Predicting taste alteration in head and neck cancer patients using 3D transformer-based deep learning model.

PURPOSE: Taste alteration is a common post-treatment side effect of head and neck cancer (HNC) radiotherapy. Accurate normal tissue complication probability (NTCP) models can support treatment planning aimed at reducing side effects that impair quality-of-life. Deep learning (DL) offers opportunities to incorporate full 3D dose, CT, and organofinterest (OOI) data. Using a large prospective cohort, this study explores whether CNN and transformerbased DL models can outperform a conventional logistic regression model for predicting taste alteration 6 months after radiotherapy. MATERIAL/METHODS: Data from 972 patients was used, with the endpoint being moderatetosevere patientreported taste alteration at six months (EORTC QLQH&N35). DL models were trained using 3D dose distributions, planning CT, OOI segmentations and clinical features. A conventional NTCP model using oral cavity mean dose, parotid mean dose, and age served as reference. Attention maps were evaluated for the transformer-based DL model to quantify the model attention to specific regions of the head and neck area. In addition, two cropping approaches for the 3D data were compared. RESULTS: The CNN and transformer DL models performed comparable to the reference NTCP model in the test set (Reference: AUC = 0.71; ResNet: AUC = 0.68; TransRP: AUC = 0.71), though calibration was best for the reference model. Shifting the crop improved DL performance and transformer attention focused on supraglottic and submandibular regions. CONCLUSION: This study showed that the prediction of taste alteration can be achieved using DL models, particularly transformer-based models which, in contrast to CNNs, can account for global features of comprehensive 3D information of dose distribution, CT-images and organs-of-interest segmentations.