A study on the predictive value of preoperative MRI-based radiomics for postoperative neurological function in patients with ossification of the posterior longitudinal ligament

作者：Baiyang Jiang, Qianxi Jin, Jiayang Yan, Xin Zhang, Fukai Li, Song Chen, Leyang Pan, Yuxin Cheng, Shaochun Xu, Xiang Wang, Yi Xiao, Shiyuan Liu · 发表于：BMC Medical Imaging · 年份：2026 · DOI：10.1186/s12880-026-02636-1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Musculoskeletal synovial abnormalities and treatments、Cervical and Thoracic Myelopathy

This study proposes to develop an integrative predictive model that synergistically combines quantitative radiomic features, established clinical risk parameters, and advanced machine learning methodologies to forecast postoperative neurological functional outcomes in patients diagnosed with cervical ossification of the posterior longitudinal ligament (OPLL). This retrospective cohort study recruited 113 consecutive patients with cervical OPLL who underwent surgical decompression at the Second Affiliated Hospital of Naval Medical University.Postoperative neurological outcomes were assessed over a 12-month follow-up period, with patients stratified into two groups based on the modified Japanese Orthopaedic Association (mJOA) score recovery rate. Radiomic features were extracted from preoperative T2-weighted MRI scans covering the C2–C7 spinal segments. After feature selection using least absolute shrinkage and selection operator (LASSO) logistic regression, eleven machine learning algorithms were employed to build radiomic signature models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, with the Hosmer-Lemeshow test assessing calibration. Clinically significant risk factors were identified through univariate and multivariate logistic regression analyses and subsequently incorporated into clinical prediction models. An integrated predictive framework was then developed by combining radiomic signatures with clinical risk factors, an...