C
Cathal McCague
Researcher at University of Cambridge
Publications - 15
Citations - 655
Cathal McCague is an academic researcher from University of Cambridge. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 3, co-authored 6 publications receiving 152 citations.
Papers
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Journal ArticleDOI
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael S. Roberts,Michael S. Roberts,Derek Driggs,Matthew Thorpe,Julian D. Gilbey,Michael Yeung,Stephan Ursprung,Angelica I. Aviles-Rivero,Christian Etmann,Cathal McCague,Lucian Beer,Jonathan R. Weir-McCall,Jonathan R. Weir-McCall,Zhongzhao Teng,Effrossyni Gkrania-Klotsas,James H.F. Rudd,Evis Sala,Carola-Bibiane Schönlieb +17 more
TL;DR: It is found that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases, which is a major weakness, given the urgency with which validated COVID-19 models are needed.
Journal ArticleDOI
Radiomics and radiogenomics in ovarian cancer: a literature review.
Stephanie Nougaret,Cathal McCague,Hichem Tibermacine,Hebert Alberto Vargas,Stefania Rizzo,Evis Sala +5 more
TL;DR: An overview of radiomics, radiogenomics, and proteomics is provided and the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer is examined.
Posted Content
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review
Michael S. Roberts,Derek Driggs,Matthew Thorpe,Julian D. Gilbey,Yeung M,Stephan Ursprung,Angelica I. Aviles-Rivero,Christian Etmann,Cathal McCague,Lucian Beer,Weir-McCall,Teng Z,Rudd Jhf,Evis Sala,C-B. Schönlieb +14 more
TL;DR: The review finds that none of the developed models discussed are of potential clinical use due to methodological flaws and underlying biases, which is a major weakness, given the urgency with which validated COVID-19 models are needed.
Journal ArticleDOI
Introduction to radiomics for a clinical audience.
Cathal McCague,S. Ramlee,Maria Reinius,Ian Selby,Dean Hulse,P. M. J. R. Piyatissa,Vlad Bura,Mireia Crispin-Ortuzar,E. Sala,Ramona Woitek +9 more
TL;DR: Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support as mentioned in this paper .
Journal ArticleDOI
Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma
Leonardo Rundo,Lucian Beer,L. Escudero Sanchez,Mireia Crispin-Ortuzar,Maria Reinius,Cathal McCague,Hilal Sahin,Vlad Bura,Roxana Maria Pintican,Marta Zerunian,Stephan Ursprung,Iris Allajbeu,Helen C. Addley,Paula Martin-Gonzalez,T. Buddenkotte,Naveena Singh,Anju Sahdev,Ionut G. Funingana,Mercedes Jimenez-Linan,Florian Markowetz,James D. Brenton,Evis Sala,Ramona Woitek +22 more
TL;DR: Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing, indicating high generalizability and reliability in identifying non-responders when using radiomics.