Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
Genetic Technologies Limited, Fitzroy, VIC, 3065, Australia.
Breast Cancer Res. 2023 Oct 25;25(1):127. doi: 10.1186/s13058-023-01733-1.
Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.
We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.
The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively).
In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.
基于不同亮度阈值定义的纹理和密度的乳腺 X 光风险评分与乳腺癌风险的相关性不同，并且可能揭示有关乳腺癌风险的不同信息。我们旨在研究这些相互关联的乳腺 X 光风险评分之间的因果关系，以确定它们与乳腺癌病因学的相关性。
我们使用澳大利亚乳腺密度双胞胎和姐妹研究中 371 对年龄在 40-70 岁之间的单卵双胞胎的数字化乳腺 X 光照片，这些双胞胎在进行乳腺 X 光检查时没有乳腺癌的先前诊断。我们使用 Cirrus 算法生成基于纹理的归一化、年龄调整和标准化风险评分，以及通过增加亮度阈值定义的三个空间独立的密集区域：浅色区域、亮区和最亮区。使用检查家族性混杂的因果推断（ICE FALCON）方法进行因果推断。
乳腺 X 光风险评分在双胞胎内和彼此之间相关（r=0.22-0.81；所有 P<0.005）。我们估计，风险评分之间的 28%-92%的关联可以归因于评分之间的因果关系，其余的归因于评分之间共享的家族性混杂因素。有一致的证据表明存在正因果效应：Cirrus、浅色区域和亮区对最亮区（分别占关联的 34%、55%和 85%）；以及浅色区域和亮区对 Cirrus（分别占 37%和 28%）。
在乳腺 X 光片中，较轻（密度较低）的区域对最亮（高密度）区域有因果影响，包括通过纹理特征的因果途径。这些因果关系帮助我们深入了解不同乳腺 X 光特征在乳腺癌中的相对病因学重要性。例如，我们的发现与最亮区域对筛查发现的乳腺癌的病因学重要性大于较轻区域一致；相反，较轻区域对间期乳腺癌的病因学重要性更大。此外，特定的纹理特征从密集区域捕获病因学独立的乳腺癌风险信息。这些发现强调了 ICE FALCON 和家族数据在将相互关联的疾病生物标志物的关联分解为不同的生物学途径中的效用。
