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Wei J, Lu S, Liu W, Liu H, Feng L, Tao Y, Pu Z, Liu Q, Hu Z, Wang H, Li W, Kang W, Yin C, Feng Z. A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study. PeerJ 2023; 11:e16485. [PMID: 38130920 PMCID: PMC10734410 DOI: 10.7717/peerj.16485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/27/2023] [Indexed: 12/23/2023] Open
Abstract
Background The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making. Methods Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator. Results A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911-0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk. Conclusions In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.
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Affiliation(s)
- Jihu Wei
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Shijin Lu
- Centre for Translational Medical Research in Integrative Chinese and Western Medicine, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Wencai Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - He Liu
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Lin Feng
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Yizi Tao
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Zhanglin Pu
- Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Qiang Liu
- Orthopedic Department, Xianyang Central Hospital, Xianyang, Shannxi, China
| | - Zhaohui Hu
- Department of Spine Surgery, Liuzhou People’s Hospital, Liuzhou, Guangxi, China
| | - Haosheng Wang
- Department of Orthopaedics, The Second Hospital of Jilin University, Changchun, China
| | - Wenle Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xianmen, Fujian, China
| | - Wei Kang
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
- Department of Mathematics, Physics and Interdisciplinary Studies, Guangzhou Laboratory (Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Zhe Feng
- Joint & Sports Medicine Surgery Division, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China
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