Interpretability of a Deep Learning-Based Prediction Model for Mandibular Osteoradionecrosis.

PURPOSE/OBJECTIVE(S): The development of radiation-induced toxicities is a multifactorial process. Existing DVH-based prediction models use traditional multivariate analysis to combine all the potential risk factors. Recently, deep learning (DL) has been proposed for predicting mandibular osteoradionecrosis (ORN) directly from 3D dose distribution maps [1]. However, with this approach, incorporating non-imaging data such as potential risk factors presents challenges. We investigate the use of DL-based multimodality fusion for the purpose of radiation-induced ORN toxicity prediction.

MATERIALS/METHODS: This study explores early and late fusion strategies for combining 3D radiation dose distribution maps and clinical and demographic variables in the prediction of mandibular ORN incidence in head and neck cancer patients treated with radiotherapy. The results are compared to single-modality predictions with a random forest (RF) trained only on clinical variables and a 3D DenseNet40 trained on dose maps alone. We investigate two different fusion approaches. In the first, the image features extracted from the radiation dose maps using a 3D DenseNet40 were concatenated with the clinical variables into one single vector using a type II early fusion strategy. The combined feature vector was input into a fully connected layer for classification of ORN vs. controls. A final softmax activation layer was added to obtain the class predicted probabilities. The second approach used a late fusion strategy, in which the outputs from the 3D DenseNet40 and the RF model were combined by averaging the predicted classification probabilities for each of the two classes (ORN and no ORN) to obtain the final class decision on a case-by-case basis.

RESULTS: The AUROC values obtained for the late and early fusion models and the single-modality 3D DenseNet40 and RF models were 0.70, 0.68, 0.69 and 0.60, respectively. The highest AUC ROC was observed with the late fusion approach, which was statistically significantly different to that of the RF single-modality model with a significance level of 0.05. However, after Bonferroni correction (Altman 1999) for multiple comparisons was applied, resulting in a corrected significance level of 0.05/6 = 0.008 for each comparison, no statistically significant difference was observed between any of the models' AUROC values. This is most likely due to the lack of discriminative contribution observed from clinical variables, which on their own resulted in a poorly predictive RF model.

CONCLUSION: To our knowledge, no previous work has been published on the use of multimodal fusion DL methods to combine dose distribution maps and clinical variables in the prediction of mandibular ORN. Although non-conclusive results were obtained, this study demonstrates the potential of DL in the prediction of the multifactorial side effects resulting from radiotherapy treatments. [1] Humbert-Vidan L et al. Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using DL (2022).