Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study
ABSTRACT 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 lear...
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Wiley
2025-08-01
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| Series: | Cancer Reports |
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| Online Access: | https://doi.org/10.1002/cnr2.70303 |
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| author | Xuanzi Li Shuai Yang Yingpeng Peng Xueqiang You Shunli Peng Siyang Wang Dasong Zha Shuyuan Zhang Chuntao Deng |
| author_facet | Xuanzi Li Shuai Yang Yingpeng Peng Xueqiang You Shunli Peng Siyang Wang Dasong Zha Shuyuan Zhang Chuntao Deng |
| author_sort | Xuanzi Li |
| collection | DOAJ |
| description | ABSTRACT 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. |
| format | Article |
| id | doaj-art-5b1dbb79d72f4a50b0fcddce5845212c |
| institution | Kabale University |
| issn | 2573-8348 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj-art-5b1dbb79d72f4a50b0fcddce5845212c2025-08-26T06:00:41ZengWileyCancer Reports2573-83482025-08-0188n/an/a10.1002/cnr2.70303Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based StudyXuanzi Li0Shuai Yang1Yingpeng Peng2Xueqiang You3Shunli Peng4Siyang Wang5Dasong Zha6Shuyuan Zhang7Chuntao Deng8The Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaDepartment of Radiotherapy of The Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaThe Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaSchool of Innovative Engineering Macao University of Science and Technology Macao Macao Special Administrative Region ChinaThe Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaThe Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaThe Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaThe Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaDepartment of Radiotherapy of The Cancer Center The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guang dong Province ChinaABSTRACT 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.https://doi.org/10.1002/cnr2.70303artificial intelligencedeep learningmachine learningpineoblastomaSEER |
| spellingShingle | Xuanzi Li Shuai Yang Yingpeng Peng Xueqiang You Shunli Peng Siyang Wang Dasong Zha Shuyuan Zhang Chuntao Deng Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study Cancer Reports artificial intelligence deep learning machine learning pineoblastoma SEER |
| title | Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study |
| title_full | Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study |
| title_fullStr | Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study |
| title_full_unstemmed | Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study |
| title_short | Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study |
| title_sort | development and validation of survival prediction models for patients with pineoblastomas using deep learning a seer based study |
| topic | artificial intelligence deep learning machine learning pineoblastoma SEER |
| url | https://doi.org/10.1002/cnr2.70303 |
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