Computational pathology to discriminate benign from malignant intraductal proliferations of the breast

The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95...基于组织病理切片的常规光镜检查对乳腺导管内增生性病变进行分类，在诸多情形下即便对于经验丰富的病理医师而言仍极具挑战性。开发可辅助病理医师对这类病变进行表征分析的计算工具，将具备极高的诊断与临床价值。为解决该问题，我们首先评估了计算图像分析技术准确区分导管原位癌（DCIS）与寻常型导管上皮增生（UDH），并对导管原位癌的核分级进行分层的能力。我们使用来自麻省总医院（MGH）的116份确诊为导管原位癌或寻常型导管上皮增生的乳腺活检样本，开发了一种计算方法，可提取392项特征，这些特征对应8个颜色通道下细胞核的大小、形态、强度及纹理的均值与标准差。我们采用L1正则化逻辑回归构建分类模型，以区分导管原位癌与寻常型导管上皮增生。性能最优的模型包含22个有效特征，其曲线下面积（AUC）达到0.95……