Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium

no code implementations • 7 May 2024 • Haris Shuaib, Gareth J Barker, Peter Sasieni, Enrico De Vita, Alysha Chelliah, Roman Andrei, Keyoumars Ashkan, Erica Beaumont, Lucy Brazil, Chris Rowland-Hill, Yue Hui Lau, Aysha Luis, James Powell, Angela Swampillai, Sean Tenant, Stefanie C Thust, Stephen Wastling, Tom Young, Thomas C Booth

Diffusion MRI is the most common non-structural imaging type, performed at every site.

Scheduling

Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021

no code implementations • 15 Apr 2021 • Thomas Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sebastien Ourselin, Marc Modat

The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.

BIG-bench Machine Learning Specificity

Machine learning and glioma imaging biomarkers

no code implementations • 28 Aug 2019 • Thomas Booth, Matthew Williams, Aysha Luis, Jorge Cardoso, Ashkan Keyoumars, Haris Shuaib

Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).

BIG-bench Machine Learning