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Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network

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dc.contributor.authorChoi, KS-
dc.contributor.authorSeung Hong Choi-
dc.contributor.authorJeong, B-
dc.date.available2020-01-31T00:55:10Z-
dc.date.created2019-11-18-
dc.date.issued2019-09-
dc.identifier.issn1522-8517-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/6883-
dc.description.abstract© 2019 The Author(s). Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved.Background: The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI. Methods: Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2∗ susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model. Results: The IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2∗ susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype. Conclusions: We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas-
dc.description.uri1-
dc.language영어-
dc.publisherOXFORD UNIV PRESS INC-
dc.subjectAngiogenesis-
dc.subjectDynamic susceptibility contrast perfusion-weighted imaging-
dc.subjectGliomas-
dc.subjectIsocitrate dehydrogenase mutations-
dc.subjectRecurrent neural network-
dc.titlePrediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000493070900014-
dc.identifier.scopusid2-s2.0-85073448728-
dc.identifier.rimsid70398-
dc.contributor.affiliatedAuthorSeung Hong Choi-
dc.identifier.doi10.1093/neuonc/noz095-
dc.identifier.bibliographicCitationNEURO-ONCOLOGY, v.21, no.9, pp.1197 - 1209-
dc.citation.titleNEURO-ONCOLOGY-
dc.citation.volume21-
dc.citation.number9-
dc.citation.startPage1197-
dc.citation.endPage1209-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusHIGH-GRADE GLIOMAS-
dc.subject.keywordPlusCENTRAL-NERVOUS-SYSTEM-
dc.subject.keywordPlusMUTATION STATUS-
dc.subject.keywordPlusBRAIN-
dc.subject.keywordPlusTEMOZOLOMIDE-
dc.subject.keywordPlusSURVIVAL-
dc.subject.keywordPlusTUMORS-
dc.subject.keywordAuthorangiogenesis-
dc.subject.keywordAuthordynamic susceptibility contrast perfusion-weighted imaging-
dc.subject.keywordAuthorgliomas-
dc.subject.keywordAuthorisocitrate dehydrogenase mutations-
dc.subject.keywordAuthorrecurrent neural network-
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Center for Nanoparticle Research(나노입자 연구단) > 1. Journal Papers (저널논문)
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