Breast Density Impact on Mammographic Screening for Breast Cancer Diagnosis

Breast Cancer Screening, Breast Cancer Screening and Diagnosis

Breast cancer, Breast cancer screening, BRADS, Mammography, Dense breast, Breast imaging

This retrospective, observational study aims to evaluate how breast density affects the accuracy and outcomes of mammographic screening for breast cancer within the regional screening program "Prevenzione Serena". Breast density is an important factor because dense breast tissue can make it more difficult to detect breast cancer on a mammogram. Dense tissue and tumors both appear white on a mammogram, which may hide abnormalities and lead to missed cancers or false-positive results. Women aged 45 to 75 years who underwent routine mammographic screening at ASL CN2 between September 2023 and May 2024 will be included. Breast density will be classified using the BI-RADS system (categories A-D), and the study will assess whether women with dense breasts (categories C and D) experience higher rates of recalls for second-level examinations such as ultrasound, MRI, etc). The study also includes an internal validation of Insight BD, an automated breast-density measurement software used at ASL CN2. The software will be evaluated using a mammography phantom (to verify technical accuracy) and by comparing its BI-RADS density classifications with readings from two radiologists (one expert and one less experienced). This will help determine whether the software can support radiologists, especially in evaluating dense breast tissue. Additional factors such as menopausal status, family history of breast cancer, and hormone therapy will also be examined to understand how they relate to breast density and screening outcomes. The study aims to quantify the frequency of false-positive recalls-cases in which additional tests are recommended but cancer is not found-because these events can increase patient anxiety and healthcare workload. Ultimately, this research seeks to provide evidence that may inform future screening guidelines and support more personalized approaches, particularly for women with dense breasts.

This retrospective, monocentric, observational study investigates the impact of breast density on mammographic screening performance in the regional program "Prevenzione Serena," implemented at ASL CN2 (Piedmont, Italy). The primary objective is to evaluate the association between BI-RADS breast-density categories and the frequency of recalls for second-level diagnostic examinations among women aged 45-75 undergoing screening mammography between 25 September 2023 and 3 May 2024. Breast density is a known factor that can reduce the sensitivity of mammography. Dense fibroglandular tissue appears radiopaque and may mask suspicious lesions, leading to false-negative or false-positive examinations. Women with dense breasts (BI-RADS categories C-D) also have an independently increased risk of breast cancer. For these reasons, the study aims to characterize how breast density influences recall rates, diagnostic appropriateness, and overall screening performance in a real-world population. A secondary goal is the internal validation of Insight BD, an automated breast-density assessment software integrated into the Siemens Mammomat Revelation mammography system used at ASL CN2. The validation includes: 1. Technical validation using a dedicated mammographic phantom with known density values to determine measurement accuracy and repeatability; 2. Diagnostic validation through comparison between the BI-RADS density category assigned by the software and those assigned by two radiologists (one expert, one non-expert). The study will also examine associations between breast density and key clinical factors, including menopausal status, family history of breast cancer, and systemic hormone therapy. Furthermore, the frequency of false-positive recalls (additional testing without a final diagnosis of cancer) will be assessed, given their clinical, psychological, and organizational implications. The study aims to characterize density-related patterns in screening performance, quantify false-positive recalls, and contribute evidence to support future updates to breast-screening guidelines and potential personalized screening strategies, especially for women with dense breast tissue.

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