MRI radiomics-based evaluation of tuberculous and brucella spondylitis

Abstract

Objectives 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(−). Methods This retrospective study included 56 patients with BS, 63 patients with TBS(+) and 71 patients with TBS(−). 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–Lemeshow tests. Results When joint-label models were used to compare BS vs TBS(+) and BS vs TBS(−) 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–Lemeshow tests demonstrated good prediction consistency for all models. Conclusions Radiomics can help distinguish TBS from BS and TBS(+) from TBS(−).