The Affiliated Shunde Hospital of Jinan University, Foshan, Guangdong Province 528305, PR China.
Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University & Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, PR China.
Seizure. 2024 Jul;119:17-27. doi: 10.1016/j.seizure.2024.04.021. Epub 2024 Apr 25.
To establish and validate a novel nomogram based on clinical characteristics and [F]FDG PET radiomics for the prediction of postsurgical seizure freedom in patients with temporal lobe epilepsy (TLE).
234 patients with drug-refractory TLE patients were included with a median follow-up time of 24 months after surgery. The correlation coefficient redundancy analysis and LASSO Cox regression were used to characterize risk factors. The Cox model was conducted to develop a Clinic-PET nomogram to predict the relapse status in the training set (n = 171). The nomogram's performance was estimated through discrimination, calibration, and clinical utility. The prognostic prediction model was validated in the test set (n = 63).
Eight radiomics features were selected to assess the radiomics score (radscore) of the operation side (Lat_radscore) and the asymmetric index (AI) of the radiomics score (AI_radscore). AI_radscor, Lat_radscor, secondarily generalized seizures (SGS), and duration between seizure onset and surgery (Durmon) were significant predictors of seizure-free outcomes. The final model had a C-index of 0.68 (95 %CI: 0.59-0.77) for complete freedom from seizures and time-dependent AUROC was 0.65 at 12 months, 0.65 at 36 months, and 0.59 at 60 months in the test set. A web application derived from the primary predictive model was displayed for economic and efficient use.
A PET-based radiomics nomogram is clinically promising for predicting seizure outcomes after temporal lobe epilepsy surgery.
建立并验证一种基于临床特征和 [F]FDG PET 放射组学的新列线图，用于预测颞叶癫痫（TLE）患者手术后无癫痫发作的情况。
共纳入 234 例药物难治性 TLE 患者，术后中位随访时间为 24 个月。采用相关系数冗余分析和 LASSO Cox 回归分析来确定风险因素。采用 Cox 模型建立用于预测训练集（n=171）中复发状态的 Clinic-PET 列线图。通过判别、校准和临床实用性来评估该列线图的性能。在测试集（n=63）中验证了该预测模型。
选择 8 个放射组学特征来评估术侧的放射组学评分（Lat_radscore）和放射组学评分的不对称指数（AI_radscore）。AI_radscor、Lat_radscor、继发性全面性发作（SGS）和癫痫发作到手术的时间间隔（Durmon）是预测无癫痫发作结局的显著预测因子。最终模型对完全无癫痫发作的 C 指数为 0.68（95%CI：0.59-0.77），在测试集中，12 个月、36 个月和 60 个月时的时间依赖性 AUROC 分别为 0.65、0.65 和 0.59。从主预测模型中衍生出一个网络应用程序，以便于经济高效地使用。
基于 PET 的放射组学列线图在预测颞叶癫痫手术后的癫痫发作结局方面具有临床应用前景。
