{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:50:13Z","timestamp":1780102213901,"version":"3.54.0"},"reference-count":30,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"\n Background<\/jats:title>\n The study aimed to assess the role of 18<\/jats:sup>F-fluorodeoxyglucose (FDG) PET\/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18<\/jats:sup>F-FDG PET\/CT were retrospectively enrolled. Clinical characteristics and PET\/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET\/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P<\/jats:italic>\u2005<\u20050.05).<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n ML models incorporating clinical and PET\/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.<\/jats:p>\n <\/jats:sec>","DOI":"10.1097\/mnm.0000000000001925","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T08:00:31Z","timestamp":1730793631000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["PET\/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma"],"prefix":"10.1097","author":[{"given":"Fanghu","family":"Wang","sequence":"first","affiliation":[{"name":"PET Center, Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"PET Center, Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyue","family":"Tan","sequence":"additional","affiliation":[{"name":"PET Center, Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wantong","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Yuan","sequence":"additional","affiliation":[{"name":"PET Center, Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Jiang","sequence":"additional","affiliation":[{"name":"PET Center, Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences)"},{"name":"Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 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