Clinical trial NCT06795711: Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis
Official title: Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis: a Prospective Observational Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Adenomyosis
Keywords: adenomyosis
gynaecology
ultrasound
Lead sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
Sponsor class: OTHER
First posted: 2025-01-28
Enrollment: 465
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients consecutively attending our ultrasound clinics for routine check-ups or pre-operative examinations will be included, as per regular practice. For the purposes of the analysis, patients will be divided into three groups according to the ultrasound and/or surgical diagnosis of A adenomyosis (presence of at least 2 ultrasound signs compatible with adenomyosis (MUSA) or presence of glands and endometrial stroma in myometrial location), B fibromatosis (uterus with inhomogeneous echostructure lacking 2 or more signs compatible with uterine adenomyosis or histological finding of several benign tumours consisting of smooth muscle tissue and fibrous tissue in varying proportions), C normal uterus (normal echostructure on transvaginal ultrasound)

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.

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

Primary outcomes:
measure: Definition of uterine biometric parameters
timeFrame: After enrollment on first visit
measure: Diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis
timeFrame: After enrollment on first visit
Secondary outcomes:
measure: Construction of deep learning models on uterine ultrasound images
timeFrame: After enrollment on first visit
measure: Evaluation of diagnostic accuracy of deep learning validated
timeFrame: After enrollment on first visit
measure: Identification of the frequency of finding ultrasound signs of adenomyosis in the cervix
timeFrame: After enrollment on first visit
measure: Evaluation of diagnostic accuracy
timeFrame: After enrollment on first visit
Other outcomes:
Locations:
city: Bologna
country: Italy
facility: IRCCS Azienda Ospedaliero-Universitaria di Bologna
Study officials:
affiliation: IRCCS Azienda Ospedaliero-Universitaria di Bologna
name: Diego Raimondo, MD
role: PRINCIPAL_INVESTIGATOR
References: