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Identification of focal epilepsy by diffusion tensor imaging using machine learning

2021; Wiley; Volume: 143; Issue: 6 Linguagem: Inglês

10.1111/ane.13407

1600-0404

Dong Ah Lee, Ho‐Joon Lee, Byung Joon Kim, Bong Soo Park, Sung Eun Kim, Kang Min Park,

Fetal and Pediatric Neurological Disorders

Acta Neurologica ScandinavicaVolume 143, Issue 6 p. 637-645 ORIGINAL ARTICLE Identification of focal epilepsy by diffusion tensor imaging using machine learning Dong Ah Lee, Dong Ah Lee Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorHo-Joon Lee, Ho-Joon Lee Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorByung Joon Kim, Byung Joon Kim Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorBong Soo Park, Bong Soo Park Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorSung Eun Kim, Sung Eun Kim orcid.org/0000-0002-7099-1749 Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorKang Min Park, Corresponding Author Kang Min Park [email protected] orcid.org/0000-0002-4229-7741 Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea Correspondence Kang Min Park, Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Postcode: 48108, Busan, Republic of Korea. Email: [email protected]Search for more papers by this author Dong Ah Lee, Dong Ah Lee Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorHo-Joon Lee, Ho-Joon Lee Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorByung Joon Kim, Byung Joon Kim Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorBong Soo Park, Bong Soo Park Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorSung Eun Kim, Sung Eun Kim orcid.org/0000-0002-7099-1749 Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, KoreaSearch for more papers by this authorKang Min Park, Corresponding Author Kang Min Park [email protected] orcid.org/0000-0002-4229-7741 Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea Correspondence Kang Min Park, Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Postcode: 48108, Busan, Republic of Korea. Email: [email protected]Search for more papers by this author First published: 18 March 2021 https://doi.org/10.1111/ane.13407Citations: 1 Dong Ah Lee and Ho-Joon Lee contributed equally to this study. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Objective The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness. Methods This was a retrospective study performed at a tertiary hospital. We enrolled 456 patients with focal epilepsy, who underwent DTI and were taking ASMs. We enrolled 100 healthy subjects as a control. We obtained the conventional DTI measures and structural connectomic profiles from the DTI. Results The support vector machine (SVM) classifier based on the conventional DTI measures revealed an accuracy of 76.5% and an area under curve (AUC) of 0.604 (95% Confidence interval (CI), 0.506–0.695). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 82.8% and an AUC of 0.701 (95% CI, 0.606–0.784). Of the 456 patients with epilepsy, 242 patients were ASM good responders, whereas 214 patients were ASM poor responders. In the classification of the ASM responders, an SVM classifier based on the conventional DTI measures revealed an accuracy of 54.9% and an AUC of 0.551 (95% CI, 0.443–0.655). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 59.3% and an AUC of 0.594 (95% CI, 0.485–0.695). Conclusion DTI using a machine learning is useful for differentiating patients with focal epilepsy from healthy controls, but it cannot classify ASM responsiveness. Combining structural connectomic profiles results in a better classification performance than the use of conventional DTI measures alone for identifying focal epilepsy and ASM responsiveness. CONFLICT OF INTEREST None of the authors have any conflict of interest to disclose. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Citing Literature Volume143, Issue6June 2021Pages 637-645 RelatedInformation