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亚洲人群中自动乳腺体积密度软件与视觉评估的比较:一项回顾性观察研究。

Evaluation of automated volumetric breast density software in comparison with visual assessments in an Asian population: A retrospective observational study.

作者信息

Rahmat Kartini, Ab Mumin Nazimah, Ramli Hamid Marlina Tanty, Fadzli Farhana, Ng Wei Lin, Muhammad Gowdh Nadia Fareeda

机构信息

Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur.

Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.

出版信息

Medicine (Baltimore). 2020 Sep 25;99(39):e22405. doi: 10.1097/MD.0000000000022405.

Abstract

This study aims to compare Quantra, as an automated volumetric breast density (Vbd) tool, with visual assessment according to ACR BI-RADS density categories and to determine its potential usage in clinical practice.Five hundred randomly selected screening and diagnostic mammograms were included in this retrospective study. Three radiologists independently assigned qualitative ACR BI-RADS density categories to the mammograms. Quantra automatically calculates the volumetric density data into the system. The readers were blinded to the Quantra and other readers assessment. Inter-reader agreement and agreement between Quantra and each reader were tested. Region under the curve (ROC) analysis was performed to obtain the cut-off value to separate dense from a non-dense breast. Results with P value <.05 was taken as significant.There were 40.4% Chinese, 27% Malays, 19% Indian and 3.6% represent other ethnicities. The mean age of the patients was 57. 15%, 45.6%, 30.4%, and 9% of patients fall under BI-RADS A, B, C and D density category respectively. Fair agreement with Kappa (κ) value: 0.49, 0.38, and 0.30 were seen for Reader 1, 2 and 3 versus Quantra. Moderate agreement with κ value: 0.63, 0.64, 0.51 was seen when the data were dichotomized (density A and B to "non-dense", C and D to "dense"). The cut-off Vbd value was 13.5% to stratify dense from non-dense breasts with a sensitivity of 86.2% and specificity of 83.1% (AUC 91.4%; confidence interval: 88.8, 94.1).Quantra showed moderate agreement with radiologists visual assessment. Hence, this study adds to the available evidence to support the potential use of Quantra as an adjunct tool for breast density assessment in routine clinical practice in the Asian population. We found 13.5% is the best cut-off value to stratify dense to non-dense breasts in our study population. Its application will provide an objective, consistent and reproducible results as well as aiding clinical decision-making on the need for supplementary breast ultrasound in our screening population.

摘要

本研究旨在将作为自动乳腺体积密度(Vbd)工具的Quantra与根据美国放射学会(ACR)乳腺影像报告和数据系统(BI-RADS)密度分类进行的视觉评估相比较,并确定其在临床实践中的潜在用途。本回顾性研究纳入了500例随机选择的筛查和诊断性乳房X光片。三位放射科医生独立为乳房X光片指定定性的ACR BI-RADS密度分类。Quantra自动将体积密度数据计算到系统中。读者对Quantra和其他读者的评估不知情。测试了读者间的一致性以及Quantra与每位读者之间的一致性。进行曲线下面积(ROC)分析以获得区分致密型与非致密型乳房的临界值。P值<0.05的结果被视为具有统计学意义。其中40.4%为华裔,27%为马来族,19%为印度族,3.6%代表其他种族。患者的平均年龄为57岁。分别有15%、45.6%、30.4%和9%的患者属于BI-RADS A、B、C和D密度类别。与Quantra相比,读者1、2和3的Kappa(κ)值分别为0.49、0.38和0.30,一致性一般。当数据二分法分类(密度A和B为“非致密型”,C和D为“致密型”)时,κ值分别为0.63、0.64、0.51,一致性中等。区分致密型与非致密型乳房的Vbd临界值为13.5%,灵敏度为86.2%,特异度为83.1%(曲线下面积91.4%;置信区间:88.8, 94.1)。Quantra与放射科医生的视觉评估显示出中等一致性。因此,本研究补充了现有证据,支持在亚洲人群的常规临床实践中,将Quantra作为乳腺密度评估辅助工具予以潜在应用。我们发现13.5%是我们研究人群中区分致密型与非致密型乳房的最佳临界值。其应用将提供客观、一致且可重复的结果,并有助于对我们筛查人群中是否需要补充乳腺超声检查进行临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb9/7523847/64b5a4311aa8/medi-99-e22405-g001.jpg

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