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Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

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Abstract
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n=80) with real IDH-mutant glioblastomas (n=38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P=0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P<0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model.
Author(s)
박지은은다인김호성이다현장령우김남국
Issued Date
2021
Type
Article
Keyword
GlioblastomaMRI
DOI
10.1038/s41598-021-89477-w
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8108
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_b9e0f4185fc740d6af0b446f7f57164e&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Generative%20adversarial%20network%20for%20glioblastoma%20ensures%20morphologic%20variations%20and%20improves%20diagnostic%20model%20for%20isocitrate%20dehydrogenase%20mutant%20type&amp;offset=0&amp;pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
미국
Language
영어
ISSN
2045-2322
Citation Volume
11
Citation Number
1
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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