Saikiran Pendem, Ramzan Ruqiya, S Nandish, Kamineni Phani Deepika, John Arathy Mary
Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal, Karnataka, India.
School of Information Sciences, Manipal Institute of Technology, Manipal, Karnataka, India.
J Clin Imaging Sci. 2019 Oct 11;9:43. doi: 10.25259/JCIS_70_2019. eCollection 2019.
We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.
This is a retrospective case-control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.
The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102-2.416), 2.756 (95% CI: 1.704-4.458), and 3.163 (95% CI: 1.356-5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.
Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.
我们评估了使用全自动软件测量的乳腺癌与乳腺密度（BD）之间的关联。我们还评估了癌症风险模型的性能，如仅使用临床风险因素、与密度相关的指标，以及同时使用临床风险因素和与密度相关的指标来确定癌症风险。
这是一项回顾性病例对照研究。数据收集于2015年8月至2018年12月。本研究纳入了250例乳腺癌女性患者和400例对照对象。我们使用乳腺影像报告和数据系统密度对BD进行定性评估，并使用3D Slicer进行定量评估。我们还收集了年龄、乳腺癌家族史、绝经状态、生育次数、体重指数和激素替代疗法使用情况等临床因素。我们计算了BD的比值比（OR）以确定患乳腺癌的风险。我们进行了受试者操作特征（ROC）曲线分析以评估癌症风险模型的性能。
第二、第三和第四四分位数的BD百分比的OR分别为1.632（95%置信区间[CI]：1.102 - 2.416）、2.756（95%CI：1.704 - 4.458）和3.163（95%CI：1.356 - 5.61）。仅临床风险因素、乳腺X线密度测量、乳腺X线与临床风险因素联合的ROC曲线下面积分别为0.578（95%CI：0.45，0.64）、0.684（95%CI：0.58，0.75）和0.724（95%CI：0.64，0.80）。
发现乳腺X线BD与乳腺癌呈正相关。与密度相关的指标联合临床风险因素，且密度模型在识别癌症风险方面具有良好的鉴别能力。
