Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.
Prevent Breast Cancer and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Southmoor Road, Wythenshawe, Manchester, M23 9LT, UK.
Breast Cancer Res. 2018 Feb 5;20(1):10. doi: 10.1186/s13058-018-0932-z.
High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening.
Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors.
The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05).
Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.
高乳房密度与乳房 X 光检查中漏诊癌症的风险以及乳腺癌发病风险增加有关。乳腺癌预防和筛查的分层需要预测癌症的乳房密度测量。本研究比较了五种乳房密度测量方法，以确定与随后的乳腺癌诊断和筛查时乳腺癌存在的关联。
参加“预测筛查时癌症风险”（PROCAS）研究的女性完成了问卷调查，提供个人信息，以便计算 Tyrer-Cuzick 风险评分。在研究入组时，通过视觉模拟量表（VAS）、阈值（Cumulus）和全自动方法（Densitas、Quantra、Volpara）对 366 名单侧乳腺癌（病例）患者的对侧乳房进行了评估，其中 311/366 名患者在筛查中发现乳腺癌，338 名患者随后发现癌症。每例病例匹配 3 名年龄、体重指数类别、激素替代疗法使用和绝经状态匹配的对照。使用控制变量逻辑回归，根据对照中密度分布，对每个密度测量的最高和最低五分位数进行调整，以估计经典风险因素后，每个密度测量的优势比（OR）。
在研究入组时，VAS 是筛查发现癌症的最强预测因子，与最低五分位数相比，最高五分位数的 OR 为 4.37（95%CI 2.72-7.03），经经典风险因素调整后。与最低五分位数相比，Volpara、Densitas 和 Cumulus 的最高五分位数的 OR 分别为 2.42（95%CI 1.56-3.78）、2.17（95%CI 1.41-3.33）和 2.12（95%CI 1.30-3.45）。Quantra 与乳腺癌无显著相关性（OR 1.02，95%CI 0.67-1.54）。对于随后的癌症，VAS、Volpara 和 Densitas 的最高五分位数与最低五分位数的 OR 分别为 4.48（95%CI 2.79-7.18）、2.87（95%CI 1.77-4.64）和 2.34（95%CI 1.50-3.68），结果相似。与最低五分位数相比，Quantra 的最高五分位数的 OR 为 1.32（95%CI 0.85-2.05）。
尽管存在已知的观察者间变异性，但视觉密度评估与癌症有很强的关系；然而，它不适用于基于人群的筛查。Volpara 和 Densitas 测量的百分比密度也与乳腺癌风险有很强的关联，在评估的自动化方法中，为风险分层提供了实用的自动化方法。
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