沙特人群中乳腺钼靶密度与乳腺癌风险：一项采用视觉和自动方法的病例对照研究。
Mammographic breast density and breast cancer risk in the Saudi population: a case-control study using visual and automated methods.
作者信息
Aloufi Areej S, AlNaeem Abdulrahman N, Almousa Abeer S, Hashem Amani M, Malik Mehreen A, Altahan Fatina M, Elsharkawi Mahmoud M, Almasar Khalid A, ElMahdy Manal H, Squires Steven E, Alzimami Khalid S, Harkness Elaine F, Astley Susan M
机构信息
Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia.
出版信息
Br J Radiol. 2022 Jun 1;95(1134):20211197. doi: 10.1259/bjr.20211197. Epub 2022 Mar 9.
OBJECTIVE
This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods.
METHODS
1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest lowest quartiles in VAS, pVAS and Volpara Density, were estimated using conditional logistic regression.
RESULTS
For BI-RADS, the OR was 6.69 (95% CI 2.79-16.06) in the highest second category and OR = 4.78 (95% CI 3.01-7.58) in the highest lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86-14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68-10.77) and Volpara Density OR = 3.55, (95% CI 1.86-6.75) for highest lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65-0.75) demonstrating better discrimination between cases and controls than all other methods.
CONCLUSION
Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists' visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density.
ADVANCES IN KNOWLEDGE
Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.
目的
本研究旨在基于沙特女性的乳房密度确定乳腺癌风险，并比较使用不同乳房密度评估方法进行癌症预测的效果。
方法
回顾性收集了1140例沙特女性的假名筛查乳房X光片。采用乳房影像报告和数据系统（BI-RADS）密度分类以及视觉模拟量表（VAS）对285例病例和855例年龄及体重指数匹配的对照进行乳房密度评估。在160例病例和480例对照的子集中，通过两种自动方法Volpara Density和预测VAS（pVAS）估算密度。使用条件逻辑回归估计BI-RADS和Volpara密度分级中最高与第二等级之间以及VAS、pVAS和Volpara Density中最高与最低四分位数之间的比值比（OR）。
结果
对于BI-RADS，最高与第二等级之间的OR为6.69（95%可信区间2.79 - 16.06），VAS最高与最低四分位数之间的OR = 4.78（95%可信区间3.01 - 7.58）。在子集中，VAS是最强的预测指标，最高与最低四分位数之间的OR = 7.54（95%可信区间3.86 - 14.74），其次是使用原始图像的pVAS，OR = 5.38（95%可信区间2.68 - 10.77），Volpara Density最高与最低四分位数之间的OR = 3.55（95%可信区间1.86 - 6.75）。VAS的匹配一致性指数为0.70（95%可信区间0.65 - 0.75），表明其在区分病例和对照方面比所有其他方法都更好。
结论
乳房X光片密度增加与沙特女性乳腺癌风险密切相关。放射科医生对乳房密度的视觉评估优于自动方法。然而，pVAS和Volpara Density™也基于乳房密度显著预测了乳腺癌风险。
知识进展
我们的研究在沙特人群中建立了乳房密度与乳腺癌之间的关联，并比较了自动方法的性能。这为使用自动乳房密度方法进行个性化筛查奠定了基础。
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