Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05980 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917662504779776 |
|---|---|
| author | Shuaib, Haris Barker, Gareth J Sasieni, Peter De Vita, Enrico Chelliah, Alysha Andrei, Roman Ashkan, Keyoumars Beaumont, Erica Brazil, Lucy Rowland-Hill, Chris Lau, Yue Hui Luis, Aysha Powell, James Swampillai, Angela Tenant, Sean Thust, Stefanie C Wastling, Stephen Young, Tom Booth, Thomas C |
| author_facet | Shuaib, Haris Barker, Gareth J Sasieni, Peter De Vita, Enrico Chelliah, Alysha Andrei, Roman Ashkan, Keyoumars Beaumont, Erica Brazil, Lucy Rowland-Hill, Chris Lau, Yue Hui Luis, Aysha Powell, James Swampillai, Angela Tenant, Sean Thust, Stefanie C Wastling, Stephen Young, Tom Booth, Thomas C |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium Shuaib, Haris Barker, Gareth J Sasieni, Peter De Vita, Enrico Chelliah, Alysha Andrei, Roman Ashkan, Keyoumars Beaumont, Erica Brazil, Lucy Rowland-Hill, Chris Lau, Yue Hui Luis, Aysha Powell, James Swampillai, Angela Tenant, Sean Thust, Stefanie C Wastling, Stephen Young, Tom Booth, Thomas C Image and Video Processing Machine Learning 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. |
| title | Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2405.05980 |