Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, USA.
JNCI Cancer Spectr. 2021 Jan 11;5(1). doi: 10.1093/jncics/pkaa119. eCollection 2021 Feb.
New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC.
Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided.
Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all <.05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, =.047). No morphometric signature was associated with BC.
The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.
新的风险生物标志物可能会提高乳腺癌 (BC) 的风险预测。我们开发了一种计算病理学方法，将良性乳腺疾病 (BBD) 的全切片图像分割为上皮、纤维基质和脂肪。我们将我们的方法应用于护士健康研究中的 BBD BC 巢式病例对照研究中，以评估计算机衍生的组织成分或形态特征是否与随后的 BC 风险相关。
建立了组织分割和核检测深度学习网络，并应用于 293 例发生 BC 的病例和 1132 例未发生 BC 的对照的 3795 张全切片图像。计算了每个组织区域的百分比，并提取了 615 个形态特征。使用弹性网回归创建 BC 形态特征。使用协方差分析评估 BC 风险因素与对照组中年龄调整后的组织成分之间的相关性。通过调整匹配因素、BBD 组织学亚型、产次、绝经状态和体重指数的条件逻辑回归，评估组织成分与 BC 风险之间的关系。所有统计检验均为双侧。
在对照组中，BBD 亚型、产次和出生次数与乳腺成分之间的关联方向因组织区域而异；某些区域与儿童期体型、体重指数、初潮年龄和绝经状态相关（均<.05）。上皮组织比例较高与 BC 风险增加相关（比值比=1.39，95%置信区间=0.91 至 2.14，最高与最低四分位数相比， =.047）。没有形态特征与 BC 相关。
上皮组织的数量可能被纳入风险评估模型，以提高 BC 的风险预测。
