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Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics

DC FieldValueLanguage
dc.contributor.author박예원-
dc.contributor.author안성수-
dc.contributor.author이승구-
dc.date.accessioned2021-01-19T08:08:22Z-
dc.date.available2021-01-19T08:08:22Z-
dc.date.issued2020-09-
dc.identifier.issn0920-9964-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181476-
dc.description.abstractBackground: Accurately diagnosing schizophrenia is still challenging due to the lack of validated biomarkers. Here, we aimed to investigate whether radiomic features in bilateral hippocampal subfields from magnetic resonance images (MRIs) can differentiate patients with schizophrenia from healthy controls (HCs). Methods: A total of 152 participants with MRI (86 schizophrenia and 66 HCs) were allocated to training (n = 106) and test (n = 46) sets. Radiomic features (n = 642) from the bilateral hippocampal subfields processed with automatic segmentation techniques were extracted from T1-weighted MRIs. After feature selection, various combinations of classifiers (logistic regression, extra-trees, AdaBoost, XGBoost, or support vector machine) and subsampling were trained. The performance of the classifier was validated in the test set by determining the area under the curve (AUC). Furthermore, the association between selected radiomic features and clinical symptoms in schizophrenia was assessed. Results: Thirty radiomic features were identified to differentiate participants with schizophrenia from HCs. In the training set, the AUC exhibited poor to good performance (range: 0.683-0.861). The best performing radiomics model in the test set was achieved by the mutual information feature selection and logistic regression with an AUC, accuracy, sensitivity, and specificity of 0.821 (95% confidence interval 0.681-0.961), 82.1%, 76.9%, and 70%, respectively. Greater maximum values in the left cornu ammonis 1-3 subfield were associated with a higher severity of positive symptoms and general psychopathology in participants with schizophrenia. Conclusion: Radiomic features from hippocampal subfields may be useful biomarkers for identifying schizophrenia.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier Science Publisher B. V.-
dc.relation.isPartOfSCHIZOPHRENIA RESEARCH-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDifferentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorYae Won Park-
dc.contributor.googleauthorDongmin Choi-
dc.contributor.googleauthorJoonho Lee-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorSeung-Koo Lee-
dc.contributor.googleauthorSang-Hyuk Lee-
dc.contributor.googleauthorMinji Bang-
dc.identifier.doi10.1016/j.schres.2020.09.009-
dc.contributor.localIdA05330-
dc.contributor.localIdA02234-
dc.contributor.localIdA02234-
dc.contributor.localIdA02912-
dc.contributor.localIdA02912-
dc.relation.journalcodeJ02641-
dc.identifier.eissn1573-2509-
dc.identifier.pmid32988740-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0920996420304679-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordHippocampus-
dc.subject.keywordMachine learning-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordRadiomics-
dc.subject.keywordSchizophrenia-
dc.contributor.alternativeNamePark, Yae-Won-
dc.contributor.affiliatedAuthor박예원-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor이승구-
dc.citation.volume223-
dc.citation.startPage337-
dc.citation.endPage344-
dc.identifier.bibliographicCitationSCHIZOPHRENIA RESEARCH, Vol.223 : 337-344, 2020-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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