Human-Level Computer-Aided Approach for BI-RADS Breast Density Classification: Multi-Reader, Multi-Centric Study

KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ, Jakub DANDÁR, Eva JANŮ a Anora ATAKHANOVA. Human-Level Computer-Aided Approach for BI-RADS Breast Density Classification: Multi-Reader, Multi-Centric Study. In European Congress of Radiology 2024, Vienna. 2024.

Základní údaje

• Originální název: Human-Level Computer-Aided Approach for BI-RADS Breast Density Classification: Multi-Reader, Multi-Centric Study
• Autoři: KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ, Jakub DANDÁR, Eva JANŮ a Anora ATAKHANOVA.
• Vydání: European Congress of Radiology 2024, Vienna, 2024.

Další údaje

• Typ výsledku: Konferenční abstrakt
• Utajení: není předmětem státního či obchodního tajemství
• Klíčová slova anglicky: Breast, Mammography, CAD, Cancer, Multidisciplinary cancer care
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

Breast cancer is the most common cancer type in women and is the main cause of cancer-related death in women population in the world [1]. The diagnosis of abnormalities in mammography studies is considered a difficult task because of variable sizes and shapes of lesions, difficult differentiation of lesion borders and some extremely small lesions. A pivotal factor affecting the detection of breast cancer is the density of the breast tissue, which can reduce visibility of tumors and is independently associated with an increased risk of breast cancer [2]. The Breast Imaging Reporting and Data System (BI-RADS) created by the American College of Radiology [3] offers a standardized classification system for breast density assessment. However, this system has faced challenges due to the substantial interobserver variability among radiologists, introducing uncertainty and inconsistency in evaluation [4,5,6]. The primary objective of this study is to evaluate the efficacy of a deep learning algorithm (DLA) in classifying breast tissue density according to the BI-RADS system. The study aims to address the challenge of interobserver variability among radiologists, thereby enhancing the accuracy and consistency of breast density assessments.