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Does Generative Adversarial Network Ensure Diversity in Data? Morphologic Evaluation of Synthetic Isocitrate Dehydrogenase-Mutant Glioblastomas in A Clinical Diagnostic Model
Ho Sung Kim
Asan Medical Center
Corresponding Author
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Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures diversity is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variability 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 = .04) and distinct non-enhancing tumor margins (OR, 2.68 vs. 3.88; P <.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 diversity and quality, thereby improving diagnostic performance in a clinical model.
Keywords
isocitrate dehydrogenase, glioblastoma, generative adversarial network, prediction
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Does Generative Adversarial Network Ensure Diversity in Data? Morphologic Evaluation of Synthetic Isocitrate Dehydrogenase-Mutant Glioblastomas in A Clinical Diagnostic Model
Ho Sung Kim
Asan Medical Center
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 Dec, 2020
No community comments so far
Editor invited
On 23 Dec, 2020
Submission checks complete
On 22 Dec, 2020
First submitted
On 11 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures diversity is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variability 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 = .04) and distinct non-enhancing tumor margins (OR, 2.68 vs. 3.88; P <.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 diversity and quality, thereby improving diagnostic performance in a clinical model.
Figure 1
Figure 2
Figure 3