Imperial College London

ProfessorEricAboagye

Faculty of MedicineDepartment of Surgery & Cancer

Professor
 
 
 
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Contact

 

+44 (0)20 3313 3759eric.aboagye

 
 
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Assistant

 

Mrs Maureen Francis +44 (0)20 7594 2793

 
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Location

 

GN1Commonwealth BuildingHammersmith Campus

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Summary

 

Summary

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.

Publications

Journals

Aboagye EO, Kraeber-Bodere F, 2017, Highlights lecture EANM 2016: "Embracing molecular imaging and multi-modal imaging: a smart move for nuclear medicine towards personalized medicine", European Journal of Nuclear Medicine and Molecular Imaging, Vol:44, ISSN:1619-7070, Pages:1559-1574

Challapalli A, Carroll L, Aboagye EO, 2017, Molecular mechanisms of hypoxia in cancer, Clinical and Translational Imaging, Vol:5, ISSN:2281-5872, Pages:225-253

Cysouw MCF, Kramer GM, Frings V, et al., 2017, Baseline and longitudinal variability of normal tissue uptake values of [F-18]-fluorothymidine-PET images, Nuclear Medicine and Biology, Vol:51, ISSN:0969-8051, Pages:18-24

Lavdas I, Glocker B, Kamnitsas K, et al., 2017, Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach., Med Phys

O'Connor JPB, Aboagye EO, Adams JE, et al., 2017, Imaging biomarker roadmap for cancer studies, Nature Reviews Clinical Oncology, Vol:14, ISSN:1759-4774, Pages:169-186

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