Developing an US-MRI Biomarker Fusion Model for Endometriosis
- Conditions
- Reproductive System DisorderEndometriosis
- Registration Number
- NCT04974710
- Lead Sponsor
- Perspectum
- Brief Summary
Single centre, prospective, observational, cohort study looking to develop a database representing the variability of disease and imaging seen in women with clinically diagnosed endometriosis, awaiting laparoscopic surgery.
- Detailed Description
In the United Kingdom (UK), endometriosis is one of the most common gynaecological diseases needing treatment. The prevalence of disease is often underestimated, however it is believed to affect at least 1 in 10 women in the UK. Within the NHS, endometriosis costs the UK economy approximately £8.2 billion a year in treatment, loss of work and healthcare costs.
Currently, the first diagnostic recommendation for endometriosis is and Ultrasound (US) scan or a MRI, followed by a diagnostic surgery called laparoscopy. Accurate diagnoses is usually limited to specialist tertiary centres, therefore a delayed diagnosis is a significant problem for women with endometriosis. Limited experience in the disease area can also lead to misdiagnosis and 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.
The main aim of this study is to curate a database of patients with varying levels of endometriosis. This database will contain fully anonymised MR and US images alongside clinical data for further use in research. There will be no intervention outside of standard of care. US and clinical data will be collected during routine visits and patients will be offered an additional visit to have a MRI. The ultimate aim is to then use this data to develop a widely available diagnostic tool based on MRI and US imaging modalities using computer modelling. Validating the predictive model with surgical findings will increase confidence and access to advanced imaging for non-experts, allowing clinicians to accurately predict surgical findings as well as reduce time to diagnosis.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Female
- Target Recruitment
- 100
- Women aged between 18-40 years
- Clinically diagnosed endometriosis and awaiting surgery
- BMI 20-35 kg/m2
- No past abdominal surgical history
- Participant willing and able to give informed consent for participation in the study
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Previous surgery in 12 months prior to consent:
- abdominal surgery
- surgery for endometriosis
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The participant may not enter the study if they have any contraindication to magnetic resonance imaging (standard MR exclusion criteria including pregnancy, extensive tattoos, pacemaker, shrapnel injury, severe claustrophobia).
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Any other cause, including a significant disease or disorder which, in the opinion of the investigator, may either put the participant at risk because of participation in the study, or may influence the participant's ability to participate in the study.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Build a database of 100 patients with endometriosis, awaiting confirmatory laparoscopic surgery on to an anonymised database for future use in algorithm development. 12 months
- Secondary Outcome Measures
Name Time Method Evaluate inter-observer variability in diagnosing and staging endometriosis using both two and three dimensional ultrasound by computing inter-rater agreement statistics (e.g. Kappa statistic) 12 months Assess the utility multi-parametric MRI in diagnosing and staging endometriosis 12 months Using measurements such as cT1, PDFF and Diffusion Weighted Imaging (DWI) to the MR imaging variables with a clinical diagnosis.
Trial Locations
- Locations (1)
King's Fertility
🇬🇧London, United Kingdom