Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer




+44 (0)20 3313 3759eric.aboagye




Mrs Maureen Francis +44 (0)20 7594 2793




GN1Commonwealth BuildingHammersmith Campus





Eric Aboagye is Professor of Cancer Pharmacology & Molecular Imaging and Director of the CRUK-EPSRC-MRC-NIHR Comprehensive Cancer Imaging Centre. He joined the college after a PhD at the CRUK Beatson Laboratories in Glasgow, UK and post-doc fellowship at The Johns Hopkins University & Hospital in Baltimore, USA. His group is interested in the discovery and development of new methods for experimental and clinical cancer molecular imaging. In the past 5 years, the team has invented and translated three novel cancer diagnostics into human application. He has acted as an advisor to GE-Healthcare, GSK, Roche and Novartis.

Professor Aboagye was recipient of the 2009 Sir Mackenzie Davidson Medal and was  elected as a Fellow of the Academy of Medical Sciences in 2010. Academy Fellows are elected for outstanding contributions to the advancement of medical science, for innovative application of scientific knowledge or for conspicuous service to healthcare.



Amgheib A, Fu R, Aboagye EO, 2022, Positron Emission Tomography Probes for Imaging Cytotoxic Immune Cells, Pharmaceutics, Vol:14, Pages:2040-2040

Aboagye E, Brickute D, Allott L, et al., 2022, Design, synthesis, and evaluation of a novel PET imaging agent targeting lipofuscin in senescent cells, Rsc Advances: an International Journal to Further the Chemical Sciences, ISSN:2046-2069

Hubbard Cristinacce PL, Keaveney S, Aboagye EO, et al., 2022, Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice., Phys Med, Vol:101, Pages:165-182

Komodromos M, Aboagye EO, Evangelou M, et al., 2022, Variational Bayes for high-dimensional proportional hazards models with applications within gene expression, Bioinformatics, Vol:38, ISSN:1367-4803, Pages:3918-3926

Boubnovski MM, Chen M, Linton-Reid K, et al., 2022, Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs, Clinical Radiology, Vol:77, ISSN:0009-9260, Pages:e620-e627

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