Literature DB >> 31752520

Hippocampal and trigeminal nerve volume predict outcome of surgical treatment for trigeminal neuralgia.

Hayden Danyluk1,2, Esther Kyungsu Lee2, Scott Wong2, Samiha Sajida2, Robert Broad2, Matt Wheatley2, Cameron Elliott2, Tejas Sankar2.   

Abstract

BACKGROUND: Many medically-refractory trigeminal neuralgia patients are non-responders to surgical treatment. Few studies have explored how trigeminal nerve characteristics relate to surgical outcome, and none have investigated the relationship between subcortical brain structure and treatment outcomes.
METHODS: We retrospectively studied trigeminal neuralgia patients undergoing surgical treatment with microvascular decompression. Preoperative magnetic resonance imaging was used for manual tracing of trigeminal nerves and automated segmentation of hippocampus, amygdala, and thalamus. Nerve and subcortical structure volumes were compared between responders and non-responders and assessed for ability to predict postoperative pain outcome.
RESULTS: In all, 359 trigeminal neuralgia patients treated surgically from 2005-2018 were identified. A total of 34 patients met the inclusion criteria (32 with classic and two with idiopathic trigeminal neuralgia). Across all patients, thalamus volume was reduced ipsilateral compared to contralateral to the side of pain. Between responders and non-responders, non-responders exhibited larger contralateral trigeminal nerve volume, and larger ipsilateral and contralateral hippocampus volume. Through receiver-operator characteristic curve analyses, contralateral hippocampus volume correctly classified treatment outcome in 82% of cases (91% sensitive, 78% specific, p = 0.008), and contralateral nerve volume correctly classified 81% of cases (91% sensitive, 75% specific, p < 0.001). Binomial logistic regression analysis showed that contralateral hippocampus and contralateral nerve volumes together classified outcome with 84% accuracy (Nagelkerke R2 = 65.1).
CONCLUSION: Preoperative hippocampal and trigeminal nerve volume, measured on standard clinical magnetic resonance images, may predict early non-response to surgical treatment for trigeminal neuralgia. Treatment resistance in medically refractory trigeminal neuralgia may depend on the structural features of both the trigeminal nerve and structures involved in limbic components of chronic pain.

Entities:  

Keywords:  MRI; Trigeminal neuralgia; hippocampus; outcome prediction; trigeminal nerve; volumetry

Year:  2019        PMID: 31752520     DOI: 10.1177/0333102419877659

Source DB:  PubMed          Journal:  Cephalalgia        ISSN: 0333-1024            Impact factor:   6.292


  6 in total

1.  Factors affecting long-lasting pain relief after Gamma Knife radiosurgery for trigeminal neuralgia: a single institutional analysis and literature review.

Authors:  Lina R Barzaghi; Luigi Albano; Claudia Scudieri; Carmen R Gigliotti; Antonella Del Vecchio; Pietro Mortini
Journal:  Neurosurg Rev       Date:  2021-01-12       Impact factor: 3.042

2.  MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia.

Authors:  Hayden Danyluk; Abdullah Ishaque; Daniel Ta; Yee Hong Yang; B Matthew Wheatley; Sanjay Kalra; Tejas Sankar
Journal:  Front Neurol       Date:  2021-02-12       Impact factor: 4.003

3.  Leveraging high-resolution 7-tesla MRI to derive quantitative metrics for the trigeminal nerve and subnuclei of limbic structures in trigeminal neuralgia.

Authors:  Judy Alper; Alan C Seifert; Gaurav Verma; Kuang-Han Huang; Yael Jacob; Ameen Al Qadi; John W Rutland; Sheetal Patel; Joshua Bederson; Raj K Shrivastava; Bradley N Delman; Priti Balchandani
Journal:  J Headache Pain       Date:  2021-09-23       Impact factor: 7.277

4.  Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning.

Authors:  Xiuhong Ge; Luoyu Wang; Lei Pan; Haiqi Ye; Xiaofen Zhu; Qi Feng; Zhongxiang Ding
Journal:  Front Neurol       Date:  2022-04-08       Impact factor: 4.003

5.  The thalamus in trigeminal neuralgia: structural and metabolic abnormalities, and influence on surgical response.

Authors:  Hayden Danyluk; Jennifer Andrews; Rohit Kesarwani; Peter Seres; Robert Broad; B Matt Wheatley; Tejas Sankar
Journal:  BMC Neurol       Date:  2021-07-24       Impact factor: 2.474

6.  Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning.

Authors:  Jinghui Lin; Lei Mou; Qifeng Yan; Shaodong Ma; Xingyu Yue; Shengjun Zhou; Zhiqing Lin; Jiong Zhang; Jiang Liu; Yitian Zhao
Journal:  Front Neurosci       Date:  2021-12-10       Impact factor: 4.677

  6 in total

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