Boyd Norman, Martin Lisa, Gunasekara Anoma, Melnichouk Olga, Maudsley Gord, Peressotti Chris, Yaffe Martin, Minkin Salomon
Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
Cancer Epidemiol Biomarkers Prev. 2009 Jun;18(6):1754-62. doi: 10.1158/1055-9965.EPI-09-0107.
Mammographic density has been found to be strongly associated with risk of breast cancer. We have assessed a novel method of assessing breast tissue that is fully automated, does not require an observer, and measures the volume, rather than the projected area, of the relevant tissues in digitized screen-film mammogram.
Sixteen mammography machines in seven locations in Toronto were calibrated to allow the estimation of the proportion of radiologically dense (stromal and epithelial tissue) and nondense (fatty) tissue represented in each pixel of the mammographic image. This information was combined with a measurement of breast thickness to calculate the volumes of these tissues. Women with newly diagnosed breast cancer (cases) identified on these mammography machines during the years 2000 to 2003 were compared with other women of the same age who did not have breast cancer (controls).
Three hundred sixty-four cases and 656 controls were recruited, epidemiologic data were collected, screen-film mammograms were digitized and measured using both a computer-assisted thresholding method, and the new measure of the volume of density. After adjustment for other risk factors, the odds ratio for those in the 5th quintile compared with the 1st quintile was 1.98 (95% confidence interval, 1.3-3.1) for the volume measure and 1.86 (95% CI, 1.1-3.0) for the area measurement. After inclusion of the volume and area measures in a predictive model, the volume measure lost significance, whereas the area measure remained significant.
Contrary to our expectations, measurement of the volume of breast tissue did not improve prediction of breast cancer risk.
乳腺钼靶密度已被发现与乳腺癌风险密切相关。我们评估了一种全新的乳腺组织评估方法，该方法完全自动化，无需观察者参与，且能测量数字化屏-片乳腺钼靶中相关组织的体积而非投影面积。
对多伦多七个地点的16台乳腺钼靶机进行校准，以估算乳腺钼靶图像每个像素中放射致密（基质和上皮组织）和非致密（脂肪）组织所占的比例。将此信息与乳腺厚度测量值相结合，计算这些组织的体积。将2000年至2003年期间在这些乳腺钼靶机上确诊为新发乳腺癌的女性（病例组）与同龄未患乳腺癌的其他女性（对照组）进行比较。
招募了364例病例和656例对照，收集了流行病学数据，对屏-片乳腺钼靶进行数字化处理，并使用计算机辅助阈值法和新的密度体积测量方法进行测量。在调整其他风险因素后，体积测量的第5个五分位数与第1个五分位数相比的优势比为1.98（95%置信区间，1.3 - 3.1），面积测量的优势比为1.86（95%CI，1.1 - 3.0）。在将体积和面积测量纳入预测模型后，体积测量失去显著性，而面积测量仍具有显著性。
与我们的预期相反，乳腺组织体积测量并未改善对乳腺癌风险的预测。
