Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, PR China.
Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, PR China.
Acta Radiol. 2022 Jan;63(1):100-109. doi: 10.1177/0284185120983971. Epub 2021 Jan 7.
Multiple neurovascular contacts in patients with vascular compressive trigeminal neuralgia often challenge the diagnosis of responsible contacts.
To analyze the magnetic resonance imaging (MRI) features of responsible contacts and establish a predictive model to accurately pinpoint the responsible contacts.
Sixty-seven patients with unilateral trigeminal neuralgia were enrolled. A total of 153 definite contacts (45 responsible, 108 non-responsible) were analyzed for their MRI characteristics, including neurovascular compression (NVC) grading, distance from pons to contact (D), vascular origin of compressing vessels, diameter of vessel (D) and trigeminal nerve (D) at contact. The MRI characteristics of the responsible and non-responsible contacts were compared, and their diagnostic efficiencies were further evaluated using a receiver operating characteristic (ROC) curve. The significant MRI features were incorporated into the logistics regression analysis to build a predictive model for responsible contacts.
Compared with non-responsible contacts, NVC grading and arterial compression ratio (84.44%) were significantly higher, D was significantly lower at responsible contacts ( < 0.001, 0.002, and 0.033, respectively). NVC grading had a highest diagnostic area under the ROC curve (AUC) of 0.742, with a sensitivity of 64.44% and specificity of 75.00%. The logistic regression model showed a higher diagnostic efficiency, with an AUC of 0.808, sensitivity of 88.89%, and specificity of 62.04%.
Contact degree and position are important MRI features in identifying the responsible contacts of the trigeminal neuralgia. The logistic predictive model based on D, NVC grading, and vascular origin can qualitatively improve the prediction of responsible contacts for radiologists.
血管压迫性三叉神经痛患者常有多个神经血管接触，这常常使责任接触的诊断受到挑战。
分析责任接触的磁共振成像（MRI）特征，并建立一个预测模型，以准确确定责任接触。
共纳入 67 例单侧三叉神经痛患者，共分析了 153 个确定的接触点（45 个责任接触点，108 个非责任接触点）的 MRI 特征，包括神经血管压迫（NVC）分级、从脑桥到接触点的距离（D）、压迫血管的血管起源、血管（D）和三叉神经（D）在接触点处的直径。比较了责任和非责任接触点的 MRI 特征，并进一步通过受试者工作特征（ROC）曲线评估其诊断效率。将显著的 MRI 特征纳入逻辑回归分析，建立责任接触点的预测模型。
与非责任接触点相比，责任接触点的 NVC 分级和动脉压迫比（84.44%）显著更高，D 显著更低（均 < 0.001、0.002 和 0.033）。NVC 分级的 ROC 曲线下面积（AUC）最高为 0.742，敏感性为 64.44%，特异性为 75.00%。逻辑回归模型显示出更高的诊断效率，AUC 为 0.808，敏感性为 88.89%，特异性为 62.04%。
接触程度和位置是识别三叉神经痛责任接触点的重要 MRI 特征。基于 D、NVC 分级和血管起源的逻辑预测模型可以定性地提高放射科医生对责任接触点的预测能力。
