Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis

NCT ID: NCT06795711

Last Updated: 2025-01-28

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 465 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-04-04
• Study Completion Date: 2025-03-31

Brief Summary

Defining ultrasound criteria for normal uterine biometry and assessing the prevalence of repeat abortions in patients with abnormalities of the uterine cavity

Detailed Description

Adenomyosis is a gynaecological disorder with a high prevalence in women of childbearing age and is characterised by the presence of glands and endometrial stroma within the myometrium, associated or not with hypertrophy and hyperplasia of the surrounding myometrium. Adenomyosis may cause pelvic pain and/or abnormal uterine bleeding. Transvaginal ultrasound may be considered the main non-invasive diagnostic modality for the diagnosis of adenomyosis. The aim is to optimise the ultrasound diagnosis of uterine pathology and in particular of adenomyosis by defining uterine biometric parameters (longitudinal, transverse and anteroposterior diameters and their ratios; uterine volume) allowing patients to be divided into 3 groups:

• Uterus affected by adenomyosis (group A): adenomyosis is a gynaecological condition with high prevalence in women of childbearing age and is characterised by the presence of endometrial tissue (innermost layer of the uterus) within the uterine muscle. Adenomyosis can cause abdominal pain and abnormal uterine bleeding.
• Uterus affected by fibromatosis (group B): uterine fibromatosis is a gynaecological condition characterised by the appearance of numerous fibroids in the uterus. It is a very frequent condition in the general population and its frequency increases as the age of the patients increases.
• Normal uterus (group C). Transvaginal ultrasound, although a reference diagnostic tool, still remains an operator-dependent examination to date: our secondary objective is to build models that can simplify diagnosis through the use of artificial intelligence. The aim is to create various artificial intelligence software that can 'learn to make a diagnosis'. This method has already been applied in radiology, proving capable of discriminating between benign and malignant tumours from images from different diagnostic methods with performance similar to that of experienced radiologists.

Conditions

Adenomyosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• age between 18 and 60;
• obtaining informed consent

Exclusion Criteria

• Hysterectomised patients;
• Virgo patients (hymenal integrity);
• Patients reporting intolerance to transvaginal ultrasound;
• Gynaecological oncology;
• Recent pregnancy or childbirth (within 6 months);
• Menopausal patients

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 60 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

IRCCS Azienda Ospedaliero-Universitaria di Bologna

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Diego Raimondo, MD

Role: PRINCIPAL_INVESTIGATOR

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Locations

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Bologna, Bologna, Italy

Site Status: RECRUITING

Countries

Italy

Central Contacts

Diego Raimondo, MD

Role: CONTACT

die.raimondo@gmail.com

+393290636618

Facility Contacts

Diego Raimondo, MD

Role: primary

die.raimondo@gmail.com

+393290636618

Other Identifiers

ADENAS

Identifier Type: -

Identifier Source: org_study_id