{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T01:35:06Z","timestamp":1792719306015,"version":"4.5.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82172011"],"award-info":[{"award-number":["82172011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We analyzed magnetic resonance imaging (MRI) and radiomics labels from tuberculous spondylitis (TBS) and brucella spondylitis (BS) to build machine learning models that differentiate TBS from BS and culture-positive TBS (TBS(+)) from culture-negative TBS (TBS(\u2212).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(\u2212). Radiomics labels were extracted from T2-weighted fat-suppression images. MRI labels were analyzed via logistic regression (LR); radiomics labels were analyzed by t-tests, SelectKBest, and least absolute shrinkage and selection operator (LASSO). Random forest (RF) and support vector machine (SVM) models were established using radiomics or joint (radiomics+MRI) labels. Models were evaluated by receiver operating characteristic curves, areas under the curve (AUCs), decision curve analysis (DCA), and Hosmer\u2013Lemeshow tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>When joint-label models were used to compare BS vs TBS(+) and BS vs TBS(\u2212) groups, SVM AUCs were 0.904 and 0.944, respectively, whereas RF AUCs were 0.950 and 0.947, respectively; these were higher than the AUCs of the MRI label-based LR model. DCA showed that radiomics-based machine learning models had a greater net benefit; Hosmer\u2013Lemeshow tests demonstrated good prediction consistency for all models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics can help distinguish TBS from BS and TBS(+) from TBS(\u2212).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605231195156","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T16:32:16Z","timestamp":1693585936000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":25,"title":["MRI radiomics-based evaluation of tuberculous and brucella spondylitis"],"prefix":"10.1177","volume":"51","author":[{"given":"Wenhui","family":"Wang","sequence":"first","affiliation":[{"name":"Shanxi Medical University"},{"name":"Department of MR, the Second Hospital of Shanxi Medical University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichang","family":"Fan","sequence":"additional","affiliation":[{"name":"Shanxi Medical University"},{"name":"Department of MR, the Second Hospital of Shanxi Medical University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3864-1420","authenticated-orcid":false,"given":"Junping","family":"Zhen","sequence":"additional","affiliation":[{"name":"Department of MR, the Second Hospital of Shanxi Medical University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.03.052"},{"key":"e_1_3_3_3_2","first-page":"2935","article-title":"[Clinical value of MRI in the diagnosis of Brucellosis spondylitis].","volume":"99","author":"Li HX","year":"2019","unstructured":"Li HX, Wang Q, Zhang JZ, Li XH, Feng L, Lei XW. 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