ArticleNeuroradiology2022
Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Artificial intelligence in trigeminal neuralgia: trigeminal nerve segmentation and neurovascular conflict detection: a systematic review and meta-analysis.Neurosurgical review · 2026Pooled it
- Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review.Frontiers in digital health · 2026Pooled it
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
- Magnetic Resonance Imaging-Based Radiomic Signatures for the Diagnosis of Zoster-Associated Pain: A Quantitative Imaging Approach.Pain and therapy · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Distinguishing symptomatic and asymptomatic trigeminal nerves through radiomics and deep learning: A microstructural study in idiopathic TN patients and asymptomatic control group.Neuroradiology · 2025Article
- Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study.European radiology · 2024Article
- Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics.BMC medical imaging · 2024Article
- Article
- Analyzing the risk factors of unilateral trigeminal neuralgia under neurovascular compression.Frontiers in human neuroscience · 2024Article
- The role of artificial intelligence in the management of trigeminal neuralgia.Frontiers in surgery · 2023Review
- Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia.Frontiers in neuroscience · 2023Article
- Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
introductionTrigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves.
methods3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability.
resultsA total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively.
conclusionOverall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.