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Objective: The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas.

Methods: Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs.

Results: IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images.

Conclusions: TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.

Competing Interests: The authors declare no conflict of interest.

Titel:	Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy.
Autor/in / Beteiligte Person:	Zhang, J ; Wang, Y ; Yang, Y ; Han, Y ; Yu, Y ; Hu, Y ; Liang, S ; Sun, Q ; Shang, D ; Bi, J ; Cui, G ; Yan, L
Zeitschrift:	Journal of computer assisted tomography, Jg. 48 (2024-05-01), Heft 3, S. 449
Veröffentlichung:	<2000->: Hagerstown, MD : Lippincott Williams & Wilkins ; <i>Original Publication</i>: New York, Raven Press., 2024
Medientyp:	academicJournal
ISSN:	1532-3145 (electronic)
DOI:	10.1097/RCT.0000000000001575
Schlagwort:	Humans Female Male Middle Aged Retrospective Studies Adult Aged Predictive Value of Tests Neural Networks, Computer Isocitrate Dehydrogenase genetics Glioma diagnostic imaging Glioma genetics Brain Neoplasms diagnostic imaging Brain Neoplasms genetics Magnetic Resonance Imaging methods Neoplasm Grading
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [J Comput Assist Tomogr] 2024 May-Jun 01; Vol. 48 (3), pp. 449-458. <i>Date of Electronic Publication: </i>2024 Jan 16. MeSH Terms: Isocitrate Dehydrogenase* / genetics ; Glioma* / diagnostic imaging ; Glioma* / genetics ; Brain Neoplasms* / diagnostic imaging ; Brain Neoplasms* / genetics ; Magnetic Resonance Imaging* / methods ; Neoplasm Grading* ; Humans ; Female ; Male ; Middle Aged ; Retrospective Studies ; Adult ; Aged ; Predictive Value of Tests ; Neural Networks, Computer References: Ostrom QT, Price M, Neff C, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2015–2019. Neuro Oncol . 2022;24:v1–v95. ; Mesfin FB, Al-Dhahir MA. Gliomas. In: StatPearls . Treasure Island, FL: StatPearls Publishing; 2021. ; Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. 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Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy.