Comparison of deep-learning data fusion strategies in mandibular osteoradionecrosis prediction modelling using clinical variables and radiation dose distribution volumes

Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dose distribution images and clinical data involves technical challenges and requires domain knowledge. We propose different data fusion strategies that we hope will serve as a starting point for future DL NTCP studies. Methods. Early, joint and late DL multi-modality fusion strategies were compared using clinical variables and mandibular radiation dose distribution volumes. The discriminative performance of the multi-modality models was compared to that of single-modality models. All the experiments were conducted on a control-case matched cohort of 92 ORN cases and 92 controls from a single institution. Results. The highest ROC AUC score was obtained with the late fusion model (0.70), but no statistically significant differences in discrimination performance were observed between strategies. While late fusion was the least technically complex strategy, its design did not model the inter-modality interactions that are required for NTCP modelling. Joint fusion involved the most complex design but resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation. Conclusions. This is the first study that compares different strategies for including image data into DL NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. We encourage future DL NTCP studies to report on different fusion strategies to better justify their choice of DL pipeline.Comment: 10 pages, 4 figures, 3 table

National audit of a system for rectal contact brachytherapy

Background and purpose: Contact brachytherapy is used for the treatment of early rectal cancer. An overview of the current status of quality assurance of the rectal contact brachytherapy systems in the UK, based on a national audit, was undertaken in order to assist users in optimising their own practices. Material and methods: Four UK centres using the Papillon 50 contact brachytherapy system were audited. Measurements included beam quality, output and radiation field size and uniformity. Test frequencies and tolerances were reviewed and compared to both existing recommendations and published reviews on other kV and electronic brachytherapy systems. External validation of dosimetric measurements was provided by the National Physical Laboratory. Results: The maximum host/audit discrepancy in beam quality determination was 6.5%; this resulted in absorbed dose variations of 0.2%. The host/audit agreement in absorbed dose determination was within 2.2%. The median of the radiation field uniformity measurements was 2.7% and the host/audit agreement in field size was within 1 mm. Test tolerances and frequencies were within the national recommendations for kV units. Conclusions: The dosimetric characterisation of the Papillon 50 was validated by the audit measurements for all participating centres, thus providing reassurance that the implementation had been performed within the standards stated in previously published audit work and recommendations for kV and electronic brachytherapy units. However, optimised and standardised quality assurance testing could be achieved by reducing some methodological differences observed. Keywords: Contact brachytherapy, Electronic brachytherapy, Audi

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