Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging

Qiuju Fan; Hui Tan; Zhongxu Zhang; Xingui Feng; Nan Yu; Yong Yu; Shaoyu Wang; Guangming Ma

doi:10.1016/j.mri.2025.110320

Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging

Magn Reson Imaging. 2025 Jan 3:117:110320. doi: 10.1016/j.mri.2025.110320. Online ahead of print.

Authors

Qiuju Fan¹, Hui Tan², Zhongxu Zhang¹, Xingui Feng¹, Nan Yu¹, Yong Yu¹, Shaoyu Wang³, Guangming Ma⁴

Affiliations

¹ Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China.
² Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China; The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
³ MR Senior Scientific Marketing Specialist, Siemens Healthineers, Shanghai, China.
⁴ Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China. Electronic address: 782121989@qq.com.

PMID: 39756666
DOI: 10.1016/j.mri.2025.110320

Abstract

Objective: To develop a novel combined nomogram based on 3D multi-echo Dixon (qDixon), magnetization transfer imaging (MTI) and clinical risk factors for the diagnosis of osteoporosis.

Materials and methods: A total of 287 subjects who underwent MR examination with qDixon and MTI sequences participated in this study. These participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. We extracted and analyzed the bone marrow fat fraction (FF) and magnetization transfer ratio (MTR) of L1 ∼ 3 vertebrae, along with clinical data. Univariate and multivariate logistic regression analyses was used to assess independent predictors of OP in the training cohort. We established a diagnostic nomogram and evaluated its performance in terms of discrimination, calibration, and clinical value using the receiver operating characteristic curve (ROC) and calibration curve. Decision curve analysis (DCA) was performed to determine the clinical validity of the nomogram by measuring the net benefits at different threshold probabilities.

Results: Gender, age, FF, and MTR (all P﹤0.05) emerged as independent indicators for diagnosing osteoporosis. The AUCs for the FF, MTR, FF + MTR, and nomogram models were 0.842, 0.903, 0.923, and 0.941, respectively, in the training cohort and 0.779, 0.872, 0.901, and 0.929, respectively, in the validation cohort. The nomogram model exhibited good calibration and discrimination. DCA revealed that the nomogram model yielded a higher net benefit than the FF and MTR models.

Conclusion: The nomogram model, integrating qDixon, MTI, and clinical parameters, could serve as a reliable tool for diagnosing the individual risk for the osteoporosis in the elderly.

Keywords: Magnetic resonance imaging; Magnetization transfer; Nomogram; Osteoporosis; qDixon.