MY-IOTA
Research type
Research Study
Full title
Masses in Young Patients - International Ovarian Tumour Analysis (MY-IOTA) Study. Prospective Validation and Comparison of Simple Rules, Benign Descriptors and ADNEX Models for Discrimination between Benign and Malignant Adnexal Masses in young girls and adolescent
IRAS ID
329324
Contact name
Tom Bourne
Contact email
Sponsor organisation
Imperial College London
Duration of Study in the UK
3 years, 0 months, 1 days
Research summary
Ovarian cysts are lumps or sacs on the ovary, which broadly fall into four groups:
1. Physiological – in other words, cysts that naturally occur as part of the normal function of the ovary
2. Benign – harmless cysts which develop on the ovary
3. Borderline – cysts which have changes which are pre-cancerous. These are very uncommon in young people.
4. Malignant – cysts which are cancerous. These are also very uncommon and account for <1% of all cancers found in children and young peopleIt’s important for us to be able to tell what type of cyst has formed, as we would not want to inappropriately operate on someone when a cyst is harmless and risk removing healthy ovary. Equally, we want to be sure that appropriate treatment is offered if there are any features of a cyst which make us worried it could be borderline or malignant (cancerous or pre-cancerous).
There is a lot of research to guide us on the typical appearances of cysts in adults, however there is much less information available about cysts in children and young people. We are interested in the ways different ovarian cysts look on ultrasound scan and how they change over time. The main things we are looking at are:
• How cysts can look in babies, children and young adults, from birth to 20 years of age
• If we can use the same tools that we use when looking at adult cysts in people aged under 20
• How ovarian cysts in children and young people change over timeThere are also some other things we are interested in, including:
• If a computer can learn to look at cysts and help us to correctly diagnose the (machine learning or artificial intelligence)
• If there are new biomarkers (i.e. blood tests) which can help us to diagnose certain cystsREC name
London - City & East Research Ethics Committee
REC reference
23/LO/0988
Date of REC Opinion
13 Dec 2023
REC opinion
Favourable Opinion