A radiomics feature classification defining ovarian tumour aggression
Research type
Research Study
Full title
A radiomics feature classification approach to defining tumour aggressiveness in ovarian cancer
IRAS ID
184497
Contact name
Mubarik A Arshad
Contact email
Sponsor organisation
Imperial College London and Imperial College Healthcare NHS Trust
Duration of Study in the UK
1 years, 4 months, 2 days
Research summary
In the UK alone ovarian cancer affects around 7000 women each year and has poor prognosis, with high relapse rate and poor 10 year survival rate of about 35%. Poor survival is linked to advance clinical stage at presentation, sub-optimal tumour debulking and frequent development of chemotherapy resistance. Notably numerous scientifically interesting predictive/prognostic biomarkers have been identified, however, none are universally accepted for routine clinical use. It is against this background that we seek to develop a biomarker tool-kit based on the patient’s routine computed tomography (CT) scan – no significant additional cost to healthcare is envisaged since this will be part of routine care – that can predict disease aggressiveness (stage and extent) ultimately for use within the multidisciplinary team (MDT) setting in association with other metrics such as fitness of patient. CT, which by itself has limited prognostic information, forms an important first step in routine management of patients with ovarian cancer or when neoadjuvant therapy is recommended.
REC name
West Midlands - Black Country Research Ethics Committee
REC reference
15/WM/0237
Date of REC Opinion
1 Jul 2015
REC opinion
Favourable Opinion