Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Wen-Cai Liu,1,2 Ming-Xuan Li,2 Wen-Xing Qian,3 Zhi-Wen Luo,1,4 Wei-Jie Liao,1,4 Zhi-Li Liu,1,4 Jia-Ming Liu1,4 1Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First Clinical Medical College of Nanchang Unive...

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Autores principales:	Liu WC, Li MX, Qian WX, Luo ZW, Liao WJ, Liu ZL, Liu JM
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	prostate cancer bone metastasis machine learning prediction model seer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/3a02e26962b042a5830373edf1b3bf91
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spelling	oai:doaj.org-article:3a02e26962b042a5830373edf1b3bf912021-11-23T18:43:01ZApplication of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer1179-1322https://doaj.org/article/3a02e26962b042a5830373edf1b3bf912021-11-01T00:00:00Zhttps://www.dovepress.com/application-of-machine-learning-techniques-to-predict-bone-metastasis--peer-reviewed-fulltext-article-CMARhttps://doaj.org/toc/1179-1322Wen-Cai Liu,1,2 Ming-Xuan Li,2 Wen-Xing Qian,3 Zhi-Wen Luo,1,4 Wei-Jie Liao,1,4 Zhi-Li Liu,1,4 Jia-Ming Liu1,4 1Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First Clinical Medical College of Nanchang University, Nanchang, 330006, People’s Republic of China; 3School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China; 4Institute of Spine and Spinal Cord, Nanchang University, Nanchang, 330006, People’s Republic of ChinaCorrespondence: Jia-Ming LiuDepartment of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Donghu District, Nanchang, Jiangxi Province, People’s Republic of ChinaTel/Fax +86-791-86319815Email liujiamingdr@hotmail.comObjective: This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients.Methods: Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model.Results: A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients.Conclusion: This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.Keywords: prostate cancer, bone metastasis, machine learning, prediction model, SEERLiu WCLi MXQian WXLuo ZWLiao WJLiu ZLLiu JMDove Medical Pressarticleprostate cancerbone metastasismachine learningprediction modelseerNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancer Management and Research, Vol Volume 13, Pp 8723-8736 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	prostate cancer bone metastasis machine learning prediction model seer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	prostate cancer bone metastasis machine learning prediction model seer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Liu WC Li MX Qian WX Luo ZW Liao WJ Liu ZL Liu JM Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
description	Wen-Cai Liu,1,2 Ming-Xuan Li,2 Wen-Xing Qian,3 Zhi-Wen Luo,1,4 Wei-Jie Liao,1,4 Zhi-Li Liu,1,4 Jia-Ming Liu1,4 1Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First Clinical Medical College of Nanchang University, Nanchang, 330006, People’s Republic of China; 3School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China; 4Institute of Spine and Spinal Cord, Nanchang University, Nanchang, 330006, People’s Republic of ChinaCorrespondence: Jia-Ming LiuDepartment of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Donghu District, Nanchang, Jiangxi Province, People’s Republic of ChinaTel/Fax +86-791-86319815Email liujiamingdr@hotmail.comObjective: This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients.Methods: Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model.Results: A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients.Conclusion: This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.Keywords: prostate cancer, bone metastasis, machine learning, prediction model, SEER
format	article
author	Liu WC Li MX Qian WX Luo ZW Liao WJ Liu ZL Liu JM
author_facet	Liu WC Li MX Qian WX Luo ZW Liao WJ Liu ZL Liu JM
author_sort	Liu WC
title	Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
title_short	Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
title_full	Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
title_fullStr	Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
title_full_unstemmed	Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer
title_sort	application of machine learning techniques to predict bone metastasis in patients with prostate cancer
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/3a02e26962b042a5830373edf1b3bf91
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Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

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