中国女性乳腺密度与乳腺癌无关

作者：乳腺杂志 来源：SIBCS

乳腺癌是中国女性最常见的肿瘤，也是女性癌症死亡的主要原因之一，但是中国女性的乳腺癌风险因素尚不完全明确【1】。与西方女性相关的主要风险因素之一为乳腺钼靶密度，但是既往使用乳腺影像报告和数据系统（BI-RADS）技术评定出来的乳腺密度，在读片者之间和读片者自身存在较大不同【2】。因此，越来越多的量化技术被推荐用于评定该重要的指标【3】。

2017年11月29日，全国乳腺中心联盟（原美国乳腺疾病学会）官方期刊《乳腺杂志》在线发表澳大利亚悉尼大学、复旦大学附属肿瘤医院的研究评论，着重通过乳腺密度定量方法对中国女性乳腺癌的风险因素进行了分析。

该研究经悉尼大学人类研究伦理委员会批准（项目编号：2014/768），于2015年3月～2016年6月在复旦大学附属肿瘤医院随机抽取84位乳腺癌女性和987位无乳腺癌女性。乳腺癌女性由复旦大学附属肿瘤医院在其住院期间确诊，其他无乳腺癌女性由复旦大学附属肿瘤医院组织的乳腺癌筛查研究（BCST）入组。通过乳腺癌女性的入院病史和出院小结、无乳腺癌女性的乳腺癌筛查研究问卷，获取人口统计学、生活方式、生育特征。通过头足轴位获取所有女性两个乳房的乳腺钼靶。通过自动算法计算乳腺密度【4】，该算法可确定乳腺钼靶的致密区域和乳房区域，并对乳腺密度进行归类。使用t检验和卡方检验评定有和无乳腺癌女性之间的特征差异，再对t检验或卡方检验有统计学意义的变量进行二元逻辑回归，得出比值比和95%置信区间。该检验排除任何一类发生率为0的分类变量。根据绝经情况，将整个数据组分为两个亚组，一组为绝经前女性，另一组为绝经后女性，对每个亚组重复上述统计学检验。

结果发现，有统计学意义的因素包括乳房较大、年龄较大、体重指数较高、初潮年龄较大、首次分娩时间较早、母乳喂养持续时间较长、绝经后、子女数量较多、母乳喂养史。不过，本文其余部分将重点讨论对乳腺密度的研究结果。

该研究未能根据百分比或致密区域参数确定乳腺密度与乳腺癌的关系，该发现与既往研究一致而又不一致：一项既往研究从4个中国城市的筛查分别入组86位和28302位有和无乳腺癌女性，与本研究样本量大小相似，也表明密度与乳腺癌之间无关【5】；相反，另一项入组2527位乳腺癌和3394位无乳腺癌女性的大型横断面研究发现，乳腺癌女性与无乳腺癌女性相比，40～49岁年龄组的乳腺密度较低，55～71岁年龄组的乳腺密度较高，但是50～54岁年龄组的乳腺密度相似【6】。本研究与后者之间的差异，可能由于本研究未评定年龄相关差异，从而模糊了具体读片结果。不过，本研究着重根据绝经状态对女性进行归类。另一个可能的解释是，与其他使用定性（即BI-RADS分类）评定的研究不同，本研究使用定量方法评定乳腺密度，因此可能影响结果，但是该影响的可能性需要进一步研究。尽管如此，根据本研究结果和既往研究结果，中国女性乳腺密度与乳腺癌之间的相关性可能不如其他人群，尤其那些入组西方女性的人群。该假设将需要进一步研究以证实或证伪。

总之，本研究表明了中国女性乳腺癌的风险因素，尤其着重乳腺密度的定量方法。乳腺癌与乳腺密度之间缺乏相关性，可能对乳腺癌筛查策略产生重大影响。

Mammographic density and other risk factors for breast cancer among women in China.

Li T, Tang L, Gandomkar Z, Heard R, Mello-Thoms C, Shao Z, Brennan P.

The University of Sydney, Lidcombe, NSW, Australia; Fudan University Shanghai Cancer Center, Shanghai, China.

