Validation and Implementation With Artificial Intelligence of Software for the Intra-procedural Assessment of Uterine Artery EMBOlization
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 250
- 试验地点
- 1
- 主要终点
- The primary outcome measure is the AUPRC of the predictive models.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 55 Years（Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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)
排除标准
- •Lack of clinical follow-up
- •Poor quality or incomplete angiographic images.
结局指标
主要结局
The primary outcome measure is the AUPRC of the predictive models.
时间窗: 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.
次要结局
未报告次要终点
研究者
Emanuele Barabino
Emanuele Barabino, MD, EBIR - Principal Investigator
IRCCS Azienda Ospedaliera Universitaria San Martino - IST Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy