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Traditional monitoring methods are insufficient for early detection. This study aims to develop and validate an interpretable machine learning (ML) model using multi\u2010dimensional data from electronic medical records (EMR) to predict the early risk of pulmonary metastasis in high\u2010grade conventional osteosarcoma patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included data from 522 high\u2010grade conventional osteosarcoma patients. After rigorous feature selection, 12 independent predictive factors were identified: prothrombin time (PT), international normalized ratio (INR), fibrinogen, globulin, prognostic nutritional index (PNI), eosinophil percentage, monocyte percentage, neutrophil percentage, monocyte count, maximum tumor diameter, gender, and amputation status. Eleven ML models were constructed and compared, with Shapley Additive Explanations (SHAP) analysis applied to enhance model interpretability and clinical transparency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The gradient boosting model exhibited superior performance, achieving an area under the curve (AUC) of 0.891 in the training set and 0.742 in the independent test set. SHAP analysis revealed that tumor maximum diameter was the most influential predictor of pulmonary metastasis risk, while inflammation and coagulation\u2010related indicators also demonstrated significant contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The gradient boosting model effectively predicts the early risk of pulmonary metastasis in high\u2010grade conventional osteosarcoma and provides interpretable risk factors. This model shows potential as a clinical decision support tool, facilitating personalized risk management and precision medicine, aiming to improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72209","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T01:34:24Z","timestamp":1789954464000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of an Interpretable Machine Learning Model for Predicting Early Pulmonary Metastasis Risk in Osteosarcoma"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-4605","authenticated-orcid":false,"given":"Jianhua","family":"Mu","sequence":"first","affiliation":[{"name":"Department of Orthopedics, Orthopeadic Research Institute, West China Hospital, West China Medical School Sichuan University  Chengdu Sichuan China"},{"name":"Model Worker and Craftsman 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