Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-26 @ 2:30 PM
Ignite Modification Date:
2025-12-26 @ 2:30 PM
NCT ID:
NCT07156006
Brief Summary:
The goal of this observational study is to assess if there is an association between the presence of BAC and traditional cardiovascular risk factors and validate a Convolutional Neural Network (CNN) for the automatic segmentation of Breast Arterial Calcifications (BAC) in mammographic images. This study focuses on understanding the potential of BAC as an imaging biomarker for cardiovascular risk.
The main questions it aims to answer are:
* Is there an association between the presence of BAC and traditional cardiovascular risk factors?
* Can a CNN accurately segment BAC in mammographic images?
* What is the correlation between BAC and White Matter Hyperintensities (WMH) detected through brain MRI?
Participants in this study will be individuals who undergo mammographic screening. The main tasks participants will be asked to do include providing consent for participation and having mammographic images and a blood sample taken. The study will use a comparison group, comparing individuals with BAC to those without BAC, to assess potential effects on cardiovascular risk.
Detailed Description:
Association between BAC and Cardiovascular Risk Factors
* Traditional cardiovascular risk factors will be analyzed, and statistical tests (t-test or U de Mann-Whitney) will be employed based on the data distribution.
* Multivariate analysis will be performed to determine the independent association between BAC load and cardiovascular risk factors.
* Linear regression will assess the relationship between BAC load and Framingham score, aiming for a clinically applicable model.
Development of CNN for BAC Segmentation
* Mammographic images will be acquired using a digital full-field mammography system as per clinical practice.
* Two experienced operators will manually segment the images to create a dataset for training, validation, and testing the CNN.
* About 60% of the images acquired in the first year will be used for training, and the remaining 40% will form the validation and test datasets.
* Performance evaluation of the CNN will be conducted using the Sørensen similarity index, Bland-Altman analysis, and Free Response Receiver Operating Characteristic (FROC).
Association between BAC and White Matter Hyperintensities (WMH)
* A subset of participants will undergo brain MRI to assess WMH.
* The association between BAC quantity in mammography and WMH load in MRI will be evaluated using machine learning techniques.
* Other small vessel disease markers, such as lacunar infarcts and microbleeds, will also be analyzed.
Patient Enrollment:
The study aims to enroll 600 women, considering a 1:1 ratio between cases and controls. With an estimated 50% adherence rate, it anticipates evaluating 1500 women over two years.
This comprehensive study integrates the development of advanced imaging techniques with clinical correlations to explore the potential of BAC as an imaging biomarker for cardiovascular risk assessment.
Study:
NCT07156006
Study Brief:
Protocol Section:
NCT07156006