Annals of surgical oncology · 2025
A Deep Learning Survival Model for Evaluating the Survival Prognosis of Papillary Thyroid Cancer: A Population-Based Cohort Study.
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Zheng Guibin, Wei Peng, Li Danxia, Li Xinna, Zafereo Mark, Li Chao, Yu Wenbin, Chen Xiaohong, Zheng Haitao, Song Xicheng, Li Guojun
Аннотация
BACKGROUND: Deep learning can assess the individual survival prognosis in sizeable datasets with intricate underlying processes. However, studies exploring the performance of deep learning survival in papillary thyroid cancer (PTC) are lacking. This study aimed to construct a deep learning model based on clinical risk factors for survival prediction in patients with PTC.
METHODS: A Cox proportional hazards deep neural network (DeepSurv) was developed and validated by using consecutive patients with PTC from 17 US Surveillance, Epidemiology, and End Results Program (SEER) cancer registries (2000-2020). The performance of the DeepSurv model was further validated on two external test datasets from the University of Texas MD Anderson Cancer Center (MDACC) and The Cancer Genome Atlas (TCGA). Using the survival risk scores at 10 years predicted by the DeepSurv model, we classified patients with PTC into low-risk and high-risk groups and explored their overall survival (OS).
RESULTS: The concordance index of the DeepSurv model for predicting OS was 0.798 in the SEER test dataset, 0.893 in the MDACC dataset, and 0.848 in the TCGA dataset. The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups according to the survival risk scores at 10 years. Patients in the high-risk group had significantly worse OS than patients in the low-risk group in all three test datasets (all P < 0.001).
CONCLUSION: The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups, which may provide important prognostic information for personalized treatment in patients with PTC.
METHODS: A Cox proportional hazards deep neural network (DeepSurv) was developed and validated by using consecutive patients with PTC from 17 US Surveillance, Epidemiology, and End Results Program (SEER) cancer registries (2000-2020). The performance of the DeepSurv model was further validated on two external test datasets from the University of Texas MD Anderson Cancer Center (MDACC) and The Cancer Genome Atlas (TCGA). Using the survival risk scores at 10 years predicted by the DeepSurv model, we classified patients with PTC into low-risk and high-risk groups and explored their overall survival (OS).
RESULTS: The concordance index of the DeepSurv model for predicting OS was 0.798 in the SEER test dataset, 0.893 in the MDACC dataset, and 0.848 in the TCGA dataset. The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups according to the survival risk scores at 10 years. Patients in the high-risk group had significantly worse OS than patients in the low-risk group in all three test datasets (all P < 0.001).
CONCLUSION: The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups, which may provide important prognostic information for personalized treatment in patients with PTC.