MRI-based radiomics features uncover the micro-change of dorsal root ganglia lesion for patients with post-herpetic neuralgia

ObjectiveTo create and authenticate MRI-based radiomic signatures to identify dorsal root ganglia (DRG) lesions in post-herpetic neuralgia (PHN) patients generalizable and interpretable.MethodThis prospective diagnostic study was conducted between January 2021 and February 2022. Lesioned DRG in patients with PHN and normal DRG in age-, sex-, height-, and weight-matched healthy controls were selected for assessment and divided into two groups (8:2) randomly: training and testing sets. The least absolute shrinkage and selection operator algorithm was employed to generate feature signatures and construct a model, followed by the assessment of model efficacy using the area under the curve (AUC) of the receiver operating characteristic (ROC), as well as sensitivity and specificity metrics.ResultsThe present investigation involved 30 patients diagnosed with postherpetic neuralgia (PHN), consisting of 18 males and 12 females (mean age 60.70 +/- 10.18 years), as well as 30 healthy controls, comprising 18 males and 12 females (mean age 58.13 +/- 10.54 years). A total of 98 DRG were randomly divided into two groups (8:2), namely a training set (n = 78) and a testing set (n = 20). Five radiomic features were chosen to construct the models. In the training dataset, the area under the curve (AUC) was 0.847, while the sensitivity and specificity were 71.79 and 97.44%, respectively. In the test dataset, the AUC was 0.87, and the sensitivity and specificity were 80.00 and 100.00%, respectively.ConclusionAn MRI-based radiomic signatures model has the capacity to uncover the micro-change of damaged DRG in individuals afflicted with postherpetic neuralgia.

Automated summary

This study aimed to create and authenticate MRI-based radiomic signatures for identifying DRG lesions in PHN patients. The study used a prospective diagnostic approach and divided the patients into training and testing sets to assess the efficacy of the model. The results showed promising sensitivity and specificity in detecting DRG lesions.