Development of a Dual‐Plane MRI‐Based Deep Learning Model to Assess the 1‐Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy

• Published in: Journal of magnetic resonance imaging Vol. 61; no. 5; pp. 2294 - 2307
• Main Authors: Wang, Kaifeng, Lin, Fabin, Liao, Zulin, Wang, Yongjiang, Zhang, Tingxin, Wang, Rui
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2025; Wiley Subscription Services, Inc
• Subjects: Adult; Age; Aged; Deep Learning; Diskectomy - methods; Female; Field strength; Humans; Intervertebral Disc Displacement - diagnostic imaging; Intervertebral Disc Displacement - surgery; Intervertebral discs; lumbar disc herniation; Lumbar Vertebrae - diagnostic imaging; Lumbar Vertebrae - surgery; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Males; Mathematical models; Microsurgery; Middle Aged; MRI; Orthopedics; Population studies; Postoperative Period; Reproducibility of Results; Retrospective Studies; Statistical analysis; Statistical models; Statistical tests; Treatment Outcome; tubular microdiscectomy; deep learning; tubular microdiscectomy; MRI; lumbar disc herniation
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29639

Background Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explored. Purpose/Hypothesis To evaluate whether integrating preoperative dual‐plane MRI‐based DL features with clinical features can assess 1‐year outcomes in TMD for LDH. Study Type Retrospective. Population/Subjects The study involved 548 patients who underwent TMD between January 2016 and January 2021. Training set (N = 305, mean age 51.85 ± 13.84 years, 56.4% male). Internal validation set (N = 131, mean age 51.85 ± 13.84 years, 54.2% male). External validation set (N = 112, mean age 51.54 ± 14.43 years, 50.9% male). Field Strength/Sequence 3 T MRI with sagittal and transverse T2‐weighted sequences (Fast Spin Echo). Assessment Ground truth labels were based on improvement rate in 1‐year Japanese Orthopaedic Association (JOA) scores. Information on 42 preoperative clinical features was collected. The largest protrusions were identified from T2 MRI by three clinicians and were used to train deep learning models (ResNet50, ResNet101, and ResNet152) to extract DL features. After feature selection, three models were built, namely, clinical, DL, and combined models. Statistical Tests Chi‐square or Fisher's exact tests was used for group comparisons. Quantitative differences were analyzed using the t‐test or Mann–Whitney U test. P‐values <0.05 were considered significant. Models were validated on internal and external datasets using metrics such as the area under the curve (AUC). Results The AUCs of the clinical models achieved 0.806 (internal) and 0.779 (external). ResNet152 performed best in three DL models, with AUCs of 0.858 (internal) and 0.834 (external). The combined model achieved AUCs of 0.889 (internal) and 0.857 (external). Data Conclusion A model combining preoperative dual‐plane MRI DL features and clinical features can assess 1‐year outcomes of TMD for LDH. Evidence Level 4 Technical Efficacy Stage 2