{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T09:45:49Z","timestamp":1791798349039,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Surgical Oncology"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Upper extremity soft tissue sarcomas (UE STS) present unique management challenges; existing prognostic nomograms were developed from mixed anatomic cohorts. This study develops anatomically specific machine learning (ML) models to predict UE\u2010STS outcomes and benchmarks them against established tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Time\u2010to\u2010event models for local recurrence (LR), distant metastasis (DM), and overall survival (OS) were developed in 188 patients treated at a single sarcoma center, treating death as a competing risk. Five survival algorithms were compared within a nested cross\u2010validation framework. Discrimination was benchmarked against the Sarculator, PERSARC, and MSKCC nomograms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Five\u2010year event rates were 24% (LR), 20% (DM), and 13% (death) over a median follow\u2010up of 3.9 years. Best\u2010performing models achieved competing\u2010risks concordance indices of 0.79 (LR), 0.67 (DM), and 0.75 (OS). Parsimonious models performed comparably. Discrimination was similar to Sarculator and PERSARC and higher than the MSKCC nomogram for LR (C\u2010index 0.90 vs. 0.68). Predicted\u2010risk groups separated clearly on cumulative\u2010incidence and survival curves (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results demonstrate the feasibility and potential value of anatomy\u2010specific, time\u2010to\u2010event risk modeling in UE STS, achieving discrimination comparable to established nomograms despite a smaller cohort. Multicenter cohorts and multidimensional data may strengthen future efforts. External validation is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jso.70422","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T08:56:26Z","timestamp":1791795386000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Based Recurrence and Survival Prediction in a Large Institutional Upper Extremity Soft Tissue Sarcoma Cohort"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3164-6133","authenticated-orcid":false,"given":"Kameel","family":"Khabaz","sequence":"first","affiliation":[{"name":"David Geffen School of Medicine at University of California Los Angeles Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2096-1880","authenticated-orcid":false,"given":"Nicole J.","family":"Newman\u2010Hung","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiwen","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Pham","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Health Services Research at University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-5050","authenticated-orcid":false,"given":"Nicholas J.","family":"Jackson","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Health Services Research at University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5168-070X","authenticated-orcid":false,"given":"William","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3338-5878","authenticated-orcid":false,"given":"Nicholas M.","family":"Bernthal","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6240-5981","authenticated-orcid":false,"given":"Lauren E.","family":"Wessel","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery University of California Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"C. 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