A Study Investigating Interpretable Deep Learning Models for Predicting Mortality and Survival in Patients with Primary Thyroid Lymphomas

Primary thyroid lymphoma (PTL) is a rare malignancy, and this study aimed to develop a prognostic prediction model for PTL using deep learning algorithms while providing interpretable analyses. Machine learning models were employed for mortality risk prediction, with the SHAP framework introduced for feature interpretation, and a DeepSurv model was constructed for comparison with the Cox proportional hazards (Cox-PH) model. Model performance was evaluated using Harrell’s c-index, ROC curves, AUC, calibration curves, and decision curve analysis (DCA). Results revealed that age, ‘B’ symptoms, histological type, and marital status were the most influential factors affecting patient mortality risk, as identified through SHAP analysis, and the DeepSurv model outperformed the Cox model in predicting the test set (consistency indices 0.758 vs. 0.739 and 0.789 vs. 0.779). Additionally, a web application platform was developed based on the DeepSurv model to predict the 5-year survival rate of PTL patients, facilitating the transition from theoretical research to clinical application. This study highlights the potential of deep learning models, particularly DeepSurv, in improving prognostic predictions for PTL and provides a practical tool for guiding clinical treatment decisions. The findings underscore the value of integrating interpretable machine learning frameworks into survival analysis for rare cancers.