Regional brain morphology predicts pain relief in trigeminal neuralgia
Background: Trigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief follo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158221001509 |
| _version_ | 1818882665651109888 |
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| author | Peter Shih-Ping Hung Alborz Noorani Jia Y. Zhang Sarasa Tohyama Normand Laperriere Karen D. Davis David J. Mikulis Frank Rudzicz Mojgan Hodaie |
| author_facet | Peter Shih-Ping Hung Alborz Noorani Jia Y. Zhang Sarasa Tohyama Normand Laperriere Karen D. Davis David J. Mikulis Frank Rudzicz Mojgan Hodaie |
| author_sort | Peter Shih-Ping Hung |
| collection | DOAJ |
| description | Background: Trigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief following surgical interventions can minimize unnecessary surgeries and thus are of substantial benefit for patients and clinicians. Purpose: To determine if pre-treatment regional brain morphology-based machine learning models can prognosticate 1 year response to Gamma Knife radiosurgery for trigeminal neuralgia. Methods: We used a data-driven approach that combined retrospective structural neuroimaging data and support vector machine-based machine learning to produce robust multivariate prediction models of pain relief following Gamma Knife radiosurgery for trigeminal neuralgia. Surgical response was defined as ≥ 75% pain relief 1 year post-treatment. We created two prediction models using pre-treatment regional brain gray matter morphology (cortical thickness or surface area) to distinguish responders from non-responders to radiosurgery. Feature selection was performed through sequential backwards selection algorithm. Model out-of-sample generalizability was estimated via stratified 10-fold cross-validation procedure and permutation testing. Results: In 51 trigeminal neuralgia patients (35 responders, 16 non-responders), machine learning models based on pre-treatment regional brain gray matter morphology (14 regional surface areas or 13 regional cortical thicknesses) provided robust a priori prediction of surgical response. Cross-validation revealed the regional surface area model was 96.7% accurate, 100.0% sensitive, and 89.1% specific while the regional cortical thickness model was 90.5% accurate, 93.5% sensitive, and 83.7% specific. Permutation testing revealed that both models performed beyond pure chance (p < 0.001). The best predictor for regional surface area model and regional cortical thickness model was contralateral superior frontal gyrus and contralateral isthmus cingulate gyrus, respectively. Conclusions: Our findings support the use of machine learning techniques in subsequent investigations of chronic neuropathic pain. Furthermore, our multivariate framework provides foundation for future development of generalizable, artificial intelligence-driven tools for chronic neuropathic pain treatments. |
| first_indexed | 2024-12-19T15:21:22Z |
| format | Article |
| id | doaj.art-75cf11bf122a476784544a3ac8eac2ad |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-19T15:21:22Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-75cf11bf122a476784544a3ac8eac2ad2022-12-21T20:15:59ZengElsevierNeuroImage: Clinical2213-15822021-01-0131102706Regional brain morphology predicts pain relief in trigeminal neuralgiaPeter Shih-Ping Hung0Alborz Noorani1Jia Y. Zhang2Sarasa Tohyama3Normand Laperriere4Karen D. Davis5David J. Mikulis6Frank Rudzicz7Mojgan Hodaie8Division of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaDivision of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaSchulich School of Medicine & Dentistry, Western University, London, Ontario, CanadaDivision of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaDepartment of Radiation Oncology, University of Toronto, Toronto, Ontario, CanadaDivision of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaInstitute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Division of Neuroradiology, Joint Department of Medical Imaging, University Health Network, Toronto Western Hospital, Toronto, Ontario, CanadaDepartment of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, CanadaDivision of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Krembil Neuroscience Centre, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Corresponding author at: Toronto Western Hospital, Division of Neurosurgery, 399 Bathurst Street, 4W W-443, Toronto, Ontario M5T 2S8, Canada.Background: Trigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief following surgical interventions can minimize unnecessary surgeries and thus are of substantial benefit for patients and clinicians. Purpose: To determine if pre-treatment regional brain morphology-based machine learning models can prognosticate 1 year response to Gamma Knife radiosurgery for trigeminal neuralgia. Methods: We used a data-driven approach that combined retrospective structural neuroimaging data and support vector machine-based machine learning to produce robust multivariate prediction models of pain relief following Gamma Knife radiosurgery for trigeminal neuralgia. Surgical response was defined as ≥ 75% pain relief 1 year post-treatment. We created two prediction models using pre-treatment regional brain gray matter morphology (cortical thickness or surface area) to distinguish responders from non-responders to radiosurgery. Feature selection was performed through sequential backwards selection algorithm. Model out-of-sample generalizability was estimated via stratified 10-fold cross-validation procedure and permutation testing. Results: In 51 trigeminal neuralgia patients (35 responders, 16 non-responders), machine learning models based on pre-treatment regional brain gray matter morphology (14 regional surface areas or 13 regional cortical thicknesses) provided robust a priori prediction of surgical response. Cross-validation revealed the regional surface area model was 96.7% accurate, 100.0% sensitive, and 89.1% specific while the regional cortical thickness model was 90.5% accurate, 93.5% sensitive, and 83.7% specific. Permutation testing revealed that both models performed beyond pure chance (p < 0.001). The best predictor for regional surface area model and regional cortical thickness model was contralateral superior frontal gyrus and contralateral isthmus cingulate gyrus, respectively. Conclusions: Our findings support the use of machine learning techniques in subsequent investigations of chronic neuropathic pain. Furthermore, our multivariate framework provides foundation for future development of generalizable, artificial intelligence-driven tools for chronic neuropathic pain treatments.http://www.sciencedirect.com/science/article/pii/S2213158221001509Trigeminal neuralgiaChronic neuropathic painMachine learningPain reliefBrain morphology |
| spellingShingle | Peter Shih-Ping Hung Alborz Noorani Jia Y. Zhang Sarasa Tohyama Normand Laperriere Karen D. Davis David J. Mikulis Frank Rudzicz Mojgan Hodaie Regional brain morphology predicts pain relief in trigeminal neuralgia NeuroImage: Clinical Trigeminal neuralgia Chronic neuropathic pain Machine learning Pain relief Brain morphology |
| title | Regional brain morphology predicts pain relief in trigeminal neuralgia |
| title_full | Regional brain morphology predicts pain relief in trigeminal neuralgia |
| title_fullStr | Regional brain morphology predicts pain relief in trigeminal neuralgia |
| title_full_unstemmed | Regional brain morphology predicts pain relief in trigeminal neuralgia |
| title_short | Regional brain morphology predicts pain relief in trigeminal neuralgia |
| title_sort | regional brain morphology predicts pain relief in trigeminal neuralgia |
| topic | Trigeminal neuralgia Chronic neuropathic pain Machine learning Pain relief Brain morphology |
| url | http://www.sciencedirect.com/science/article/pii/S2213158221001509 |
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