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Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.

Mohammad Moharrami^1,2, Erin Watson^1,2, Shao Hui Huang³

¹Faculty of Dentistry, University of Toronto, Toronto, ON, Canada.

Journal of Medical Systems

|March 9, 2026

Summary

This summary is machine-generated.

This study developed a 5-feature Random Survival Forest model to predict osteoradionecrosis (ORN) risk after head and neck radiation therapy (RT), accurately accounting for competing risks like death.

Keywords:

Competing risks Fine-gray regression Head and neck cancer Osteoradionecrosis Predictive modeling Random survival forests

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Area of Science:

Oncology
Radiotherapy
Biostatistics

Background:

Osteoradionecrosis (ORN) is a significant complication of head and neck radiation therapy (RT).
Accurate risk prediction is crucial for patient management and treatment planning.
Ignoring competing risks, such as all-cause mortality, can lead to overestimation of ORN risk.

Purpose of the Study:

To develop and validate predictive models for ORN after head and neck RT using time-to-event data.
To incorporate competing risks (death) into ORN risk prediction models.
To quantify the overestimation of ORN risk when competing risks are ignored.

Main Methods:

Prognostic study of 2,466 patients undergoing curative RT (2011-2018).
Utilized Fine-Gray regression (FGR), Random Survival Forests (RSF), and DeepHit, accounting for all-cause mortality as a competing event.
Compared competing risk models with non-competing risk models (Cox PH, standard RSF) using nested cross-validation and SHAP for feature interpretation.

Main Results:

The 5-feature Random Survival Forest (RSF) model, including tumor site, D10cc, smoking pack-years, periodontal condition, and dental insurance, demonstrated strong predictive performance (e.g., time-dependent AUC 0.776).
Competing risk models accurately estimated ORN risk, while non-competing models overestimated it (e.g., 8.7% vs. 6.8% cumulative incidence at 60 months).
The RSF model reliably estimated individualized ORN risk, avoiding the overestimation associated with ignored competing risks.

Conclusions:

A parsimonious 5-feature RSF model effectively predicts osteoradionecrosis (ORN) risk in head and neck cancer patients undergoing RT.
Accounting for competing risks, particularly all-cause mortality, is essential for accurate ORN risk assessment.
The developed model and accompanying web application can aid clinicians in personalized risk stratification and treatment decisions.