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Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.

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Machine learning models can predict treatment response in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCCRT). Key predictors include clinical N stage and blood markers, aiding personalized chemotherapy decisions.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Radiotherapy

Background:

  • Deciding on additional chemotherapy after neoadjuvant concurrent chemoradiotherapy (nCCRT) for locally advanced rectal cancer (LARC) lacks clear evidence.
  • This study addresses the challenge of optimizing treatment strategies for LARC patients.

Purpose of the Study:

  • To evaluate machine learning (ML) model performance in predicting treatment response for LARC patients receiving nCCRT alone versus nCCRT plus chemotherapy (CT).
  • To identify key features associated with improved treatment outcomes.
  • To derive ML-based thresholds for treatment response prediction.

Main Methods:

  • A retrospective analysis of 409 LARC patients treated at three hospitals.
  • Comparison of two groups: nCCRT alone (n=182) and nCCRT plus CT (n=227).
  • Evaluation of four ML algorithms (K-Star, Random Forest, Multilayer Perceptron, Random Committee) and feature-ranking algorithms using 34 variables.

Main Results:

  • K-Star model showed highest accuracy for nCCRT alone (80.8%; AUC=0.89), while Random Committee performed best for nCCRT plus CT (77.3%; AUC=0.84).
  • Top predictors included clinical N stage (cN), Sodium, Glutamic pyruvic transaminase (GPT), eGFR, and blood counts.
  • ML-derived thresholds indicated improved outcomes with higher lymphocyte percentage and lower platelet distribution width for CT, and elevated eGFR, GPT, and cN=2 for nCCRT alone.

Conclusions:

  • ML models demonstrate strong predictive performance for treatment response in LARC patients post-nCCRT.
  • Key clinical and laboratory variables can guide personalized chemotherapy decisions after nCCRT.
  • This approach supports individualized treatment strategies for LARC management.