ArticleJournal of magnetic resonance imaging : JMRI2025
Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.
Article in Journal of magnetic resonance imaging : JMRI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Grading of glioma and prediction of IDH mutation status via longitudinal relaxation time in rotating frame mapping on 5-T magnetic resonance imaging: added value to amide proton transfer-weighted imaging.Quantitative imaging in medicine and surgery · 2026Article
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Abstract
backgroundAccording to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases. PURPOSE: To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images. STUDY TYPE: Retrospective. POPULATION: Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets. FIELD STRENGTH/SEQUENCES: 1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo). ASSESSMENT: The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value <80 msec). After measurement, the T1ρ values, phases, and levels of IVDs were input into the model as labels. STATISTICAL TESTS: Intraclass correlation coefficient, area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall (P < 0.05 was considered significant).
resultsIn the test dataset, the model achieved a mean average precision of 0.996 for detecting IVD levels. The diagnostic accuracy of the T1ρ-MR based IDD phases was 0.840 and the AUC was 0.871, the average AUC of 5-folds cross validation was 0.843. DATA
conclusionThe proposed deep learning model achieved the classification of T1ρ-MR based IDD phases from routine T1-MR images, which may provide a method to facilitate the application of T1ρ-MR in IDD. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.
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