Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis

Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis: a Prospective Observational Study

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

Study Overview

• Status: Recruiting
• Conditions: Adenomyosis

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.

• Study Type: Observational
• Enrollment (Estimated): 465

Contacts and Locations

• Study Contact: Name: Diego Raimondo, MD; Phone Number: +393290636618; Email: die.raimondo@gmail.com

Study Locations

• Italy
  • Bologna, Italy, 40138
    • Recruiting
    • IRCCS Azienda Ospedaliero-Universitaria di Bologna
    • Contact: Diego Raimondo, MD; Phone Number: +393290636618; Email: die.raimondo@gmail.com
    • Contact: Diego Raimondo, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• 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)

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Definition of uterine biometric parameters	Definition of uterine biometric parameters for the diagnosis of adenomyotic uterus (group A), fibromatous uterus (group B) and normal uterus (group C) by means of transvaginal ultrasound, performed as per the care procedure. Evaluation of the diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis as an additional parameter to those already known in the literature with possible subsequent identification of a biometric cut-off	After enrollment on first visit
Diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis	Evaluation of the diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis as an additional parameter to those already known in the literature with possible subsequent identification of a biometric cut-off	After enrollment on first visit

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Construction of deep learning models on uterine ultrasound images	Construction of deep learning models trained, validated and tested on uterine ultrasound images for the ultrasound diagnosis of adenomyosis and evaluation of their diagnostic accuracy	After enrollment on first visit
Evaluation of diagnostic accuracy of deep learning validated	Evaluation of diagnostic accuracy of deep learning validated for ultrasound diagnosis of adenomyosis	After enrollment on first visit
Identification of the frequency of finding ultrasound signs of adenomyosis in the cervix	In patients with a diagnosis of adenomyosis made on the basis of ultrasound features at the level of the uterine body and fundus	After enrollment on first visit
Evaluation of diagnostic accuracy	Evaluation of the diagnostic accuracy of trainees when experienced (identifying experienced operators as doctors in specialised training in Gynaecology and Obstetrics for at least four years, with an experience of at least 500 gynaecological ultrasound cases) and moderately experienced (identifying moderately experienced operators as doctors in specialised training in Gynaecology and Obstetrics for at least two years, with an experience of at least 200 gynaecological ultrasound cases	After enrollment on first visit

Collaborators and Investigators

• Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
• Investigators: Principal Investigator: Diego Raimondo, MD, IRCCS Azienda Ospedaliero-Universitaria di Bologna

Study record dates

Study Major Dates

• Study Start (Actual): April 4, 2022
• Primary Completion (Actual): December 31, 2024
• Study Completion (Estimated): March 31, 2025

Study Registration Dates

• First Submitted: December 3, 2024
• First Submitted That Met QC Criteria: January 21, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 21, 2025
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Keywords: ultrasound; adenomyosis; gynaecology
• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Uterine Diseases; Genital Diseases, Female; Adenomyosis
• Other Study ID Numbers: ADENAS

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No