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Vocal Cord Dysfunction in Nonlaryngeal Head and Neck Cancer After Chemoradiation Therapy: Predictive Modeling Using

Sakineh Bagherzadeh1, Pedram Fadavi2, Hamid Abdollahi3,4

  • 1Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran, semums.ac.ir.

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Machine learning models using CT radiomic features and clinical data can predict vocal cord dysfunction in head and neck cancer patients after chemoradiation therapy, improving outcome prediction.

Keywords:
chemoradiation therapymachine learningradiomicsvocal cord dysfunction

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Area of Science:

  • Radiology and Oncology
  • Medical Imaging Analysis
  • Machine Learning in Healthcare

Background:

  • Head and neck cancer (HNC) patients treated with chemoradiation therapy (CRT) are at risk of developing vocal cord dysfunction (VCD).
  • Predicting VCD is crucial for managing treatment side effects and improving patient quality of life.
  • Current prediction methods may not fully capture the complex interplay of factors contributing to VCD.

Purpose of the Study:

  • To investigate the predictive value of computed tomography (CT) radiomic features and dosimetric-clinical biomarkers for VCD in HNC patients undergoing CRT.
  • To develop and evaluate machine learning (ML) models for predicting radiation-induced VCD.

Main Methods:

  • Sixty-five HNC patients treated with CRT were analyzed.
  • CT radiomic features, clinical data, and dose-volume histogram (DVH) metrics were collected.
  • Nine ML classifiers were trained using feature selection algorithms (LASSO, extra trees, elastic net) on radiomics-only and combined datasets.

Main Results:

  • Radiomics models showed moderate predictive performance (e.g., Random Forest AUC of 0.84).
  • Combined models integrating radiomic, clinical, and dosimetric features achieved high predictive accuracy (AUC > 0.95) with LASSO and elastic net.
  • Feature selection algorithms significantly impacted the performance of combined models.

Conclusions:

  • Pretreatment CT radiomic features serve as effective biomarkers for predicting radiation-induced toxicities like VCD.
  • Combining radiomic features with clinical and dosimetric data significantly enhances the predictive modeling of radiotherapy outcomes.
  • These findings support the use of ML for personalized radiotherapy planning and toxicity management in HNC.