Kontos Despina, Bakic Predrag R, Carton Ann-Katherine, Troxel Andrea B, Conant Emily F, Maidment Andrew D A
Hospital of the University of Pennsylvania, Department of Radiology, Physics Section, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA 19104-4206, USA.
Acad Radiol. 2009 Mar;16(3):283-98. doi: 10.1016/j.acra.2008.08.014.
Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation.
DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk.
No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates.
Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.
研究表明乳腺钼靶实质纹理与乳腺癌风险之间存在关联。尽管前景广阔，但钼靶图像中的纹理分析受组织叠加的限制。数字乳腺断层合成（DBT）是一种新型的断层X线乳腺成像模式，可减轻组织叠加的影响，与乳腺钼靶相比，能提供更优质的实质纹理可视化效果。本研究的目的是探讨DBT实质纹理分析在乳腺癌风险评估中的潜在优势。
分析了39名女性的DBT和数字乳腺钼靶（DM）图像。从乳晕后乳腺区域计算出先前研究中显示与癌症风险相关的纹理特征。比较了DBT和DM纹理特征在与两种乳腺癌风险指标相关性方面的相对表现：（1）盖尔和克劳斯风险估计值；（2）乳腺钼靶密度。进行线性回归以建立纹理特征与风险水平增加之间的关联模型。
未检测到实质纹理与盖尔和克劳斯风险估计值之间存在显著相关性。观察到纹理特征与乳腺密度之间存在显著相关性。总体而言，DBT纹理特征与乳腺密度百分比的相关性比DM特征更强（P≤0.05）。将研究人群按乳腺密度百分比增加分组时，DBT纹理特征似乎更具区分性，其回归线的总体P值更低、斜率更陡且R²估计值更高。
尽管本研究结果是初步的，但表明DBT实质纹理分析可以更准确地表征乳腺密度模式，最终可能改善乳腺癌风险评估。
