Rawat Rishi R, Ruderman Daniel, Macklin Paul, Rimm David L, Agus David B
1Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 2250 Alcazar Street, CSC 240, Los Angeles, CA 90089-9075 USA.
2Intelligent Systems Engineering, Indiana University, 700N. Woodlawn Ave., Bloomington, IN 47408 USA.
NPJ Breast Cancer. 2018 Sep 4;4:32. doi: 10.1038/s41523-018-0084-4. eCollection 2018.
In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status-defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining-from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55-0.89, = 56). This proof of concept shows that machine-derived descriptors of morphologic histology patterns can be correlated to signaling pathway status. Unlike other deep learning approaches to pathology, our system uses deep neural networks to learn spatial relationships between pre-defined biological features, which improves the interpretability of the system and sheds light on the features the neural network uses to predict ER status. Future studies will correlate morphometry to quantitative measures of estrogen receptor status and, ultimately response to hormonal therapy.
在这项初步研究中，我们引入了一个机器学习框架，以确定乳腺癌病理苏木精和伊红（H&E）染色样本中癌组织形态与激素受体途径激活之间的关系。作为概念验证，我们专注于通过核特征的空间排列来预测临床雌激素受体（ER）状态，即通过免疫组织化学染色定义为雌激素受体阳性细胞超过1%。我们的学习流程从H&E图像中分割细胞核，提取其位置、形状和方向描述符，然后将它们传递给深度神经网络以预测ER状态。在对57个浸润性导管癌（IDC）组织芯进行训练后，我们的流程在患者样本的独立测试集中预测了ER状态（AUC ROC = 0.72，95%CI = 0.55 - 0.89，n = 56）。这一概念验证表明，形态学组织学模式的机器衍生描述符可以与信号通路状态相关联。与其他病理学深度学习方法不同，我们的系统使用深度神经网络来学习预定义生物学特征之间的空间关系，这提高了系统的可解释性，并揭示了神经网络用于预测ER状态的特征。未来的研究将把形态测量学与雌激素受体状态的定量测量相关联，并最终与激素治疗反应相关联。
