Eom Hye Joung, Cha Joo Hee, Choi Woo Jung, Cho Su Min, Jin Kiok, Kim Hak Hee
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Acta Radiol. 2024 Jul;65(7):708-715. doi: 10.1177/02841851241257794. Epub 2024 Jun 2.
Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated.
To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories.
A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates.
Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%.
The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).
基于人工智能的计算机辅助诊断（AI-CAD）在乳腺钼靶检查中应用日益广泛，其在乳腺密度评估中的作用有待评估。
使用乳腺影像报告和数据系统（BI-RADS）密度分类，评估放射科医生、自动体积密度测量程序（Volpara）和AI-CAD系统在乳腺密度分类中的不同模态间一致性。
对2022年12月至2023年1月在我们健康体检中心为亚洲女性患者（平均年龄 = 56岁±10岁）进行的1015例筛查数字乳腺钼靶影像进行回顾性研究。四名具有不同经验水平的放射科医生（专家级和普通放射科医生）进行密度评估。使用加权kappa统计量和匹配率评估放射科医生、Volpara和AI-CAD（Lunit INSIGHT MMG）之间的一致性。
专家级和普通放射科医生之间的阅片者间一致性较高（κ = 0.65），匹配率为72.8%。专家级或普通放射科医生与Volpara之间的一致性较高（κ = 0.64 - 0.67），匹配率为72.0%，但专家级或普通放射科医生与AI-CAD之间的一致性为中等（κ = 0.45 - 0.58），匹配率为56.7% - 67.0%。Volpara和AI-CAD之间的一致性为中等（κ = 0.53），匹配率为60.8%。
放射科医生与自动体积密度测量程序（Volpara）在乳腺密度分类中的一致性高于放射科医生与AI-CAD（Lunit INSIGHT MMG）之间的一致性。
