Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.

中文翻译：

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基于高分辨率磁共振成像的放射组学特征有助于在脑静脉窦血栓形成患者中选择血管内候选药物

脑静脉窦血栓（CVST）可导致窦阻塞和狭窄，并可能造成致命后果。高分辨率磁共振成像 (HRMRI) 可以定性诊断 CVST，但对于抗凝治疗难治且可能受益于血管内治疗 (EVT) 的患者缺乏定量筛查方法。因此，在本研究中，我们使用从 HRMRI 中提取的放射组学特征 (RF) 来构建机器学习模型，以预测对药物治疗的反应并确定 EVT 的适当性。从三维 T1 加权运动敏感驱动平衡 (MSDE)、T2 加权 MSDE、T1 对比度和 T1 对比度 MSDE 序列中提取 RF，以构建放射组学特征和支持向量机 (SVM) 模型来预测疗效标准药物治疗的认识和 EVT 的必要性。我们在一项前瞻性队列研究中回顾性纳入了 53 例 CVST 患者，其中 14 例在标准药物治疗失败后接受了 EVT。选择了 13 个 RF 来构建 RF 签名和 CVST-SVM 模型。在验证数据集中，RF 特征模型的灵敏度、特异性和曲线下面积性能分别为 0.833、0.937 和 0.977。放射组学评分与症状出现的天数、血脂异常病史、吸烟、纤维蛋白降解产物和 D-二聚体水平相关。验证集中 CVST-SVM 模型的敏感性、特异性和曲线下面积分别为 0.917、0.969 和 0.992。使用从 HRMRI 中提取的 RF 进行训练的 CVST-SVM 模型优于 RF 特征模型，可以帮助医生预测患者对药物治疗的反应并识别可能需要 EVT 的患者。