Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium
Barker, G. J.
De Vita, E.
Thust, S. C.
Booth, T. C.
Kanodia, A. K.
Thurston, M. D.
Mathew, R. K.
AffiliationGuy's & St Thomas' NHS Foundation Trust, King's College, London
MetadataShow full item record
AbstractObjective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning models. Methods: MR imaging data were analysed from a random sample of 5 patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres.
CitationShuaib H, Barker GJ, Sasieni P, De Vita E, Chelliah A, Andrei R, et al. Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium. The British Journal of Radiology. British Institute of Radiology; 2022.
JournalBritish Journal of Radiology
- Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.
- Authors: Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pflüger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P
- Issue date: 2021 Dec
- IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
- Authors: Choi Y, Nam Y, Lee YS, Kim J, Ahn KJ, Jang J, Shin NY, Kim BS, Jeon SS
- Issue date: 2020 Jul
- Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
- Authors: Perkuhn M, Stavrinou P, Thiele F, Shakirin G, Mohan M, Garmpis D, Kabbasch C, Borggrefe J
- Issue date: 2018 Nov
- Deep learning-based convolutional neural network for intramodality brain MRI synthesis.
- Authors: Osman AFI, Tamam NM
- Issue date: 2022 Apr
- Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.
- Authors: Shin I, Kim H, Ahn SS, Sohn B, Bae S, Park JE, Kim HS, Lee SK
- Issue date: 2021 May