Collaborative Studies
Collaborative studies:
Here you can explore an overview of our current international research studies that are open for new onboarding centers. These studies aim to advance gynecological ultrasound diagnostics and artificial intelligence applications in clinical practice. Below, you will find summaries of our active studies — IOTA 7B, MY-IOTA, MUSA-1, and MUSA-AI — along with details on how your center can join.
IOTA 7B
The IOTA 7B study is the continuation of the IOTA-7 and IOTA-AI projects, focusing on improving the diagnosis of adnexal masses. It is an international multicentre prospective cohort study that aims to validate and compare different diagnostic approaches:
· Subjective expert ultrasound assessment
· The IOTA ADNEX model
· The IOTA two-step strategy
The study recruits women with newly detected adnexal masses and collects both clinical and imaging data(2D images, 3D volumes, and optionally video clips). These data are used to refine diagnostic models, validate risk predictions for malignancy, and support AI development in ultrasound imaging.
The ultimate goal is to provide more accurate, accessible, and standardized tools to distinguish between benign and malignant ovarian tumors, improving clinical decision-making and patient outcomes.
Are you interested in the study? Email us at: iota@uzleuven.be
MY-IOTA
MY-IOTA (Masses in Young Patients – International Ovarian Tumour Analysis) is an international, multicentre prospective cohort study focusing on adnexal masses in neonates, children, and adolescents up to 20 years old. Since most adnexal tumors in this age group are benign, yet surgery (often with removal of the ovary) is still frequently performed out of concern for malignancy, MY-IOTA seeks to provide reliable diagnostic evidence to reduce unnecessary interventions and safeguard fertility. The study prospectively collects standardized clinical data, ultrasound findings, and images using IOTA methodology, and follows patients up to 12 months to assess outcomes.
The main objective is to validate established IOTA tools — including the Simple Rules, Benign Descriptors, and ADNEX model — for distinguishing benign from malignant adnexal masses in this young population. Secondary aims include evaluating the value of subjective ultrasound assessment, describing the natural course of conservatively managed masses, and documenting complications such as torsion or rupture. With a target of over 1000 surgically managed cases (including at least 100 malignancies), MY-IOTA will build the largest dataset of its kind and generate much-needed evidence to guide safe, standardized, and fertility-preserving management of adnexal masses in children and adolescents.
Are you interested in the study? Email us at: iota@uzleuven.be
MUSA-1
MUSA-1 (Ultrasound evaluation of the myometrium using the MUSA terminology, comparison with histology) is a multicentre, prospective observational cohort sponsored by UZ Leuven (Coordinating Investigator: Prof. Dirk Timmerman). It applies the standardized 2015 MUSA terms to systematically describe myometrial and uterine mass features on preoperative ultrasound, and compares these findings with histology after surgery. Consecutive women ≥18 years scheduled for hysterectomy or myomectomy for clinically significant myometrial lesions (>1 cm) undergo structured scanning (gray scale + Doppler, encouraged 3D volumes) with ~50 predefined parameters recorded in a secure web database (Clinical Data Miner). The primary endpoint is the diagnostic accuracy of MUSA features to distinguish fibroids, adenomyosis, and malignant lesions (notably leiomyosarcoma/STUMP). The target sample is 2,000 patients over ~6 years, yielding ~70 malignant cases, making this the largest prospective series of its kind.
Secondary aims are to develop and internally validate a prediction model for malignancy using preselected ultrasound variables (outer contour, echogenicity, colour score, central necrosis, maximal diameter, acoustic shadows; logistic regression with Firth correction and cluster bootstrapping), and to assess safety and long-term outcomes of myomectomy. For the myomectomy subgroup, intra-/early postoperative complications (Clavien–Dindo), surgical metrics, and a 5-year follow-up capture symptom recurrence and quality of life (UFS-QOL), fertility/pregnancy outcomes, re-interventions, and parasitic fibroids. The study enforces quality control of imaging, encourages pathology correlation per lesion, excludes power morcellation, and follows GDPR-compliant pseudonymized data handling. Ultimately, MUSA-1 aims to deliver robust, generalizable diagnostic criteria and a practical risk model to guide safe, uterus-preserving management while minimizing missed sarcomas.
Are you interested in the study? Email us at: musa@uzleuven.be
MUSA-AI
MUSA-AI (Radiomics and Artificial Intelligence for Uterine Smooth Muscle Tumors) is a multicentre retrospective case–control study coordinated by UZ Leuven (Prof. Wouter Froyman). The project focuses on the pre-operative challenge of differentiating benign uterine myomas from rare malignant smooth muscle tumors such as leiomyosarcoma and STUMP. Current ultrasound diagnosis is often inconclusive, leading to unnecessary surgery or missed malignancy. By applying radiomics and artificial intelligence (AI) models to ultrasound images collected between 2010 and 2025, the study aims to create automated tools that can support clinicians in identifying suspicious lesions earlier and more reliably.
The study plans to include approximately 100 malignant/STUMP cases and 200 benign myoma controls, all with histological confirmation and available preoperative ultrasound. Data will be processed through advanced image analysis (segmentation, radiomics feature extraction, deep learning models) and validated against pathology results. Secondary goals include building a multicentre image database of myometrial tumors and testing the feasibility of AI-based segmentation and classification in routine practice. Ultimately, MUSA-AI seeks to develop robust, generalizable diagnostic algorithms that could be integrated into ultrasound systems, improving patient safety, surgical decision-making, and fertility-preserving care in women with uterine masses.
Are you interested in the study? Email us at: musa@uzleuven.be