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Machine Learning Improves Prediction of Normal-Tissue Complications Compared with Radiobiological Models in

Kalyan Mondal^1,2,3, Abhijit Mandal², Anuj Vijay¹

¹Department of Physics, Institute of Applied Science and Humanities, GLA University, Mathura, India.

Journal of Medical Physics

|April 27, 2026

Summary

This summary is machine-generated.

Machine learning models significantly outperformed traditional methods in predicting head-and-neck radiotherapy toxicity for organs at risk. These advanced artificial neural network and extreme gradient boosting approaches offer improved accuracy for normal tissue complication probability.

Keywords:

Clinical radiobiology head and neck cancer machine learning normal-tissue complication probability radiotherapy

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Area of Science:

Radiation Oncology
Medical Physics
Machine Learning in Healthcare

Background:

Predicting radiation-induced toxicity in head-and-neck radiotherapy is a significant clinical challenge.
Traditional normal-tissue complication probability (NTCP) models have limitations in accuracy.
Machine learning (ML) offers potential for improved toxicity prediction.

Purpose of the Study:

To compare the predictive performance of traditional NTCP models (Lyman-Kutcher-Burman, relative seriality) with ML models (ANN, XGBoost).
To evaluate model accuracy for organ-at-risk (OAR) toxicity in head-and-neck cancer patients.
To identify key clinical and dosimetric factors influencing toxicity.

Main Methods:

Retrospective analysis of 57 head-and-neck cancer patients treated with modern radiotherapy techniques.
Evaluation of toxicity in parotid glands, larynx, and spinal cord using Common Terminology Criteria for Adverse Events v5.0.
Stratified 5-fold cross-validation to assess model discrimination (AUC) and calibration (Brier score).

Main Results:

ML models demonstrated superior discrimination for parotid gland (ANN AUC: 0.866, XGBoost AUC: 0.847) and larynx toxicity (XGBoost AUC: 0.853) compared to traditional models (all AUC < 0.60).
ML models achieved better calibration (Brier scores 0.135-0.145) than traditional models (0.276-0.295), though systematic under-prediction was noted.
Patient age, total dose, and treatment duration were significant predictors of parotid toxicity.

Conclusions:

Machine learning models effectively capture complex interactions, leading to superior predictive accuracy for radiotherapy toxicity.
Findings suggest ML approaches are promising for personalized treatment planning in head-and-neck radiotherapy.
External validation in larger cohorts is crucial before widespread clinical implementation.