Andrea Rockall is Clinical Chair of Radiology, Imperial College London and Hon Consultant Radiologist at Imperial College Healthcare NHS Trust and at The Royal Marsden Hospital. Following graduation from King’s College Hospital, London in 1990 she trained in radiology at St Mary’s Hospital and University College Hospital, London. She was awarded the Rohan Williams Medal (Gold Medal) for the FRCR examination. In 2000, she was appointed as Senior Lecturer in Diagnostic Imaging at Queen Mary University London and Honorary Consultant Radiologist at Barts and The London NHS Trust and has worked at both Imperial and Royal Marsden Hospital since 2012, prior to her current appointment.
Her special interests are in genitourinary cancer, image-based clinical trials, functional imaging in response assessment and machine-learning applications in radiology. She is currently the Chief Investigator of the CRUK MAPPING trial in cervix and endometrial cancer and three NIHR trials MALIBO, MALIMAR (machine learning studies) and MROC (multicenter UK trial evaluating multi-parametric MRI in suspected or confirmed ovarian cancer).
et al., 2021, Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018 (Apr, 10.1007/s00330-020-07632-9, 2021), European Radiology, ISSN:0938-7994
et al., 2021, O-RADS MRI score: analysis of misclassified cases in a prospective multicentric European cohort, European Radiology, ISSN:0938-7994
et al., 2021, An Inter-observer Study to Determine Radiotherapy Planning Target Volumes for Recurrent Gynaecological Cancer Comparing Magnetic Resonance Imaging Only With Computed Tomography-Magnetic Resonance Imaging, Clinical Oncology, Vol:33, ISSN:0936-6555, Pages:307-313
et al., 2021, Ovarian-Adnexal Reporting Lexicon for MRI: A White Paper of the ACR Ovarian-Adnexal Reporting and Data Systems MRI Committee, Journal of the American College of Radiology, Vol:18, ISSN:1546-1440, Pages:713-729
et al., Multiple instance learning with auxiliary task weighting for multiple myeloma classification, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)