Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment
BackgroundClassic 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 clin...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-11-01
|
| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1443124/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846151768588681216 |
|---|---|
| author | Ji Wu Chengjian Qin Yixuan Zhou Xuanlei Wei Deling Qin Keyu Chen Yuankun Cai Lei Shen Jingyi Yang Dongyuan Xu Songshan Chai Nanxiang Xiong |
| author_facet | Ji Wu Chengjian Qin Yixuan Zhou Xuanlei Wei Deling Qin Keyu Chen Yuankun Cai Lei Shen Jingyi Yang Dongyuan Xu Songshan Chai Nanxiang Xiong |
| author_sort | Ji Wu |
| collection | DOAJ |
| description | BackgroundClassic 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).MethodsThe 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.ResultsImaging 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-72e82c1c3ea141cf95f370af70f6cafd |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-72e82c1c3ea141cf95f370af70f6cafd2024-11-27T05:10:12ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-11-011510.3389/fneur.2024.14431241443124Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatmentJi Wu0Chengjian Qin1Yixuan Zhou2Xuanlei Wei3Deling Qin4Keyu Chen5Yuankun Cai6Lei Shen7Jingyi Yang8Dongyuan Xu9Songshan Chai10Nanxiang Xiong11Department of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaDepartment of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, ChinaBackgroundClassic 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).MethodsThe 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.ResultsImaging 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fneur.2024.1443124/fullmachine learningnomogrampercutaneous balloon compressiontrigeminal neuralgiaprognosis |
| spellingShingle | Ji Wu Chengjian Qin Yixuan Zhou Xuanlei Wei Deling Qin Keyu Chen Yuankun Cai Lei Shen Jingyi Yang Dongyuan Xu Songshan Chai Nanxiang Xiong Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment Frontiers in Neurology machine learning nomogram percutaneous balloon compression trigeminal neuralgia prognosis |
| title | Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| title_full | Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| title_fullStr | Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| title_full_unstemmed | Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| title_short | Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| title_sort | machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment |
| topic | machine learning nomogram percutaneous balloon compression trigeminal neuralgia prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1443124/full |
| work_keys_str_mv | AT jiwu machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT chengjianqin machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT yixuanzhou machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT xuanleiwei machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT delingqin machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT keyuchen machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT yuankuncai machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT leishen machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT jingyiyang machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT dongyuanxu machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT songshanchai machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment AT nanxiangxiong machinelearningtopredictradiomicsmodelsofclassicaltrigeminalneuralgiaresponsetopercutaneousballooncompressiontreatment |