DEFEND

  • Research type

    Research Study

  • Full title

    Developing an US-MRI biomarker fusion model for Endometriosis

  • IRAS ID

    281860

  • Contact name

    Ippokratis Sarris

  • Contact email

    ippokratis@kingsfertility.org

  • Sponsor organisation

    Perspectum Ltd

  • Duration of Study in the UK

    2 years, 11 months, 31 days

  • Research summary

    This study will be an observational study conducted at Kings Fertility involving 100 women with endometriosis. It is primarily designed to collect information from women with endometriosis and use data from ultrasound (US) scans and magnetic resonance imaging (MRI) to develop a database representing the variability of the disease.

    Currently the first recommended diagnostic investigation for endometriosis is a US scan or a MRI followed by a diagnostic surgery - laparoscopy (the insertion of a tube into the abdomen under general anaesthetic). Unfortunately, the current mode of practice is subjective and its utility at predicting surgical findings of endometriosis is limited. Furthermore, accurate prediction using US and MRI is limited to selected tertiary units where high-level expertise exists. The latest report from the National Institute of Clinical Excellence (NICE) reports a time delay of around 7.5 years before a confirmed diagnosis of endometriosis.

    A model that could accurately predict surgical findings of endometriosis would be of significant clinical and economical benefit. With the information collected, we propose to develop an intelligent predictive model that will accurately predict endometriosis by fusing different MRI sequences; as well as MRI and ultrasound image fusion and ultrasound/ultrasound fusion. In addition to image fusion, we aim to incorporate a biomarker (MDNA) as an additional predictive tool. Its significance, when used in combination with imaging findings to predict endometriotic nodule proliferation, has not been robustly studied before.

    By validating our predictive model with surgical findings, we aim to develop a widely available diagnostic tool based on image fusion and biomarker using computer modelling. This will increase confidence and access to advanced imaging for non-experts, allow clinicians to accurately predict surgical findings as well as reduce time to diagnosis.

  • REC name

    West of Scotland REC 5

  • REC reference

    20/WS/0111

  • Date of REC Opinion

    27 Aug 2020

  • REC opinion

    Further Information Favourable Opinion