Deep-Learning-Based Survival Prediction of Patients with Thyroid Cancer

Abstract

Abstract Background This study acquired data pertaining to individuals diagnosed with Thyroid Cancer (TC) from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, a deep learning and neural network model known as DeepSurv was employed to forecast the survival rate of TC patients and assess its efficacy. Methods Information on individuals diagnosed with TC from the years 2000 to 2019 was collected from the SEER database. The individuals in question were subsequently allocated into training and testing cohorts through a random selection process, maintaining a ratio of 7:3. The outcomes of the DeepSurv model were compared to those of the Cox proportional-hazards (CoxPH) model in order to estimate the chances of survival for TC patients. The accuracy of the model's predictions was evaluated through the examination of calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index). Results A total of 25,797 individuals diagnosed with TC were included in this study, with 18,057 comprising the training group and 7,740 forming the testing cohort. The CoxPH model exhibited robust correlations between age, gender, marital status, surgical intervention, radiation therapy, tumor extension, and the survival outcomes of TC patients. Notably, the C-index for the CoxPH model was 0.884, indicating a high level of predictive accuracy. Additionally, the training cohort data were used to create the DeepSurv model, which produced a higher C-index of 0.904. The predictive performance of both models was assessed, and the 3-, 5-, and 8-year AUC values were calculated. Regarding the CoxPH model, the corresponding area under the receiver operating characteristic curve (AUC) values were determined to be 0.835, 0.797, and 0.756, respectively. In comparison, the DeepSurv model achieved higher AUC values of 0.942, 0.918, and 0.906. The DeepSurv model demonstrated superior predictive ability for TC patients, as indicated by both the AUC values and the calibration curve, suggesting higher reliability compared to the CoxPH model. Conclusion Using TC patient data from the SEER database for research, we built the DeepSurv model, which performed better than the CoxPH model in estimating the survival time of TC patients.