TOWARDS NOVEL DIAGNOSTIC MARKER BASED ON BREAST ARTERIAL CALCIFICATIONS

Abstract

In recent years, medical image analysis has advanced significantly, mainly due to the development of computer vision tools. With AI becoming more and more prevalent, these technologies offer tremendous potential for the field of radiology. Novel computer-aided detection (CAD) methods can aid radiologists by reducing their workload, decreasing costs, speeding up diagnoses, and providing supporting tools for more accurate diagnostic assessments. Our latest study on Breast Arterial Calcification (BAC) demonstrates how these cutting-edge CAD systems can seamlessly integrate into clinical practice, ultimately enhancing patient care efficiency and effectiveness. Recent literature [1-3] shows that BAC could become a cardiovascular disease (CVD) biomarker. In collaboration with Radboud UMC (Nijmegen,The Netherlands), we evaluated a novel BAC detector capable of identifying BAC occurrence in screening mammograms. It is particularly challenging since even well-trained radiologists often differ in BAC grade assessments. Detecting and quantifying early BAC stages, which are not apparent in the mammograms, is crucial for accurate CVD assessment. Using advanced imaging techniques and statistical modelling, our research aims to determine BAC prevalence and severity concerning CVD outcomes, including stroke and coronary artery disease. We achieved a 70% accuracy rate in BAC detection from 500 mammograms annotated bytworadiologists via consensus. It is a noteworthy achievement given the heterogeneous, imbalanced data and weak labelling provided byradiologists. However, we are still looking for a sufficient pool of annotated parallel BAC-CVD cases, which is essential for further study. Although annotation is costly, and quality varies with radiologist expertise, we aim to expand our dataset to include data from other institutions to provide more generalizable results, preferably independent of demographic biases.