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Article

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

by
Luca Pasquini
1,2,
Antonio Napolitano
3,*,
Emanuela Tagliente
3,
Francesco Dellepiane
1,
Martina Lucignani
3,
Antonello Vidiri
4,
Giulio Ranazzi
5,
Antonella Stoppacciaro
5,
Giulia Moltoni
1,
Matteo Nicolai
1,
Andrea Romano
1,
Alberto Di Napoli
1 and
Alessandro Bozzao
1
1
Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
2
Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
3
Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
4
Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy
5
Surgical Pathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2021, 11(4), 290; https://doi.org/10.3390/jpm11040290
Submission received: 11 March 2021 / Revised: 2 April 2021 / Accepted: 7 April 2021 / Published: 9 April 2021
(This article belongs to the Section Omics/Informatics)
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Abstract

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.
Keywords: GBM; high grade glioma; MRI; IDH; deep learning; CBV; artificial intelligence GBM; high grade glioma; MRI; IDH; deep learning; CBV; artificial intelligence

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MDPI and ACS Style

Pasquini, L.; Napolitano, A.; Tagliente, E.; Dellepiane, F.; Lucignani, M.; Vidiri, A.; Ranazzi, G.; Stoppacciaro, A.; Moltoni, G.; Nicolai, M.; et al. Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM. J. Pers. Med. 2021, 11, 290. https://doi.org/10.3390/jpm11040290

AMA Style

Pasquini L, Napolitano A, Tagliente E, Dellepiane F, Lucignani M, Vidiri A, Ranazzi G, Stoppacciaro A, Moltoni G, Nicolai M, et al. Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM. Journal of Personalized Medicine. 2021; 11(4):290. https://doi.org/10.3390/jpm11040290

Chicago/Turabian Style

Pasquini, Luca, Antonio Napolitano, Emanuela Tagliente, Francesco Dellepiane, Martina Lucignani, Antonello Vidiri, Giulio Ranazzi, Antonella Stoppacciaro, Giulia Moltoni, Matteo Nicolai, and et al. 2021. "Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM" Journal of Personalized Medicine 11, no. 4: 290. https://doi.org/10.3390/jpm11040290

APA Style

Pasquini, L., Napolitano, A., Tagliente, E., Dellepiane, F., Lucignani, M., Vidiri, A., Ranazzi, G., Stoppacciaro, A., Moltoni, G., Nicolai, M., Romano, A., Di Napoli, A., & Bozzao, A. (2021). Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM. Journal of Personalized Medicine, 11(4), 290. https://doi.org/10.3390/jpm11040290

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