{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T20:37:26Z","timestamp":1791405446464,"version":"4.3.3"},"reference-count":41,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Orthop Surg (Hong Kong)"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    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.\n                    <jats:bold>Methods:<\/jats:bold>\n                    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.\n                    <jats:bold>Results:<\/jats:bold>\n                    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: Age \u2264 10 with dedifferentiated chondrosarcoma, amputation, local surgical treatment, absence of distant metastasis, or grade III tumor; Age \u2264 20 who were male with clear cell chondrosarcoma, non-axial primary sites, or no radiotherapy; Age \u2264 30 who were female with primary site at pelvis\/limb, received radiotherapy, extension beyond periosteum, further extension, or distant metastasis; Age\u226440 with chondrosarcoma NOS (including mesenchymal, juxtacortical and classical chondrosarcoma); Age \u2264 50 with grade IV tumor or no surgery received.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    Ensemble learning algorithms demonstrate outstanding overall performance in prognostic assessment of high-grade chondrosarcoma and identification of age-specific factors associated with chemotherapy benefit for tailored chemotherapy strategy.\n                  <\/jats:p>","DOI":"10.1177\/10225536251340113","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T01:04:28Z","timestamp":1746839068000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["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"],"prefix":"10.1177","volume":"33","author":[{"given":"Xu","family":"Zheng","sequence":"first","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longqiang","family":"Shu","sequence":"additional","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanyi","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanqiang","family":"Jin","sequence":"additional","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-9738","authenticated-orcid":false,"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Yuan","sequence":"additional","affiliation":[{"name":"Shanghai Sixth People\u2019s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/corr.0000000000002011"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.suronc.2018.05.009"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2021.08.1995"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1097\/cco.0000000000000456"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.5435\/jaaos-d-20-01188"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.4143\/crt.2014.135"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.12659\/msm.911673"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdt374"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1200\/jop.2015.009944"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2017.75.1743"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/jor.24463"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/623746"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/9069272"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1097\/coc.0000000000000590"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-018-4741-7"},{"key":"e_1_3_5_17_2","unstructured":"Overview of the SEER program. 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