patients, with the optimal GBDT model demonstrating effective risk stratification.

Intratumoral and peritumoral radiomics-based machine learning models for the postoperative survival prediction in esophageal squamous cell carcinoma

Key Points

Abstract

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