###### Abstract
[12] used ML techniques for the early detection of thyroid cancer in the pre-symptomatic stage. The dataset they used was collected from King Fahd Specialized Hospital in Dammam, Saudi Arabia, and the final results showed that Random Forest had the highest prediction accuracy (90.91%). [PERSON] et al. [13] investigated the application of ML in the prediction of malignancy of indeterminate thyroid nodules using non-invasive assay data and the results showed good performance of Random Forest. Past studies have less frequently used optimization algorithms to optimize hyperparameters for machine learning, and in a study by [PERSON] [14], the Naked Mole Mouse algorithm was applied for the first time to optimize the parameters of a machine learning model and perform feature selection. The results of the study show that the naked mole rat algorithm has great potential to improve the accuracy of machine learning models, which also provides ideas for subsequent research. Most patients with differentiated thyroid cancer recur even after initial treatment, and identifying individuals at high risk of recurrence is crucial for optimal patient management. [PERSON] et al. [15] combined XGBoost and SHAP for predicting the probability of recurrence in patients, which improved the identification of patients with a high risk of tumor recurrence. [PERSON] et al. [16] demonstrated the effectiveness of regularized class association rules (RCAR) in predicting thyroid cancer recurrence. [PERSON] et al. [17] introduced a new machine learning approach to create a prognostic system for cancer patients, which was significantly more accurate in predicting survival than the AJCC staging scheme. [PERSON] et al. [18] evaluated the performance of five ML algorithms in predicting short-term survival in ATC patients and found that these models could potentially guide clinical decisions and individualized treatment strategies. The utilization of machine learning in healthcare has the potential to enhance the efficacy and precision of medical diagnosis. However, deep learning and machine learning models are typically regarded as opaque, with limited interpretability, making it challenging to comprehend the influence of features on model predictions, which is crucial for disease diagnosis [19]. Consequently, the SHAP approach was employed to elucidate the variables that influence the mortality risk in patients with primary thyroid lymphoma. Currently, machine learning (ML) has been employed to predict the overall survival (OS) and cancer-specific survival (CSS) of PTL [4, 20, 21]. However, these models depend on the traditional Cox proportional hazard model (Cox-PH), requiring manual feature selection and consuming significant time and effort. Additionally, it is important to note that Cox-PH is a semiparametric model that can only be used to explore linear relationships between covariates [22]. However, the relationship between covariates and survival outcomes is generally nonlinear. Therefore, the aim of this study is to assess the feasibility of using the deep learning (DL) model DeepSurv for PTL survival analysis based on the Surveillance, Epidemiology, and End Results (SEER) dataset. Through the DeepSurv model, physicians can assess the survival probability of patients to adjust treatment measures in a timely manner. In addition, by building a web-based application platform, doctors can input patients' clinical data in real time, and the model will automatically generate personalized survival prediction results, providing more comprehensive support for precision medicine.