Quantification of head and neck cancer patients' anatomical changes during radiotherapy: prediction of replanning need

TL;DR

This study developed metrics and machine learning models to predict the need for replanning in head and neck cancer radiotherapy, aiming to improve clinical workflow efficiency and patient care.

Contribution

The paper introduces a novel set of metrics and ML models that predict replanning needs during radiotherapy based on anatomical changes in HNC patients.

Findings

ML models achieved up to 0.82 AUC in predicting replanning.

Metrics can characterize and distinguish patients requiring replanning.

Early prediction models identified 76% of true positives.

Abstract

Head and neck cancer (HNC) patients who undergo radiotherapy (RT) may experience anatomical changes during treatment, compromising the validity of the initial treatment plan, necessitating replanning. However, replanning disrupts clinical workflows, creating a stressful environment. Currently, no standardized method exists to determine the total amount of anatomical change that necessitates replanning. This project aimed to create metrics to describe anatomical changes HNC patients may experience during RT and develop machine learning (ML) models to predict RT replanning. We included a cohort of 150 HNC patients treated at the McGill University Health Centre. Based on the shape of the RT structures, we created metrics and developed an extraction pipeline, called HNGeoNatomyX, to automatically calculate them. A univariate metric analysis using linear regression was conducted to obtain the rate of change of each metric. We also obtained the relative variation of each metric between the pre-treatment scan and the fraction at which replanning was requested. Fraction-specific ML models (models that incorporated information available up to and including the specific fraction) for fractions 5, 10, and 15 were built using the metrics, clinical data, and feature selection techniques. To estimate models' performance, we used a repeated stratified 5-fold cross-validation resampling technique and the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The best specific multivariate models for fractions 5, 10, and 15 yielded testing scores of 0.82, 0.70, and 0.79, respectively. Our models early predicted replanning for 76% of the true positives. The created metrics have the potential to characterize and distinguish which patients will necessitate RT replanning. They show promise in guiding clinicians to evaluate RT replanning for HNC patients and streamline workflows.