  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 five 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. Advances in knowledge: Successful translation of
deep-learning models will likely be based on structural post-treatment imaging
unless there is significant effort made to standardise non-structural or
peri-operative imaging protocols and schedules.
