Mammographic features and subsequent risk of breast cancer:: A comparison of qualitative and quantitative evaluations in the Guernsey prospective studies
Mammographic features and subsequent risk of breast cancer:: A comparison of qualitative and quantitative evaluations in the Guernsey prospective studies
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DOI：
10.1158/1055-9965.epi-04-0717
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发表时间：
2005-05-01
影响因子：
3.8
通讯作者：
Silva, ID
中科院分区：
文献类型：
--
作者：
Torres-Mejía, G;De Stavola, B;Silva, ID
已知乳腺X线摄影特征与乳腺癌相关，但其影响程度在不同研究中有显著差异。评估乳腺摄影特征的方法从主观定性分类到计算机自动定量测量。我们使用来自英国根西岛前瞻性研究的数据来检验这些方法在预测乳腺癌风险方面的相对价值。总共有3 211名年龄≥ 35岁的妇女在1986年至1989年接受了乳房X光检查，随访至2003年10月底，其中111人在此期间患上了乳腺癌。使用主观定性Wolfe分类和使用基于计算机的技术测量的几个定量乳腺X线摄影特征对乳腺X线摄影进行分类。乳腺癌风险与高级Wolfe分类、乳腺密度百分比和致密组织面积呈正相关，与透明组织面积、分形维数和空隙度呈负相关。在同一模型中纳入定量测量，确定致密组织面积和空隙是乳腺癌的最佳预测因子，致密组织总面积每增加一个SD，风险增加59% [95%置信区间（95%CI），29-94%]，但下降39%（95% CI，53-22%），在对彼此和其他混杂因素进行调整后。比较模型，包括定性沃尔夫分类和这两个定量措施的模型，包括定性或两个定量变量表明，他们都作出了重大贡献，预测乳腺癌的风险。这些研究结果表明，乳腺癌的风险不仅受到乳腺摄影密度的影响，而且还受到实质模式异质性程度的影响，并且可能受到Wolfe分类所捕获的其他特征的影响。
Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages >= 35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.