Automatic Quantification of Breast Arterial Calcifications as an Imaging Biomarker of Cardiovascular Risk (the BAKER Study)
试验速览
- 阶段
- 不适用
- 状态
- 终止
- 发起方
- 入组人数
- 149
- 试验地点
- 1
- 主要终点
- Association Between BAC and Cardiovascular Risk Factors
研究概览
简要总结
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.
详细描述
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)
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 40 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Female participants. Consent to undergo mammography screening. Agreement to participate in brain MRI for a subset of the study.
排除标准
- •Male participants. Age below
- •Inability or unwillingness to undergo mammography screening. Contraindications for brain MRI, including the presence of pacemaker, intracranial ferromagnetic vascular clips, intraocular metallic fragments, severe claustrophobia, inability to maintain a supine position, involuntary movements, or pregnancy.
- •Known history of breast cancer. Previous reductive breast surgery.
结局指标
主要结局
Association Between BAC and Cardiovascular Risk Factors
时间窗: One observation at the time of the mammography examination. Total time frame: 1 day.
Methodology: This aspect of the study aims to assess the association between the burden of BAC and traditional cardiovascular risk factors. Parametric and non-parametric tests will be employed to evaluate differences in BAC burden based on the presence or absence of traditional cardiovascular and gynecological risk factors. Implications: A positive association between BAC burden and cardiovascular risk factors may emphasize the potential of BAC as a biomarker for cardiovascular risk.
次要结局
- Diagnostic Performance of CNN Detection and Quantification of BAC on Mammograms(One observation at the time of the mammography examination. Total time frame: 1 day.)
研究者
Francesco Sardanelli
Director
IRCCS Policlinico S. Donato