Uterine Fibroids (UF)
Conditions
Keywords
uterine fibroids, embolization, uterine artery embolization, quantitative imaging, artificial intelligence
Brief summary
Uterine artery embolization is a minimally invasive treatment for symptomatic uterine fibroids, but intra-procedural assessment of embolization adequacy currently relies on subjective angiographic criteria. This study evaluates a proprietary angiographic analysis software (AQ-VERO) that extracts quantitative time-to-density perfusion metrics in real time. The study aims to (1) validate the accuracy and reproducibility of AQ-VERO during uterine artery mebolization, and (2) develop an AI-based decision support system using AQ-VERO-derived metrics to improve objective intra-procedural assessment of treatment endpoints.
Detailed description
Background and Rationale. Uterine fibroids affect up to 70-80% of women of reproductive age. Uterine artery embolization achieves technical success rates above 95% and symptom improvement in approximately 75-90% of patients; however, it is associated with a 20-30% cumulative risk of clinical failure or need for reintervention at 5 years. Current intra-procedural assessment of embolization adequacy is based on qualitative angiographic criteria (e.g., 5-10 heartbeats stasis, pruned tree appearance), which are subjective and operator-dependent. Emerging evidence suggests that achieving near-complete, rather than complete, flow stasis may reduce post-procedural pain, underscoring the need for quantitative and standardized assessment tools. AQ-VERO is an internally developed software platform that performs quantitative time-to-density (TTD) analysis of angiographic images to objectively quantify uterine and fibroid perfusion in real time. Objectives. Primary Objective: To validate the accuracy and intra-/interobserver reproducibility of AQ-VERO TTD metrics in quantifying perfusion changes during uterine artery embolization. Secondary Objectives: (a) To develop and internally validate an AI-based decision support model that uses AQ-VERO-derived metrics to identify predefined embolization endpoints; (b) To explore the correlation between intra-procedural TTD metrics and post-procedural clinical outcomes, including symptom improvement, early pain scores, and need for reintervention. Study Design. This is an ambispective (includes retrospective and prospective follow-up), multicenter observational study including women undergoing uterine artery embolization for symptomatic uterine fibroids. Standardized angiograms will be acquired and analyzed with AQ-VERO to extract TTD perfusion parameters (e.g., time-to-peak, area under the curve, wash-in/wash-out characteristics). Operators will document conventional qualitative angiographic endpoints. Clinical and imaging follow-up will be collected according to institutional protocols. Primary Objective: • To evaluate whether the AI predictive model developed using AQ-VERO© metrics can predict the clinical outcome, defined as complete or significant resolution of fibroid-related symptoms. Secondary Objectives: * To correlate distinct TTD curve morphologies and AQ-VERO metrics with post-procedural pain. * To detect the presence of collateral or accessory arterial supply that may compromise embolization efficacy. Significance. This study is expected to establish a quantitative and AI-augmented framework for intra-procedural embolization assessment during uterine artery embolization, potentially reducing variability and improving long-term clinical outcomes.
Interventions
None listed
Sponsors
Emanuele Barabino
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 55 Years
Healthy volunteers
No
Inclusion criteria
* Female patients ≥18 years * Symptomatic uterine fibroids (e.g., bleeding, bulk symptoms, pain) * Underwent UAE as definitive therapy * Availability of baseline clinical/imaging data (for retrospective arm) or ability to provide informed consent (for prospective arm)
Exclusion criteria
* Lack of clinical follow-up * Poor quality or incomplete angiographic images.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The primary outcome measure is the AUPRC of the predictive models. | From treatment to the end of the required follow-up (6 months). | The Area Under the Precision-Recall Curve (AUPRC) will be calculated to evaluate the performance of the AI-based decision support model in identifying clinically relevant embolization endpoints. AUPRC is a threshold-independent metric that summarizes the tradeoff between precision (positive predictive value) and recall (sensitivity) across all decision thresholds. It is particularly suitable for imbalanced datasets, where positive outcome events may be less frequent. Higher AUPRC values indicate better discriminative performance of the model. |
Countries
Italy
Contacts
Primary ContactEmanuele Barabino, MD
Outcome results
None listed