• Xiaonan Yang, Na Tian, Yuzhu Zhang, Chuanping Gao, Dapeng Hao, Jie Li, Chuanli Zhou, and Jiufa Cui.
• Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, China.
• Eur Spine J. 2025 Feb 8.

PurposeThis study aimed to develop and validate a deep learning radiomics nomogram (DLRN) to differentiate between tuberculous spondylitis (TS) and pyogenic spondylitis (PS) using contrast-enhanced MRI (CE-MRI).MethodsA retrospective approach was employed, enrolling patients diagnosed with TS or PS based on pathological examination at two centers. Clinical features were evaluated to establish a clinical model. Radiomics and deep learning (DL) features were extracted from contrast-enhanced T1-weighted images and subsequently fused. Following feature selection, radiomics, DL, combined DL-radiomics (DLR), and a deep learning radiomics nomogram (DLRN) were developed to differentiate TS from PS. Performance was assessed using metrics including the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).ResultsA total of 147 patients met the study criteria. Center 1 comprised the training cohort with 102 patients (52 TS and 50 PS), while Center 2 served as the external test cohort with 45 patients (17 TS and 28 PS). The DLRN model exhibited the highest diagnostic accuracy, achieving an AUC of 0.994 (95% CI: 0.983-1.000) in the training cohort and 0.859 (95% CI: 0.744-0.975) in the external test cohort. Calibration curves indicated good agreement for DLRN, and decision curve analysis (DCA) demonstrated it provided the greatest clinical benefit.ConclusionThe CE-MRI-based DLRN showed robust diagnostic capability for distinguishing between TS and PS in clinical practice.© 2025. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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