A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images
Lang DM, Peeken JC, Combs SE, Wilkens JJ, Bartzsch S (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12962 LNCS
Pages Range: 306-314
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783031089985
DOI: 10.1007/978-3-031-08999-2_25
Abstract
Patient MGMT (O 6 methylguanine DNA methyltransferase) status has been identified essential for the responsiveness to chemotherapy in glioblastoma patients and therefore depicts an important clinical factor. Testing for MGMT methylation is invasive, time consuming and costly and lacks a uniform gold standard. We studied MGMT status assessment by multi-parametric magnetic resonance imaging (mpMRI) scans and tested the ability of deep learning for classification of this task. To overcome the limited number of training examples we used a transfer learning approach based on the video clip classification network C3D [30], allowing for full exploitation of three dimensional information in the MR images. MRI sequences were fused using a locally connected layer. Our approach was able to differentiate MGMT methylated from unmethylated patients with an area under the receiver operating characteristics curve (AUC) of 0.689 for the public validation set. On the private test set AUC was given by 0.577. Further studies for assessment of clinical importance and predictive power in terms of survival are needed.
Involved external institutions
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Lang, D.M., Peeken, J.C., Combs, S.E., Wilkens, J.J., & Bartzsch, S. (2022). A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images. In Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 306-314). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Lang, D. M., et al. "A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images." Proceedings of the 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Alessandro Crimi, Spyridon Bakas, Springer Science and Business Media Deutschland GmbH, 2022. 306-314.
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