Professor of Radiology, Head of Department

Professor of Radiology – University of Cambridge School of Clinical Medicine.

Honorary Consultant Radiologist – Addenbrooke’s Hospital, Cambridge. University Hospitals NHS Foundation Trust.

Specialty: Breast and Oncology Imaging and Magnetic Resonance Imaging.

Email: F J Gilbert Tel.: + 44 (0)1223 46438

Profile

Professor Gilbert was appointed to the Chair of Radiology, head of department in Cambridge in 2011 having previously been the chair of the Aberdeen Biomedical Imaging Centre at the University of Aberdeen.

She has held various positions – Chair of the Academic committee of the Royal College of Radiologists, Chair of the NCRI Imaging Advisory group, Chair of the Royal College of Radiologists Breast Group, and co-chair of the NCRI PET Research Development group. She is past President of the European Society of Breast Imaging and past chair of the breast subcommittee of Radiological Society of North America. She now sits on the Research & development committee of RSNA.

She was an NIHR Senior Investigator and was awarded honorary fellowships of Radiological Society of North America, the American College of Radiology, the Gold medal from European Society of Radiology and a fellow of the Royal Society of Edinburgh and the Academy of Medical Sciences.

Her research interests are in imaging technology evaluation – covering early cancer detection, diagnostic accuracy in CT and MRI, and newer breast imaging techniques such as breast Tomosynthesis, Contrast mammography, abbreviated MRI, whole breast ultrasound and non FDG radiotracers in cancer. She uses imaging to gain a better understanding of disease physiology and the tumour microenvironment. She advocates risk adapted breast screening for women and is currently testing risk stratified approaches through the MyPEBS trial and leading the BRAID trial of supplemental imaging. She is using Artificial intelligence to improve early cancer detection with different imaging modalities.

Link to PubMed

Last 5 Publications

1. Kessler DA, Kaggie JD, MacKay JM, McDonald S, McDonnell S, Grainger AJ, Roberts AR, Janiczek RL, Graves MJ, Gilbert FJ. Automated Segmentation of Knee MRI Data with Two- and Three-Dimensional Convolutional Neural Networks for Quantitative Cartilage Surface Analysis. Osteoarthritis Imaging 2022.
2. Gilbert FJ, Harris S, Weir-McCall J, Miles K, Qureshi N, Dizdarevic S, Sinclair D, Shah, Eaton R, Clegg A, Benedetto, Hill, Cook A, Tzeli, Vale L, Brindle L, Madden J, Cozens , Little L, Eichhorst K, Moate T, McClement, Peebles, Banerjee, Han, Poon FW, Groves A, Kurban L, Callister, Crosbie P, Karunasaagarar, Kankam, George SD. Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules. HTA Report 2022.
3. Abeyakoon O, Woitek R, Wallis MG, Moyle PL, Morscher S, Dahlhaus N, Ford SJ, Burton NC, Manavaki R, Mendichovszky IA, Joseph J, Quiros-Gonzalez I, Bohndiek SE, Gilbert FJ. An optoacoustic imaging feature set to characterise blood vessels surrounding benign and malignant breast lesions. Photoacoustics July 2022.
4. Weir-McCall JR, Debruyn E, Harris S, Qureshi NR, Rintoul RC, Gleeson FV, Gilbert FJ, on behalf of the SPUtNIk investigators. Diagnostic accuracy of a convolutional neural network assessment of solitary pulmonary nodules compared with PET/CT and DCE-CT using unenhanced and contrast enhanced CT. Chest 2022.
5. Cozzi A; Di Leo G; Houssami N; Gilbert FJ; Helbich TH; Benito MA; Balleyguier C; Bazzocchi M; Bult P; Calabrese M; Camps Herrero J; Cartia F; Cassano E; Clauser P; de Lima Docema MF; Depretto C; Dominelli V; Forrai G; Girometti R; Harms SE; Hilborne S; Ienzi R; Lobbes MBI; Losio C; Mann RM; Montemezzi S; Obdeijn I-M; Ozcan UA; Pediconi F; Pinker K; Preibsch H; Raya Povedano L; Rossi Saccarelli C; Sacchetto D; Scaperrotta GP; Schlooz M; Szabó BK; Taylor DB; Ulus OS; Van Goethem M; Veltman J;Weigel JS; Wenkel E; Zuiani C; Sardanelli F. Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study. European Radiology 2022

