Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. METHODS: A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose-volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar's hypothesis test. RESULTS: The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar's test applied to all model pair combinations, no statistically significant difference between the models was found. CONCLUSION: Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. ADVANCES IN KNOWLEDGE: This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.

An adaptive and predictive respiratory motion model for image-guided interventions: theory and first clinical application.

This paper describes a predictive and adaptive single parameter motion model for updating roadmaps to correct for respiratory motion in image-guided interventions. The model can adapt its motion estimates to respond to changes in breathing pattern, such as deep or fast breathing, which normally would result in a decrease in the accuracy of the motion estimates. The adaptation is made possible by interpolating between the motion estimates of multiple submodels, each of which describes the motion of the target organ during cycles of different amplitudes. We describe a predictive technique which can predict the amplitude of a breathing cycle before it has finished. The predicted amplitude is used to interpolate between the motion estimates of the submodels to tune the adaptive model to the current breathing pattern. The proposed technique is validated on affine motion models formed from cardiac magnetic resonance imaging (MRI) datasets acquired from seven volunteers and one patient. The amplitude prediction technique showed errors of 1.9-6.5 mm. The combined predictive and adaptive technique showed 3-D motion prediction errors of 1.0-2.8 mm, which represents an improvement in modelling performance of up to 40% over a standard nonadaptive single parameter motion model. We also applied the combined technique in a clinical setting to test the feasibility of using it for respiratory motion correction of roadmaps in image-guided cardiac catheterisations. In this clinical case we show that 2-D registration errors due to respiratory motion are reduced from 7.7 to 2.8 mm using the proposed technique.