Zhang Zifeng, Li Ning, Ding Yi, Cheng Huilin
School of Medicine, Southeast University, Nanjing, China.
Department of Neurosurgery, Zhongda Hospital, Southeast University, Nanjing, China.
Eur Spine J. 2025 Mar;34(3):1164-1176. doi: 10.1007/s00586-024-08609-8. Epub 2024 Dec 14.
To construct a nomogram model based on magnetic resonance imaging (MRI) radiomics combined with clinical characteristics and evaluate its role and value in predicting the prognosis of patients with cervical spinal cord injury (cSCI).
In this study, we assessed the prognosis of 168 cSCI patients using the American Spinal Injury Association (ASIA) scale and the Functional Independence Measure (FIM) scale. The study involved extracting radiomics features using both manually defined metrics and features derived through deep learning via transfer learning methods from MRI sequences, specifically T1-weighted and T2-weighted images (T1WI & T2WI). The feature selection was performed employing the least absolute shrinkage and selection operator (Lasso) regression across both radiomics and deep transfer learning datasets. Following this selection process, a deep learning radiomics signature was established. This signature, in conjunction with clinical data, was incorporated into a predictive model. The efficacy of the models was appraised using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) to assess their diagnostic performance.
Comparing the effectiveness of the models by linking the AUC of each model, we chose the best-performance radiomics model with clinical model to create the final nomogram. Our analysis revealed that, in the testing cohort, the combined model achieved an AUC of 0.979 for the ASIA and 0.947 for the FIM. The training cohort showed more promising performance, with an AUC of 0.957 for ASIA and 1.000 for FIM. Furthermore, the calibration curve showed that the predicted probability of the nomogram was consistent with the actual incidence rate and the DCA curve validated its effectiveness as a prognostic tool in a clinical setting.
We constructed a combined model that can be used to help predict the prognosis of cSCI patients with radiomics and clinical characteristics, and further provided guidance for clinical decision-making by generating a nomogram.
构建基于磁共振成像（MRI）影像组学结合临床特征的列线图模型，并评估其在预测颈髓损伤（cSCI）患者预后中的作用和价值。
在本研究中，我们使用美国脊髓损伤协会（ASIA）量表和功能独立性测量（FIM）量表评估了168例cSCI患者的预后。该研究涉及通过手动定义的指标以及通过迁移学习方法从MRI序列（特别是T1加权和T2加权图像（T1WI和T2WI））中深度学习得出的特征来提取影像组学特征。采用最小绝对收缩和选择算子（Lasso）回归对影像组学和深度迁移学习数据集进行特征选择。在此选择过程之后，建立了深度学习影像组学特征。该特征与临床数据一起被纳入预测模型。使用受试者操作特征曲线（AUC）下的面积、校准曲线和决策曲线分析（DCA）来评估模型的诊断性能。
通过关联每个模型的AUC比较模型的有效性，我们选择了性能最佳的影像组学模型与临床模型来创建最终的列线图。我们的分析表明，在测试队列中，联合模型在ASIA量表上的AUC为0.979，在FIM量表上的AUC为0.947。训练队列表现出更有前景的性能，ASIA量表的AUC为0.957，FIM量表的AUC为1.000。此外，校准曲线表明列线图的预测概率与实际发生率一致，DCA曲线验证了其作为临床环境中预后工具的有效性。
我们构建了一个联合模型，可用于帮助预测具有影像组学和临床特征的cSCI患者的预后，并通过生成列线图为临床决策提供进一步指导。
