Introduction: Trigeminal 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. Methods: 3D 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. Results: A 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. Conclusion: Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.
Bibliographical noteFunding Information:
This research was supported by National Institute of Biomedical Imaging and Bioengineering P41 EB027061 and P30 NS076408. Personnel performing this research were also supported by the National Institutes of Health’s National Center for Advancing Translational Sciences grants TL1R002493 and UL1TR002494.
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
- Machine learning
- Trigeminal neuralgia
- Trigeminal Nerve/diagnostic imaging
- Magnetic Resonance Imaging/methods
- Retrospective Studies
- Trigeminal Neuralgia/diagnostic imaging
PubMed: MeSH publication types
- Journal Article