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OpenTrials
Enrolling by Invitation

NCT Number: NCT06838130

AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)

This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.

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Key information

Conditions

Breast Parenchimal Enhancement Artificial Intelligence (AI)

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

University of Campania Luigi Vanvitelli

Naples, 80138, Italy

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Patients who underwent CEM, mammography, and ultrasound between May 2022 and June 2023.

Availability of BPE assessment, BI-RADS density classification, and age data.

Complete dataset available for statistical and AI-based analysis.

Exclusion criteria

Patients with prior breast cancer treatment that could alter BPE.

Incomplete imaging or missing classification data.

Contraindications to contrast-enhanced imaging.

Treatment and study plan

Primary outcomes

  1. Correlation between breast density, BPE, and age using AI-driven analysis.

    Time frame: Data analysis within 12 months of study completion.

    Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression.

Secondary outcomes

  1. AI-based optimization of breast density and BPE classification

    Time frame: Within 12 months of study completion

    Evaluating the performance of neural networks in predicting BPE levels across different breast density categories.

  2. Comparative performance of multiple linear regression vs. AI models.

    Time frame: Within 12 months of study completion.

    Assessing the accuracy of traditional statistical methods versus ANN-based predictions in explaining variance in BPE values.

  3. Mean Squared Error (MSE) and explained variance in predictive models

    Time frame: Within 12 months of study completion

    Analyzing the error rates and variance explained by different AI models compared to multiple linear regression.

Sponsors and collaborators

Lead sponsor

Link Campus University

Other

Collaborators

  • University of Campania Luigi Vanvitelli

Registry information

Important dates

Study start
2022
Primary completion
2023
Study completion
2025
First posted
Feb 20, 2025
Registry last updated
Feb 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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