Deep learning prediction of radiation-induced xerostomia with supervised contrastive pre-training and cluster-guided loss

Xerostomia is a common toxicity for patients with head and neck cancer (HNC) treated with radiation therapy (RT) with significant potential to impact patient quality of life. In this study, we propose a deep learning model that predicts whether the patient will experience xerostomia 3-6 months after RT. A 3D residual network is designed to predict xerostomia at 3-6 months post-RT from RT planning CT data. We hypothesize that self-supervised contrastive pre-training that forces the network to learn invariant features from data samples after different data augmentations, as well as samples with the same labels, can effectively overcome the issue of scarce data to improve xerostomia prediction. Furthermore, during training, a side branch that outputs latent embeddings is optimized to cluster samples with the same label. The cluster centers are updated using the exponential moving average method to better fit the samples. The Euclidean distances between the latent embeddings and the cluster centers are added to the classification logits to guide the classification for stronger supervision and generalization. The xerostomia prediction model was trained and tested on 500 HNC patient data, and achieved mean±SD AUC, sensitivity, specificity, negative predictive value, precision and accuracy of 0.80±0.03, 0.67±0.13, 0.71±0.10, 0.75±0.05, 0.65±0.07, 0.70±0.03, respectively. Our results suggest that the developed model is a promising approach for predicting the occurrence of xerostomia 3-6 months post-RT. Furthermore, the supervised contrastive learning as well as the proposed cluster-guided loss are powerful tools for improving the model’s generalizability in predicting xerostomia.