Cancer reports (Hoboken, N.J.) · 2025
Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER-Based Study.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Li Xuanzi, Yang Shuai, Peng Yingpeng, You Xueqiang, Peng Shunli, Wang Siyang, Zha Dasong, Zhang Shuyuan, Deng Chuntao
Аннотация
PURPOSE: Pineoblastomas (PBs) are rare central nervous system tumors primarily affecting children and adolescents, with limited data on clinical characteristics and survival outcomes. Prognosis prediction models for this disease are lacking. The purpose of this study was to develop deep learning (DL) models for predicting 3-year survival in patients with pineoblastoma.
METHODS: Patients with pineoblastomas of all ages were identified from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2019). Deep neural networks (DNN) were trained and tested at a ratio of 7:3 in a 5-fold cross-validated fashion. Multivariate CPH models were constructed for comparison. The primary outcomes were 3-year overall survival (OS) and disease-specific survival (DSS). All the variables were included in the analysis. Receiver operating characteristic (ROC) curve analysis and calibration plots were used to evaluate the model performance.
RESULTS: A total of 145 patients were included in this study. The area under the curve (AUC) for the DNN models was 0.92, 0.91, and 0.749 for OS and 0.76 for DSS. The DNN models exhibited good calibration: the OS model (slope = 0.94, intercept = 0.07) and DSS model (slope = 0.81, intercept = 0.20).
CONCLUSION: Our DNN models showed a more accurate prediction of survival outcomes in patients with pineoblastoma than the widely used CPH models. These results indicate the potential of DL algorithms to improve outcome prediction in patients with rare tumors.
METHODS: Patients with pineoblastomas of all ages were identified from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2019). Deep neural networks (DNN) were trained and tested at a ratio of 7:3 in a 5-fold cross-validated fashion. Multivariate CPH models were constructed for comparison. The primary outcomes were 3-year overall survival (OS) and disease-specific survival (DSS). All the variables were included in the analysis. Receiver operating characteristic (ROC) curve analysis and calibration plots were used to evaluate the model performance.
RESULTS: A total of 145 patients were included in this study. The area under the curve (AUC) for the DNN models was 0.92, 0.91, and 0.749 for OS and 0.76 for DSS. The DNN models exhibited good calibration: the OS model (slope = 0.94, intercept = 0.07) and DSS model (slope = 0.81, intercept = 0.20).
CONCLUSION: Our DNN models showed a more accurate prediction of survival outcomes in patients with pineoblastoma than the widely used CPH models. These results indicate the potential of DL algorithms to improve outcome prediction in patients with rare tumors.