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Machine learning-based clinical decision support tool for advanced ESCC in the immunotherapy era: a multi-center

Hui Bai1,2, Xiaofeng Wang3, Qifeng Wang3

  • 1Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.

Cancer Biology & Medicine
|April 16, 2026
PubMed
Summary

A new model predicts survival for advanced esophageal squamous cell carcinoma (ESCC) patients receiving immunochemotherapy. This tool uses routine clinical data to personalize prognostic assessments and guide treatment strategies effectively.

Keywords:
Esophageal cancermachine learningprognosistreatment

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Area of Science:

  • Oncology
  • Machine Learning in Medicine
  • Clinical Decision Support

Background:

  • Advanced esophageal squamous cell carcinoma (ESCC) presents a significant challenge in treatment selection.
  • Personalized prognostic assessment is crucial for optimizing treatment strategies in treatment-naïve advanced ESCC patients.

Purpose of the Study:

  • To develop and validate clinical decision support tools for predicting overall survival (OS) in treatment-naïve advanced ESCC patients.
  • To leverage machine learning for enhanced prognostic accuracy using routine clinical variables.

Main Methods:

  • A cohort of 1,048 patients receiving first-line immunochemotherapy was analyzed.
  • Feature selection was performed using Boruta, followed by model development with a random survival forest (RSF) algorithm.
  • Model performance was evaluated using time-dependent area under the receiver operator curve (tAUC), concordance index (C-index), Brier score, calibration plots, and decision curve analysis (DCA).

Main Results:

  • The Boruta-RSF model demonstrated superior predictive performance, with tAUCs for 6-, 12-, and 18-month OS of 0.886, 0.775, and 0.772, respectively, in the training set.
  • The model's superiority was confirmed in an external validation cohort (all tAUCs > 0.750).
  • A publicly accessible web calculator was developed for individualized OS prediction, showing excellent calibration and clinical utility.

Conclusions:

  • The Boruta-RSF model effectively predicts prognosis for advanced ESCC patients using readily available clinical data.
  • The developed web calculator facilitates personalized prognostic assessment and treatment strategy optimization.
  • This tool enhances clinical decision-making for patients with treatment-naïve advanced ESCC.