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Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>486 patients (LM\u2009=\u2009200, non-LM\u2009=\u2009286) with histologically proven OS were retrospectively analyzed and divided into a training set (n\u2009=\u2009389) and a validation set (n\u2009=\u200997). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (<jats:italic>t<\/jats:italic><jats:sub>radscore<\/jats:sub> = -5.829, <jats:italic>\u03c7<\/jats:italic><jats:sup><jats:italic>2<\/jats:italic><\/jats:sup><jats:sub>ALP<\/jats:sub>\u2009=\u200997.137, <jats:italic>t<\/jats:italic><jats:sub>size<\/jats:sub> = -3.437, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR]\u2009=\u20097.272, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC\u2009=\u20090.807, ACC\u2009=\u20090.784).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-00991-x","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T17:02:49Z","timestamp":1679590969000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma"],"prefix":"10.1186","volume":"23","author":[{"given":"Ping","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwen","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"issue":"5","key":"991_CR1","first-page":"6228","volume":"16","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yang J, Zhao N, et al. 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