Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics.
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 ac...
| Publicado en: | Neuroradiology Vol. 64; no. 3; pp. 603 - 610 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155281129&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155281129 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Mar2022 vid: 64 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155281129 155281129 155281129 10.1007/s00234-022-02900-5 155281129 ppf: 603 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics. aug: au: Mulford, Kellen L. Moen, Sean L. Grande, Andrew W. Nixdorf, Donald R. Van de Moortele, Pierre-Francois affil: Department of Radiology, University of Minnesota, Minneapolis, MN, USA sug: subj: Trigeminal Nerve Magnetic Resonance Imaging Trigeminal Neuralgia Therapy Human Prospective Studies Retrospective Design Radiosurgery Machine Learning Deep Learning ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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