Maria Lyasheva
BSc (Hons), MPhil (Cantab)
Clinical Researcher
Originally from Russia, Maria came to the UK back in 2012. Maria gained a BSc (Hons) in Biomedical Sciences at the University of Manchester before obtaining an MPhil (Oncology) in Medical Science at the University of Cambridge.
During her Masters, Maria had her first exposure to CT image analysis. Her research was focused on optimising radiotherapy treatment for head and neck cancer patients via tracking the anatomical changes of the parotid glands using CT scans.
Being fascinated by the power of imaging and acknowledging that “images are more than pictures”, Maria decided to build her career in this field. To broaden her experience and expertise, Maria received a scholarship from the British Council to undertake a research project on hyperpolarised xenon-129 MRI in patients with lung diseases at the Sir Peter Mansfield Imaging Centre (University of Nottingham).
As a clinical researcher in Professor Antoniades lab, Maria is in charge of running the ORFAN study. This international study is currently one of the largest studies that is looking into perivascular adipose tissue and the risk of developing cardiovascular disease using computed tomography (CT) as a non-invasive quantitative tool.
Alongside ORFAN, Maria also works across a multitude of other studies run by Prof Antoniades group.
Maria has also won a Recognition Scheme 2020/2021 Excellence Award from RDM Cardiovascular Medicine (University of Oxford).
Recent publications
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Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.
Journal article
Kotanidis CP. et al, (2022), Lancet Digit Health, 4, e705 - e716
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Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.
Journal article
di Noia C. et al, (2022), Diagnostics (Basel), 12
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AUTOMATED DEEP LEARNING QUANTIFICATION OF EPICARDIAL ADIPOSITY ON CARDIAC CT PREDICTS ATRIAL FIBRILLATION RISK IMMEDIATELY FOLLOWING CARDIAC SURGERY AND LONG-TERM
Conference paper
West H. et al, (2022), HEART, 108, A105 - A106
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Filtration-histogram based texture analysis and CALIPER based pattern analysis as quantitative CT techniques in idiopathic pulmonary fibrosis: head-to-head comparison.
Journal article
AlDalilah Y. et al, (2022), Br J Radiol
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Applying a Deep-Learning Approach for Automated Quantification of Epicardial Adipose Tissue on Coronary Computed Tomography Angiography in Challenging Clinical Populations
Conference paper
West HW. et al, (2021), CIRCULATION, 144