Cited 1 times in
Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김진아 | - |
dc.contributor.author | 박예원 | - |
dc.contributor.author | 안성수 | - |
dc.contributor.author | 이승구 | - |
dc.date.accessioned | 2024-12-06T02:17:20Z | - |
dc.date.available | 2024-12-06T02:17:20Z | - |
dc.date.issued | 2024-07 | - |
dc.identifier.issn | 1323-1316 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200735 | - |
dc.description.abstract | AimsThe cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum.MethodsA total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability.ResultsWe identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia.ConclusionThe radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Blackwell Science | - |
dc.relation.isPartOf | PSYCHIATRY AND CLINICAL NEUROSCIENCES | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Cerebellum* / diagnostic imaging | - |
dc.subject.MESH | Cerebellum* / pathology | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Magnetic Resonance Imaging* / standards | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Neuroimaging / methods | - |
dc.subject.MESH | Neuroimaging / standards | - |
dc.subject.MESH | Radiomics | - |
dc.subject.MESH | Schizophrenia* / diagnostic imaging | - |
dc.subject.MESH | Schizophrenia* / pathology | - |
dc.subject.MESH | Sensitivity and Specificity | - |
dc.subject.MESH | Young Adult | - |
dc.title | Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Minji Bang | - |
dc.contributor.googleauthor | Kisung Park | - |
dc.contributor.googleauthor | Seoung-Ho Choi | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Jinna Kim | - |
dc.contributor.googleauthor | Seung-Koo Lee | - |
dc.contributor.googleauthor | Yae Won Park | - |
dc.contributor.googleauthor | Sang-Hyuk Lee | - |
dc.identifier.doi | 10.1111/pcn.13707 | - |
dc.contributor.localId | A01022 | - |
dc.contributor.localId | A05330 | - |
dc.contributor.localId | A02234 | - |
dc.contributor.localId | A02912 | - |
dc.relation.journalcode | J02568 | - |
dc.identifier.eissn | 1440-1819 | - |
dc.identifier.pmid | 38953397 | - |
dc.identifier.url | https://onlinelibrary.wiley.com/doi/10.1111/pcn.13707 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | cerebellum | - |
dc.subject.keyword | diagnostic modeling | - |
dc.subject.keyword | radiomics | - |
dc.subject.keyword | schizophrenia | - |
dc.contributor.alternativeName | Kim, Jinna | - |
dc.contributor.affiliatedAuthor | 김진아 | - |
dc.contributor.affiliatedAuthor | 박예원 | - |
dc.contributor.affiliatedAuthor | 안성수 | - |
dc.contributor.affiliatedAuthor | 이승구 | - |
dc.citation.volume | 78 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 527 | - |
dc.citation.endPage | 535 | - |
dc.identifier.bibliographicCitation | PSYCHIATRY AND CLINICAL NEUROSCIENCES, Vol.78(9) : 527-535, 2024-07 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.