乳腺钼靶体积密度的自动化测量：一种广泛用于乳腺癌风险评估的工具。

Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment.

作者信息

Brand Judith S, Czene Kamila, Shepherd John A, Leifland Karin, Heddson Boel, Sundbom Ann, Eriksson Mikael, Li Jingmei, Humphreys Keith, Hall Per

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California, San Francisco, California.

出版信息

Cancer Epidemiol Biomarkers Prev. 2014 Sep;23(9):1764-72. doi: 10.1158/1055-9965.EPI-13-1219. Epub 2014 Jul 10.

DOI:10.1158/1055-9965.EPI-13-1219

PMID:25012995

Abstract

INTRODUCTION

Mammographic density is a strong risk factor for breast cancer and an important determinant of screening sensitivity, but its clinical utility is hampered due to the lack of objective and automated measures. We evaluated the performance of a fully automated volumetric method (Volpara).

METHODS

A prospective cohort study included 41,102 women attending mammography screening, of whom 206 were diagnosed with breast cancer after a median follow-up of 15.2 months. Percent and absolute dense volumes were estimated from raw digital mammograms. Genotyping was performed in a subset of the cohort (N = 2,122). We examined the agreement by side and view and compared density distributions across different mammography systems. We also studied associations with established density determinants and breast cancer risk.

RESULTS

The method showed good agreement by side and view, and distributions of percent and absolute dense volume were similar across mammography systems. Volumetric density was positively associated with nulliparity, age at first birth, hormone use, benign breast disease, and family history of breast cancer, and negatively with age and postmenopausal status. Associations were also observed with rs10995190 in the ZNF365 gene (P < 1.0 × 10(-6)) and breast cancer risk [HR for the highest vs. lowest quartile, 2.93; 95% confidence interval, 1.73-4.96 and 1.63 (1.10-2.42) for percent and absolute dense volume, respectively].

CONCLUSIONS

In a high-throughput setting, Volpara performs well and in accordance with the behavior of established density measures.

IMPACT

Automated measurement of volumetric mammographic density is a promising tool for widespread breast cancer risk assessment.

摘要

引言

乳腺钼靶密度是乳腺癌的一个强有力的风险因素，也是筛查敏感性的一个重要决定因素，但其临床应用因缺乏客观和自动化的测量方法而受到阻碍。我们评估了一种全自动容积测量方法（Volpara）的性能。

方法

一项前瞻性队列研究纳入了41102名接受乳腺钼靶筛查的女性，其中206名在中位随访15.2个月后被诊断为乳腺癌。从原始数字乳腺钼靶图像中估计致密体积百分比和绝对致密体积。在队列的一个子集中（N = 2122）进行基因分型。我们检查了双侧和不同投照位的一致性，并比较了不同乳腺钼靶系统的密度分布。我们还研究了与既定密度决定因素和乳腺癌风险的关联。

结果

该方法在双侧和不同投照位显示出良好的一致性，不同乳腺钼靶系统的致密体积百分比和绝对致密体积分布相似。容积密度与未生育、初产年龄、激素使用、良性乳腺疾病和乳腺癌家族史呈正相关，与年龄和绝经状态呈负相关。还观察到与ZNF365基因中的rs10995190存在关联（P < 1.0 × 10⁻⁶）以及与乳腺癌风险的关联[最高四分位数与最低四分位数相比的风险比，百分比致密体积为2.93；95%置信区间为1.73 - 4.96，绝对致密体积为1.63（1.10 - 2.42）]。