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April 1, 2026Intelligent MedicineOpen Access

Predicting the prognosis of locally advanced rectal cancers after neoadjuvant chemoradiotherapy using quantitative MRI features and machine learning: A retrospective study

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Authors

MYMingyu YangWXWentao XieWWWenzhi Wu

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Overview

Retrospective study develops a radiomics model to predict outcomes in rectal cancer post-chemoradiotherapy, suggesting improved treatment strategies.

Key Points

  • This study aims to predict the prognosis of locally advanced rectal cancer patients after neoadjuvant chemoradiotherapy using MRI and clinical data.
  • Involved 234 patients with locally advanced rectal cancer who underwent radical surgery after nCRT.
  • Patients were split into training (164) and testing (70) sets; 1172 MRI features were extracted.
  • Created a clinical radiomics model integrating radiomic features and clinical predictors using the random forest algorithm.
  • The hybrid radiomics model achieved AUCs of 0.832 (training) and 0.764 (testing).
  • The clinical radiomics model attained an AUC of 0.874 (training) and 0.813 (testing), indicating high predictive performance for prognosis.
  • Identified 8 tumor and 12 mesenteric radiomic features correlated with patient prognosis.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/6a5f17565fa505cbb76037a9https://doi.org/10.1016/j.imed.2026.03.004
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