Abstract
Background
The radiological assessment of neurovascular compression (NVC) is various regarding MRI techniques and assessing methods in patients with trigeminal neuralgia (TN), and the false-positive rate of MRI findings is not low. Better MRI techniques with the NVC assessing method are warranted to be determined. This study aims to investigate the diagnostic performance of 3D TOF MRA and 3D Fast Imaging Employing Steady-state Acquisition (FIESTA) with a novel NVC scoring system in TN patients.
Methods
Patients with confirmed TN who underwent MRI studies before microvascular decompression (MVD) were retrospectively included into the study. A new NVC scoring system based on the contact relationship of the trigeminal nerve and the vessel was performed to assess the NVC in the symptomatic and contralateral asymptomatic side. The radiological finding was correlated with the intraoperative result to figure out the diagnostic accuracy of MRI techniques. Besides, the comparison of both sides was performed to determine the radiological indicator of MVD.
Results
Seventy-three TN patients were recruited, and 146 trigeminal nerve sides were analyzed. For the symptomatic sides, 69 patients had surgically confirmed offending vessels, most of which was SCA, and the positive NVC rate was 95.5%. For the contralateral side, 33 patients have been found with NVC on MRI. The NVC score of the symptomatic side was significantly higher than that of asymptomatic sides (6.7 vs. 1.6; p < 0.001). The optimal cut-off value in predicting trigeminal neuralgia was found as NVC > 4 with sensitivity and specificity of 82.2% and 98.6%, respectively.
Conclusion
3D-TOF MRA and FIESTA enable a good diagnostic performance of NVC, and NVC score > 4 was identified to predict trigeminal neuralgia, suggestive of subsequent surgical treatment.
Trial registration:
The study has been retrospectively registered at the local ethical Institution Review Board (IRB) of Huzhou Central Hospital and Sir Run Run Shaw Hospital with the IRB number (20181108-01; Huzhou) and (20200423-43; SRRSH).