DPU - Symposium 2022

7 Symposium 2022 Curriculum Vitae: Prof. Sala is an academic radiologist with a special interest in Cancer Imaging. She is the Professor of Oncological Imaging at the University of Cambridge, UK. Previously, she served as Chief of Body Imaging Service at Memorial Sloan Kettering Cancer Center and Professor of Radiology, Weill Cornell Medical College in New York until December 2017. Before joining the Memorial Sloan Kettering in July 2012, she was a University Lecturer in Radiology and Specialty Teaching Director (Radiology) at the University of Cambridge, UK. She obtained her PhD from University of Cambridge, UK in 2000 and completed her training in Clinical Radiology at Cambridge, UK in August 2005. Today, Prof. Sala leads the Radiogenomics and Quantitative Imaging Group in the Department of Radiology. She is also active in many academic organizations. She is the Chair of Radiology Society of North America (RSNA) Oncologic Imaging Track, serves on the Oncologic Imaging and Therapies Task Force of RSNA and the Genitourinary Imaging Subcommittee of European Society of Radiology. She is a member of Board of Trustees of the International Society for Magnetic Resonance in Medicine (ISMRM), the International Cancer Imaging Society (ICIS) and The European Society of Urogenital Radiology (ESUR). Prof. Sala is an Editorial Board member and Head of Oncology Section of European Radiology. In recognition for her contribution to education and research in oncological imaging, she was elected as a Fellow of ICIS in 2014, a Fellow of ISMRM in 2015, a fellow of ESUR in 2018 and received the RSNA Honoured Educator Award in 2014 and 2017. Abstract: Integrated Radiogenomic for Unravelling Tumour Heterogeneity and Treatment Monitoring in Ovarian Cancer At every point along the cancer continuum of care, from early detection through to advanced disease, a patient must face decisions that may have a direct impact on their long-term outcomes. These decisions are increasingly complex as an ever-growing array of management options emerge, and highly complex data drive both individual patient care and clinical studies. Consequently, much uncertainty remains around how to optimize decisions for a given patient. At the root of the challenge is the profound clinical and genomic heterogeneity. Risk stratification based on standard clinical and pathologic features often has suboptimal performance. Moreover, tumour heterogeneity is the main driver of treatment resistance in any cancer but especially in in high grade serous ovarian cancer (HGSOC) as both primary tumours and metastatic lesions are spatially and temporally heterogeneous. They would require multiple biopsies to extract and Prof. Evis Sala, MD, PhD, FRCR Professor of Oncological Imaging University of Cambridge © HUEBL