Breast cancer is the most common neoplasm diagnosed among females in China and it is one of the leading causes of female cancer death, however the risk factors for breast cancer are not fully understood for Chinese women.[1] One of the key risk factors shown to be relevant for westernized women is mammographic density but previously used observer Breast Imaging Reporting and Data System (BI-RADS) technique to assess density is shown to have wide inter- and intraobserver variations.[2] Therefore, quantitative techniques are increasingly recommended to assess this important parameter.[3] The aim of the current study is to identify risk factors of breast cancer for Chinese women, with attention paid to mammographic density using quantitative measurements.

This study was approved by the Human Research Ethics Committee of the University of Sydney (Project number: 2014/768). Women of 84 with and 987 without breast cancer were randomly selected from Fudan University Shanghai Cancer Center (FUSCC) from March 2015 to June 2016. The women with breast cancer were diagnosed within the hospital environment at FUSCC, while the other women were recruited from the Breast Cancer Screening Trail (BCST) organized by FUSCC. Demographic, lifestyle and reproductive characteristics were obtained from the registration form and the discharge summary in the health record for each woman with breast cancer and through a BCST questionnaire for breast cancer-free women. For all of the women, mammograms were acquired for cranio-caudal projection of both breasts. Mammographic density was measured by a fully automatic algorithm AutoDensity,[4] which identifies both dense and breast areas in mammograms and then classifies mammographic density. Differences in characteristics between cancer and cancer-free women were assessed using t tests and chi-square tests. Binary logistic regression was then conducted for variables that were statistically significant from either the t test or the chi-squared test to produce odds ratios and 95% confidence intervals. Categorical variables with 0 frequency in any one of the categories were excluded from this test. The whole data set was then divided into two subsets based on menopause status, one for premenopausal and another for postmenopausal women. The statistical tests mentioned above were repeated for each subset.

Table 1 shows the baseline differences of characteristics for two groups of women, and the outputs from binary logistic regression. Overall, it appears that large breast area, increasing age, increasing BMI, later age at menarche, earlier age at first delivery, longer duration of breastfeeding, postmenopause status, greater number of children, and a breastfeeding history are important agents. The results for pre and postmenopausal women are shown in Tables S1 and S2, respectively. The rest of this commentary however will focus on the implications around our findings on mammographic density.

We failed to identify any association for mammographic density with breast cancer using percentage or dense area parameters, a finding which is consistent and inconsistent with previous work: one previous study which recruited 86 and 28 302 women with and without breast cancer, respectively, from a screening trial across 4 Chinese cities of similar size to our study also showed no association between density and cancer[5]; in contrast another large cross-sectional study, involving 2527 cancer and 3394 cancer-free women, reported that, compared to women without breast cancer, mammographic density was lower and higher for cancer women within the 40-49 and 55-71 age groups, respectively, however there was no association for women aged 50-54.[6] This difference between our work and the latter study might be explained by the fact that age-dependent variations were not assessed in our work, thereby obscuring specific observations. Instead, we focused on categorizing our women based on menopausal status. Another possible explanation is that, unlike other studies that used qualitative (ie, BI-RADS classification) assessment, we used quantitative approach to assess mammographic density, thus potentially impacting on the results, but the possibility of this impact requires further study. Nonetheless from our findings and that of previous studies, the possibility remains that the relationship between mammographic density and breast cancer for women in China may not be as strong as or at least could be different from that demonstrated in other populations, particularly those involving western women. This hypothesis will need further work to be proven or disproven.

In summary, this study demonstrated risk factors of breast cancer for Chinese women with a particular focus on quantitative methods of mammographic density. The lack of association between breast cancer and mammographic density could have significant implications for breast cancer screening strategies.

参考文献

Li T, Mello-Thoms C, Brennan PC. Descriptive epidemiology of breast cancer in China: incidence, mortality, survival and prevalence. Breast Cancer Res Treat. 2016;159:395-406.

Ciatto S, Houssami N, Apruzzese A, et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. Breast. 2005;14:269-275.

Ekpo EU, Hogg P, Highnam R, McEntee MF. Breast composition: measurement and clinical use. Radiography. 2015;21:324-333.

Nickson C, Arzhaeva Y, Aitken Z, et al. AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes. Breast Cancer Res. 2013;15:R80-R80.

Dai HJ, Yan Y, Wang PS, et al. Distribution of mammographic density and its influential factors among Chinese women. Int J Epidemiol. 2014;43:1240-1251.

Liu J, Liu PF, Li JN, et al. Analysis of mammographic breast density in a group of screening Chinese women and breast cancer patients. Asian Pac J Cancer Prev. 2014;15:6411-6414.

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