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Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes.

Cheng-Ming Peng1,2, Chun-Wen Chen3,4,5, Chia-Hong Hsieh3,6

  • 1Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.

World Journal of Gastrointestinal Oncology
|October 20, 2025
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Summary

Machine learning models combining radiomics and sarcopenia show promise for predicting esophageal cancer survival. Integrating these biomarkers with clinical data improves prognostic accuracy for personalized treatment planning.

Keywords:
Esophageal cancerGastroesophageal cancerMachine learningOutcome predictionRadiomicsSarcopenia

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Esophageal cancer is aggressive with poor outcomes, necessitating improved prognostic tools.
  • Current treatments often fail due to late diagnosis and high recurrence rates.
  • Personalized management strategies require accurate prediction of patient survival.

Purpose of the Study:

  • To systematically review recent studies (2022-2024) on integrated machine learning (ML) models for esophageal cancer prognosis.
  • To evaluate the combined predictive power of radiomics, sarcopenia, and clinical data for survival outcomes.
  • To assess the potential of multimodal biomarkers in enhancing risk stratification for esophageal cancer patients.

Main Methods:

  • Systematic review of six studies (2022-2024) involving patients with esophageal and gastroesophageal cancers.
  • Analysis of integrated ML models incorporating radiomics (tumor texture/shape) and sarcopenia (skeletal muscle indices).
  • Inclusion of various imaging modalities (PET/CT, CT) and ML algorithms (Cox regression, random forest, LGBM).

Main Results:

  • Integrated ML models combining clinical data, radiomics, and sarcopenia demonstrated superior survival prediction compared to single-modality models.
  • Features analyzed included skeletal muscle indices, tumor texture, and shape descriptors.
  • Studies utilized sample sizes ranging from 83 to 243 patients.

Conclusions:

  • Multimodal imaging biomarkers (radiomics and sarcopenia) integrated with ML show significant potential for robust, individualized prognostic models in esophageal cancer.
  • These models can enhance personalized risk stratification and treatment planning.
  • Further research is needed for standardization and external validation due to the retrospective nature of most studies.