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We hope to deeply mine the utility of raiomics data in the prognosis of MM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We extensively explored the predictive ability and clinical decision-making ability of different combination image data of PET, CT, clinical parameters and six machine learning algorithms, Cox proportional hazards model (Cox), linear gradient boosting models based on Cox\u2019s partial likelihood (GB-Cox), Cox model by likelihood based boosting (CoxBoost), generalized boosted regression modelling (GBM), random forests for survival model (RFS) and support vector regression for censored data model (SVCR). And the model evaluation methods include Harrell concordance index, time dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We finally confirmed 5 PET based features, and 4 CT based features, as well as 6 clinical derived features significantly related to progression free survival (PFS) and we included them in the model construction. In various modalities combinations, RSF and GBM algorithms significantly improved the accuracy and clinical net benefit of predicting prognosis compared with other algorithms. For all combinations of various modalities based models, single-modality PET based prognostic models\u2019 performance was outperformed baseline clinical parameters based models, while the performance of models of PET and CT combined with clinical parameters was significantly improved in various algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p><jats:sup>18<\/jats:sup>F\u2011FDG PET\/CT based radiomics models implemented with machine learning algorithms can significantly improve the clinical prediction of progress and increased clinical benefits providing prospects for clinical prognostic stratification for precision treatment as well as new research areas.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-01033-2","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T16:02:48Z","timestamp":1687881768000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["18F\u2011FDG PET\/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma"],"prefix":"10.1186","volume":"23","author":[{"given":"Haoshu","family":"Zhong","sequence":"first","affiliation":[]},{"given":"Delong","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Junhao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaomin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chunlan","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"3","key":"1033_CR1","first-page":"37","volume":"6","author":"L Raimondi","year":"2020","unstructured":"Raimondi L, De Luca A, Giavaresi G, et al. 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