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

  tom.bourne@nhs.net

• 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 people

  It’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 time

  There 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 cysts

• REC name

  London - City & East Research Ethics Committee

• REC reference

  23/LO/0988

• Date of REC Opinion

  13 Dec 2023

• REC opinion

  Favourable Opinion