Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database

作者：Xu Zheng, Longqiang Shu, Shanyi Lin, Hanqiang Jin, Xiaoyu Wang, Ting Yuan · 发表于：Journal of orthopaedic surgery · 年份：2025 · DOI：10.1177/10225536251340113 · 研究领域：Bone Tumor Diagnosis and Treatments、Sarcoma Diagnosis and Treatment、Musculoskeletal synovial abnormalities and treatments

Purpose: The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has better overall performance than single computational approaches for clinical decision. The primary objective was to select prognostic variables and develop optimal ensemble learning algorithms for survival prediction and analyzing chemotherapy benefit in high-grade chondrosarcoma. The secondary objective included identifying specific patient groups with estimated survival benefit for guidance in chemotherapy strategies. Methods: The data of 1931 patients with chondrosarcoma from 2000 to 2019 were obtained from the Surveillance, Epidemiology, and End Results database to conduct the retrospective analysis. Among 468 patients with high-grade chondrosarcoma, cox proportional hazards models and random survival forests were used for feature selection. Ensemble learning and survival support vector machine with different kernel methods were developed and compared for their prognostic performance. Results: Ensemble learning outperformed the single models, with the concordance index reaching 0.764 (based on inverse probability of censoring weights) and the mean area under time-dependent receiver operating characteristic curve of 0.851. According to the ensemble model, overall survival generally improved in younger patients after chemotherapy. Age-stratified analysis revealed differential chemotherapy benefits across various clinical subgroups. Survival benefits were observed in:...