• Eur Spine J · Jun 2024

    A novel quantitative method to evaluate lumbar disc degeneration: MRI histogram analysis.

    • Zhaohui Li, Yurui Wu, Mengyang Liu, Dazhuang Miao, Di Zhang, and Wenyuan Ding.
    • Department of Spine Surgery, Hebei Medical University Third Hospital, 139 Ziqiang Road, Shijiazhuang, 050051, China.
    • Eur Spine J. 2024 Jun 1; 33 (6): 242024292420-2429.

    PurposeThis study aimed to use MRI histogram analysis to routine MRI sequences to evaluate lumbar disc degeneration (LDD), illustrate the correlation between this novel method and the traditional Pfirrmann classification method, and more importantly, perform comprehensive agreement analysis of MRI histogram analysis in various situations to evaluate its objectivity and stability.MethodsLumbar MRI images from 133 subjects were included in this study. LDD was classified into grades by Pfirrmann classification and was measured as peak separation value by MRI histogram analysis. Correlation analysis between the two methods was performed and cutoff values were determined. In addition, the agreement analysis of peak separation value was performed by intraclass correlation coefficient (ICC) in four scenarios, including inter-resolution, inter-observer, inter-regions of interest (ROI) and inter-slice.ResultsPeak separation values were strongly correlated with Pfirrmann grades (r = - 0.847). The inter-resolution agreements of peak separation value between original image resolution of 2304 × 2304 and compressed image resolutions (1152 × 1152, 576 × 576, 288 × 288) were good to excellent (ICCs were 0.916, 0.876 and 0.822), except 144 × 144 was moderate (ICC = 533). The agreements of inter-observer (ICC = 0.982) and inter-ROI (ICC = 0.915) were excellent. Compared with the mid-sagittal slice, the inter-slice agreements were good for the first adjacent slices (ICCs were 0.826 and 0.844), and moderate to good for the second adjacent slices (ICC = 0.733 and 0.753).ConclusionMRI histogram analysis, used in routine MRI sequences, demonstrated a strong correlation with Pfirrmann classification and good agreements in various scenarios, expanding the range of application and providing an effective, objective and quantitative tool to evaluate LDD.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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