Best 5 Publications

TOMMY trial: The accuracy of digital breast tomosynthesis in detecting breast cancer subgroups in a UK retrospective reading study (TOMMY Trial). Gilbert FJ, Tucker L, Gillan GC, Willsher P, Cooke J, Duncan KA, Michell MJ, Dobson HM, Lim YY, Purushothaman H, Strudley C, Astley SM, Morrish O, Young KC, Duffy SW.
Radiology. 2015 Jul 15:142566. [Epub ahead of print]PMID:26176654

International evaluation of an AI system for breast cancer screening. McKinney SM, Sieniek MT, Godble V, Godwin J, Antropova N, Ashrafian H, Black T, Chesus M, Corrado G, Darzi A, Elemadi M, Garcia-Vincente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam J, Melnick D, Mostofi H, Romero-Paredes B, Peng L, Reicher JJ, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. Nature 2020 577:89-94  doi: 10.1038/s41586-019-1799-6

Imaging breast cancer using hyperpolarized ¹³C-MRI. Gallagher FA, Woitek R, McLean MA, …… Graves M, Abraham JE, Gilbert FJ, Caldas C, Brindle KM. PNAS 2020 117, 2092-2098. doi: 10.1073/pnas.1913841117

Single reading with computer-aided detection for screening mammography. Gilbert, F.J., S.M. Astley, M.G. Gillan, O.F. Agbaje, M.G. Wallis, J. James, C.R. Boggis, S.W. Duffy, and C.I. Group. New England Journal of Medicine, 2008. 359(16): p. 1684.

Federated Learning used for predicting outcomes in SARS-COV-2 patients. Dayan I, Roth H, et al, Quanzheng L, Gilbert FJ^*, Flores MG^* Nature Medicine 2021 (^*joint senior authors).

Published Books

Magnetic Resonance Imaging.Gilbert, FJ. & Redpath, TW. (2006).Anaesthesia Science, pp. 379-395

Mammography reading with computer-aided detection(CAD);performance of different readers. Astley, SM., Duffy, SW., Boggis, C., Wilson, M., Barr, N., Beetles, U., Griffiths, MA., Johnson, J., Roberts, RM., Duncan, KA., Iyengar, G., Agbaje, O., Griffiths, P., McGee, MM., Gillan, MGC. & Gilbert, FJ. (2006).Digital mammography Proceedings 8th International Workshop:Lecture Notes in Computer Science 4046, Digital mammography Proceedings 8th International Workshop:Lecture Notes in Computer Science 4046, pp. 97-104.

Mammography reading with computer-aided detection (CAD);single view vs two views.Agbaje, O., Astley, SM., Gillan, MGC., Boggis, C., Wilson, M., Barr, N., Beetles, U., Griffiths, MA., Jain, A., Johnson, J., Roberts, RM., Duncan, KA., Iyengar, G., Griffiths, P., McGee, MM., Duffy, SW. & Gilbert, FJ. (2006)..Digital Mammography 8th International WorkshopProceedings Series: lecture Notes in Computer Science, Digital Mammography 8th International WorkshopProceedings Series: lecture Notes in Computer Science, pp. 125-130.

Reduction of movement artefacts in comparative 3D magnetic resonance (MR) breast imaging..Undrill, PE., Redpath, TW. & Gilbert, FJ. (1996).MH Loew & KM Hanson (eds), Medical Imaging 1996. Image Processing., vol. 2710, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), pp. 922-930..

Commissioned Reports

The clinical effectiveness and cost-effectiveness of different surveillance mammography regimes after the treatment of primary breast cancer’. NIHR Health Technology Assessment programme, Southampton, United Kingdom..

Robertson, C., Boachie, C., Burr, JM., Fraser, CM., MacLennan, GS., Mowatt, G., Ternent, L., Thomas, RE., Vale, LD., Gilbert, FJ., Dixon, M., Heys, SD., Wilson, R., Pinder, S., Bain, V., Donnelly, P., Jack, W., Kerr, G., Lawrence, G., Maxwell, A., McGregor, J. & Murchie, P. (2011).

NIHR Health Technology Assessment programme, Southampton, United Kingdom..