Department of Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
Deepwise AI Lab, Beijing, China.
Clin Breast Cancer. 2021 Jun;21(3):256-262.e2. doi: 10.1016/j.clbc.2020.10.011. Epub 2020 Nov 2.
Contrast-enhanced mammography (CEM) is a novel breast imaging technique that can provide additional information of breast tissue blood supply. This study aimed to test the possibility of CEM in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions with consideration of morphology and distribution.
Data of patients with suspicious malignant calcification-only lesions (BI-RADS 4) on low-energy CEM and proved pathologic diagnoses were retrospectively collected. Two junior radiologists independently reviewed the two sets of CEM images, low-energy images (LE) to describe the calcifications by morphology and distribution type, and recombined images (CE) to record the presence of enhancement. Low-risk and high-risk groups were divided by calcification morphology, distribution, and both, respectively. Positive predictive values and misdiagnosis rates (MDR) were compared between LE-only reading and CE reading. Diagnostic performance was also tested using machine learning method.
The study included 74 lesions (26 malignant and 48 benign). Positive predictive values were significantly higher and MDRs were significantly lower using CE images than using LE alone for both the low-risk morphology type and low-risk distribution type (P < .05). MDRs were significantly lower when using CE images (18.18%-24.00%) than using LE images alone in low-risk group (76.36%-80.00%) (P < .05). Using a machine learning method, significant improvements in the area under the receiver operating characteristic curve were observed in both low-risk and high-risk groups.
CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more.
对比增强乳腺摄影(CEM)是一种新的乳腺成像技术,可以提供乳腺组织血液供应的额外信息。本研究旨在测试 CEM 改善乳腺影像报告和数据系统(BI-RADS)4 类仅有钙化病变的诊断准确性的可能性,同时考虑形态和分布。
回顾性收集了低能 CEM 检查怀疑恶性仅有钙化病变(BI-RADS 4)的患者数据,并进行了病理证实。两位初级放射科医生分别独立地阅读两组 CEM 图像,低能图像(LE)用于描述形态和分布类型的钙化,重组图像(CE)用于记录增强的存在。根据钙化形态、分布和两者,分别将低危和高危组进行分组。比较了 LE 阅读和 CE 阅读之间的阳性预测值和误诊率(MDR)。还使用机器学习方法测试了诊断性能。
本研究共纳入 74 个病变(26 个恶性和 48 个良性)。对于低危形态类型和低危分布类型,CE 图像的阳性预测值明显高于 LE 图像,误诊率明显低于 LE 图像(P<0.05)。在低危组中,CE 图像(18.18%-24.00%)的误诊率明显低于 LE 图像(76.36%-80.00%)(P<0.05)。使用机器学习方法,在低危和高危组中,受试者工作特征曲线下面积均有显著提高。
CEM 有可能有助于诊断 BI-RADS 4 类仅有钙化病变;特别是那些形态和/或分布呈低危的病变可能受益更多。