Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium
Authors
Shuaib, H.Barker, G. J.
Sasieni, P.
De Vita, E.
Chelliah, A.
Andrei, R.
Ashkan, K.
Beaumont, E.
Brazil, L.
Rowland-Hill, C.
Lau, A.
Luis, A.
Powell, J.
Swampillai, A.
Tenant, Sean
Thust, S. C.
Wastling, S.
Young, T.
Booth, T. C.
Brock, J.
Currie, S.
Fatani, K.
Foweraker, K.
Glendenning, J.
Hoggard, N.
Kanodia, A. K.
Krishnan, A.
Thurston, M. D.
Lewis, J.
Linares, C.
Mathew, R. K.
Ramalingam, S.
Sawlani, V.
Welsh, L.
Williams, M.
Affiliation
Guy's & St Thomas' NHS Foundation Trust, King's College, LondonIssue Date
2022
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Show full item recordAbstract
Objective: 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.Citation
Shuaib 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.Journal
British Journal of RadiologyDOI
10.1259/bjr.20220206PubMed ID
35616700Additional Links
https://dx.doi.org/10.1259/bjr.20220206Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1259/bjr.20220206
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