Qin Siyuan, Qu Ruomu, Liu Ke, Yan Ruixin, Zhao Weili, Xu Jun, Zhang Enlong, Zhou Feifei, Lang Ning
Department of Radiology, Peking University Third Hospital, Beijing, China.
Department of Spinal Surgery, Peking University Third Hospital, Beijing, China.
Neurospine. 2025 Mar;22(1):144-156. doi: 10.14245/ns.2448846.423. Epub 2025 Mar 31.
This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery.
This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression.
Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model's predictions, enhancing clinical interpretability.
Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.
本研究探讨放射组学预测颈椎后路手术后后纵韧带骨化（OPLL）术后进展的潜力。
这项回顾性研究纳入了2006年10月至2022年9月期间在北京大学第三医院被诊断为OPLL的473例患者。患者接受了后路脊柱手术，且至少有两次间隔至少1年的计算机断层扫描（CT）检查。OPLL进展定义为年增长率超过7.5%。从OPLL病变的术前CT图像中提取放射组学特征，然后使用相关系数分析和最小绝对收缩与选择算子进行特征选择，并使用主成分分析进行降维。单变量分析确定了构建临床模型的显著临床变量。开发了包括Rad评分模型、临床模型和联合模型在内的逻辑回归模型，以预测OPLL进展。
在473例患者中，191例（40.4%）出现OPLL进展。在测试集上，结合了Rad评分和临床变量的联合模型（受试者操作特征曲线下面积[AUC]=0.751）优于仅放射组学模型（AUC=0.693）和临床模型（AUC=0.620）。校准曲线显示预测概率与观察结果之间具有良好的一致性，决策曲线分析证实了联合模型的临床实用性。SHAP（SHapley加性解释）分析表明，Rad评分和年龄是模型预测的关键因素，提高了临床可解释性。
放射组学与临床变量相结合，为评估颈椎OPLL术后进展风险提供了一种有价值的预测工具，支持更个性化的治疗策略。需要进行前瞻性、多中心验证，以确认该模型在更广泛临床环境中的实用性。
