Radiomics nomogram based on MRI water imaging identifying symptomatic nerves of patients with primary trigeminal neuralgia: A preliminary study

Abstract

The study proposes a combined nomogram based on radiomics features from magnetic resonance neurohydrography and clinical features to identify symptomatic nerves in patients with primary trigeminal neuralgia. We retrospectively analyzed 140 patients with clinically confirmed trigeminal neuralgia. Out of these, 24 patients constituted the external validation set, while the remaining 116 patients contributed a total of 231 nerves, comprising 118 symptomatic nerves, and 113 normal nerves. Radiomics features were extracted from the MRI water imaging (t2-mix3d-tra-spair). Radiomics feature selection was performed using L1 regularization-based regression, while clinical feature selection utilized univariate analysis and multivariate logistic regression. Subsequently, radiomics, clinical, and combined models were developed by using multivariate logistic regression, and a nomogram of the combined model was drawn. The performance of nomogram in discriminating symptomatic nerves was assessed through the area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves. Clinical applications of the nomogram were further evaluated using decision curve analysis. Five clinical factors and 13 radiomics signatures were ultimately selected to establish predictive models. The AUCs in the training and validation cohorts were 0.77 (0.70–0.84) and 0.82 (0.72–0.92) with the radiomics model, 0.69 (0.61–0.77) and 0.66 (0.53–0.79) with the clinical model, 0.80 (0.74–0.87), and 0.85 (0.76–0.94) with the combined model, respectively. In the external validation set, the AUCs for the clinical, radiomics, and combined models were 0.70 (0.60–0.79), 0.78 (0.65–0.91), and 0.81 (0.70–0.93), respectively. The calibration curve demonstrated that the nomogram exhibited good predictive ability. Moreover, The decision curve analysis curve indicated shows that the combined model holds high clinical application value. The integrated model, combines radiomics features from magnetic resonance neurohydrography with clinical factors, proves to be effective in identify symptomatic nerves in trigeminal neuralgia. The diagnostic efficacy of the combined model was notably superior to that of the model constructed solely from conventional clinical features.