Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Abstract Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we...

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Autores principales:	Jiuzhou Jiang, Hao Pan, Mobai Li, Bao Qian, Xianfeng Lin, Shunwu Fan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4fc0f872ca3141acb0ce6f8ff31106a9
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spelling	oai:doaj.org-article:4fc0f872ca3141acb0ce6f8ff31106a92021-12-02T11:37:26ZPredictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm10.1038/s41598-021-85223-42045-2322https://doaj.org/article/4fc0f872ca3141acb0ce6f8ff31106a92021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85223-4https://doaj.org/toc/2045-2322Abstract Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.Jiuzhou JiangHao PanMobai LiBao QianXianfeng LinShunwu FanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiuzhou Jiang Hao Pan Mobai Li Bao Qian Xianfeng Lin Shunwu Fan Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
description	Abstract Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.
format	article
author	Jiuzhou Jiang Hao Pan Mobai Li Bao Qian Xianfeng Lin Shunwu Fan
author_facet	Jiuzhou Jiang Hao Pan Mobai Li Bao Qian Xianfeng Lin Shunwu Fan
author_sort	Jiuzhou Jiang
title	Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
title_short	Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
title_full	Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
title_fullStr	Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
title_full_unstemmed	Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm
title_sort	predictive model for the 5-year survival status of osteosarcoma patients based on the seer database and xgboost algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4fc0f872ca3141acb0ce6f8ff31106a9
work_keys_str_mv	AT jiuzhoujiang predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm AT haopan predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm AT mobaili predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm AT baoqian predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm AT xianfenglin predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm AT shunwufan predictivemodelforthe5yearsurvivalstatusofosteosarcomapatientsbasedontheseerdatabaseandxgboostalgorithm
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Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

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