###### Abstract  
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 [PERSON]'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. printed: 29 March 2025  
29 April 2025  
3 May 2025  
2025 July 2  
weight loss [6]. PTL is typically diagnosed through surgical biopsy or ultrasound-guided needle biopsy [1,4]. PTL is typically treated with a combination of chemotherapy and radiotherapy due to the potential complications and incomplete resection of residual tissue that may result from surgery [1,7,8]. Only a few cases that require substantial reduction of the tumor are treated with surgery. Treatment that includes both chemotherapy and radiotherapy is commonly known as Combined-Modality Therapy (CMT). Studies have shown that CMT can significantly reduce distant recurrence [9]. Chemotherapy typically involves the use of CHOP in combination with rituximab [1,3]. For patients experiencing airway obstruction, tracheotomy or tracheal stent implantation is typically performed [8]. In survival analysis studies in the medical field, the time-to-event variable is employed as a core response variable to assess the likelihood of an individual to experience a target clinical event and the time of its occurrence. In instances where a target event is not observed in a study subject due to factors unrelated to the study, the data for that individual are considered censored [10]. The target event is defined as any clinically meaningful and observable endpoint event. In this study, the target event is defined as the death of the patient due to cancer. From a statistical perspective, the time-to-event variable is modeled as a random variable and analyzed by conventional parameter estimation methods, including the derivation of the cumulative distribution function and the risk function. The risk function, in particular, is instrumental in delineating the conditional probability density of the occurrence of the target event within a brief period following a specified time point. These methodologies form the foundation for statistical inference on time-to-event variables and their distributional characteristics within a specified population. Machine learning (ML) techniques have great potential in thyroid cancer research, providing new solutions for diagnosis, metastasis prediction, prognosis, and treatment personalization, which may significantly change the treatment of thyroid cancer [11]. Various ML models have been applied to various datasets containing clinical, biochemical and ultrasound derived features. With the rising incidence of thyroid cancer, determining whether a detected tumor is malignant or benign is gradually becoming a major challenge. [PERSON] et al.