Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning

Xiuhong Ge; Luoyu Wang; Lei Pan; Haiqi Ye; Xiaofen Zhu; Qi Feng; Zhongxiang Ding

doi:10.3389/fneur.2022.862973

Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning

Front Neurol. 2022 Apr 8:13:862973. doi: 10.3389/fneur.2022.862973. eCollection 2022.

Authors

Xiuhong Ge¹, Luoyu Wang^{1

2}, Lei Pan¹, Haiqi Ye¹, Xiaofen Zhu¹, Qi Feng¹, Zhongxiang Ding^{1

3}

Affiliations

¹ Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
³ Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Zhejiang University School of Medicine, Hangzhou, China.

Abstract

Purpose: Neurovascular compression (NVC) is considered as the main factor leading to the classical trigeminal neuralgia (CTN), and a part of idiopathic TN (ITN) may be caused by NVC (ITN-nvc). This study aimed to explore the risk factors for unilateral CTN or ITN-nvc (UC-ITN), which have bilateral NVC, using machine learning (ML).

Methods: A total of 89 patients with UC-ITN were recruited prospectively. According to whether there was NVC on the unaffected side, patients with UC-ITN were divided into two groups. All patients underwent a magnetic resonance imaging (MRI) scan. The bilateral cisternal segment of the trigeminal nerve was manually delineated, which avoided the offending vessel (Ofv), and the features were extracted. Dimensionality reduction, feature selection, model construction, and model evaluation were performed step-by-step.

Results: Four textural features with greater weight were selected in patients with UC-ITN without NVC on the unaffected side. For UC-ITN patients with NVC on the unaffected side, six textural features with greater weight were selected. The textural features (rad_score) showed significant differences between the affected and unaffected sides (p < 0.05). The nomogram model had optimal diagnostic power, and the area under the curve (AUC) in the training and validation cohorts was 0.76 and 0.77, respectively. The Ofv and rad_score were the risk factors for UC-ITN according to nomogram.

Conclusion: Besides NVC, the texture features of trigeminal-nerve cisternal segment and Ofv were also the risk factors for UC-ITN. These findings provided a basis for further exploration of the microscopic etiology of UC-ITN.

Keywords: classical trigeminal neuralgia; idiopathic trigeminal neuralgia; machine learning; neurovascular compression; risk factors.