[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124079-en":3,"doc-seo-124079-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124079,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment","Classic trigeminal neuralgia substantially impairs quality of life, and percutaneous balloon compression can fail or lead to relapse in a subset of patients. This study retrospectively analyzes clinical data and intraoperative balloon imaging from January 2017 to August 2023 to develop machine-learning based clinical imaging models. Feature-derived imaging predictors are assessed for postoperative recurrence risk using ROC performance, decision curve analysis, and calibration. Results show a final model with strong discriminatory ability and a nomogram with favorable calibration, supporting potential clinical utility for prognosis and treatment planning.","TYPE Original Research PUBLISHED 27 November 2024 DOI 10.3389/fneur.2024.1443124  \nOPEN ACCESS  \nEDITED BY  \nJun Zhong,  \nShanghai Jiao Tong University, China  \nREVIEWED BY  \nMostafa Meshref,  \nAl-Azhar University, Egypt Bowen Chang,  \nAnhui Provincial Hospital, China  \n*CORRESPONDENCE  \nSongshan Chai  \n [chai_s_s@qq.com](chai_s_s@qq.com)[ ](chai_s_s@qq.com)Nanxiang Xiong  \n [mozhuoxiong@163.com](mozhuoxiong@163.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 03 June 2024  \nACCEPTED 04 November 2024  \nPUBLISHED 27 November 2024  \nCITATION  \nWu J, Qin C, Zhou Y, Wei X, Qin D, Chen K, Cai Y, Shen L, Yang J, Xu D, Chai S and Xiong N (2024) Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment.  \nFront. Neurol. 15:1443124 .  \ndoi: 10.3389/fneur.2024.1443124  \nCOPYRIGHT  \n© 2024 Wu, Qin, Zhou, Wei, Qin, Chen, Cai, Shen, Yang, Xu, Chai and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment  \nJi Wu 1†, Chengjian Qin 2†, Yixuan Zhou 1†, Xuanlei Wei 2,  \nDeling Qin 2, Keyu Chen 1, Yuankun Cai 1, Lei Shen 1, Jingyi Yang 1, Dongyuan Xu 1, Songshan Chai 1* and Nanxiang Xiong 1*  \n1 Department of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, China, 2 Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China  \nBackground: Classic trigeminal neuralgia (CTN) seriously affects patients’quality of life. Percutaneous balloon compression (PBC) is a surgical program for treating trigeminal neuralgia. But some patients are ineffective or relapse after treatment. The aim is to use machine learning to construct clinical imaging models to predict relapse after treatment (PBC) .  \nMethods: The clinical data and intraoperative balloon imaging data of CTN from January 2017 to August 2023 were retrospectively analyzed. The relationship between least absolute shrinkage and selection operator and random forest prediction of PBC postoperative recurrence, ROC curve and decision-decision curve analysis is used to evaluate the impact of imaging histology on TN recurrence.  \nResults: Imaging features, like original_shape_Maximum2D, DiameterRow, Original_Shape_Elongation, etc. predict the prognosis of TN on PBC. The areas under roc curve were 0.812 and 0. 874, respectively. The area under the ROC curve of the final model is 0.872. DCA and calibration curves show that nomogram has a promising future in clinical application.  \nConclusion: The combination of machine learning and clinical imaging and clinical information has the good potential of predicting PBC in CTN treatment. The efficacy of CTN is suitable for clinical applications of CTN patients after PBC.  \nKEYWORDS  \nmachine learning, nomogram, percutaneous balloon compression, trigeminal neuralgia, prognosis  \nIntroduction  \nClassic trigeminal neuralgia (CTN) is a spontaneous pain sensation that occurs in the trigeminal nerve region, and the clinical manifestation is mainly paroxysmal electric shocklike or pinprick-like recurrent pain, CTN is a type of chronic pain that excludes secondary causes such as tumors, multiple sclerosis, or arteriovenous malformations affecting the trigeminal nerve. CTN is characterized by sudden, severe, electric shock-like pain or tingling in the distribution of one or more branches of the trigeminal nerve ( 1). The exact cause ofCTN  \nFrontiers in Neurolog","cbCairCv0cCTifUl","https://ap.wps.com/l/cbCairCv0cCTifUl","pdf",2345835,1,10,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What is the study aiming to predict in classical trigeminal neuralgia patients?\",\"answer\":\"The study aims to construct clinical imaging models using machine learning to predict postoperative relapse/recurrence after percutaneous balloon compression treatment.\"},{\"question\":\"What data sources were used to build the models?\",\"answer\":\"Clinical data and intraoperative balloon imaging data for classical trigeminal neuralgia patients from January 2017 to August 2023 were retrospectively analyzed.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"Model discrimination was assessed using ROC curve metrics, while clinical usefulness and reliability were examined through decision curve analysis (DCA) and calibration curves.\"}]","Machine learning to 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is the study aiming to predict in classical trigeminal neuralgia patients?","Question",{"text":75,"@type":76},"The study aims to construct clinical imaging models using machine learning to predict postoperative relapse/recurrence after percutaneous balloon compression treatment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources were used to build the models?",{"text":80,"@type":76},"Clinical data and intraoperative balloon imaging data for classical trigeminal neuralgia patients from January 2017 to August 2023 were retrospectively analyzed.",{"name":82,"@type":73,"acceptedAnswer":83},"How was model performance evaluated?",{"text":84,"@type":76},"Model discrimination was assessed using ROC curve metrics, while clinical usefulness and reliability were examined through decision curve analysis (DCA) and calibration 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