## 4 Discussion  
The prevalence of PTL is low and relatively rare, and there are few studies on PTL. Therefore, risk stratification and individualized treatment of PTL play a crucial role in improving survival. ML is increasingly used in the medical and health field, and recent studies have used Cox-PH model analysis to construct various prognostic models of PTL [4; 21; 23; 24; 25]. [PERSON] et al. used Cox regression analysis to evaluate prognostic factors and to find the best treatment plan [4]. The study concluded that surgical treatment alone did not affect the prognosis of patients with primary thyroid DLBCL. However, patients who received a combination of chemotherapy and radiotherapy had a better prognosis. [PERSON] et al. established and verified a nomogram for B-cell primary thyroid malignant lymphoma (BC-PTML), which had higher discrimination power and clinical benefit than the traditional Ann Arbor staging system. [PERSON] et al. also fitted the Cox-PH model to predict the CSS of PTL, and the c-index of the validation set reached 0.762, indicating high accuracy. At present, Cox regression analysis has become a popular prognostic prediction method, but the limitations of fitting linear survival models require more complex methods, such as DL. [PERSON] et al. proved that the DeepSurv model performs as well or better than other survival models in simulated and real survival data [26]. [PERSON] et al. found that the DeepSurv model was more accurate and flexible than the traditional Cox regression analysis model in predicting the survival probability and prognosis of patients with long osteosarcoma [27], with a higher c-index (0.800 VS0.774). This proves the feasibility of applying the DeepSurv model to fit prognostic models. Therefore, the present study proposes the DeepSurv model to predict CSS and OS in PTL patients and contrasts its performance with the Cox-HP model. The dataset underwent feature screening using single-factor and multi-factor Cox regression analysis, recursive feature elimination, and Boruta feature selection. This process yielded a total of 12 important features. To address the issue of imbalanced data categories, the data were processed using SMOTE oversampling, ClusterCentroids downsampling, and SMOTE Tomek link combinatorial sampling. Additionally, multiple machine learning models and multiple evaluation indexes were compared. The XGBoost model based on combinatorial sampling was found to perform the best. In light of these findings, the SHAP theoretical framework was selected to investigate the interpretability of the XGBoost model from both an overall and local perspective. The results indicated that age, the \"B\" symptom, histological type, and marital status were significant factors influencing patients' risk of death. By leveraging the XGBoost model and SHAP framework, medical practitioners can proactively identify patients at elevated mortality risk. This capability enables timely implementation of preventative interventions, which subsequently reduces both consultation times and patient mortality risk. Based on the c-index, the study concluded that the DeepSurv model was superior to the Cox-PH model in predicting survival in PTL patients. In terms of the calibration curve, the mortality risk estimated by DeepSurv was closer to the real situation than that estimated by Cox-PH. In terms of the ROC curve, the AUC value of the DeepSurv model was significantly higher than that of the Cox-PH model, demonstrating that the specificity and sensitivity of the DeepSurv model were better. The net clinical benefit was similar for both in terms of DCA curves. [PERSON] et al. [22] found that the DeepSurv model was superior to the traditional Cox-PH model in predicting survival in patients with malignant small bowel tumors. [PERSON] et al.