Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.

Abstract

Background: According 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).

Results: In 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 conclusion: The 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.