Nguyen L, Tosun A B, Fine J L, Taylor D L, Chennubhotla S C
Department of Computational and Systems Biology, University of Pittsburgh.
Drug Discovery Institute, University of Pittsburgh.
Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:152-155. doi: 10.1109/ISBI.2017.7950490. Epub 2017 Jun 19.
The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic boundaries on features that otherwise exist on a continuum going from benign to atypia to malignant is a challenge. Current computational pathology methods have focused primarily on nuclear atypia in drawing these boundaries. In this paper, we improve on these approaches by encoding for both cellular morphology and spatial architectural patterns. Using a publicly available breast lesion database consisting of UDH and three different grades of DCIS, we improve the classification accuracy by 10% over the state-of-the-art method for discriminating UDH and DCIS. For the four way classification of UDH and the three grades of DCIS, our method improves the results by 6% in accuracy, 8% in micro-AUC, and 19% in macro-AUC.
增殖性乳腺病变的鉴别诊断颇具挑战性，其中良性的普通导管增生（UDH）与恶性的导管原位癌（DCIS）的鉴别尤为困难。这需要病理学家使用光学显微镜检查活检组织的病理切片，评估组织结构的架构或大小，并评估单个细胞核的形态。要在从良性到非典型增生再到恶性的连续特征上划定诊断界限是一项挑战。当前的计算病理学方法主要集中在通过核异型性来划定这些界限。在本文中，我们通过对细胞形态和空间结构模式进行编码来改进这些方法。使用一个公开可用的乳腺病变数据库，该数据库包含UDH和三种不同级别的DCIS，我们在区分UDH和DCIS方面比现有最先进的方法将分类准确率提高了10%。对于UDH和三种DCIS级别的四分类，我们的方法在准确率上提高了6%，在微AUC上提高了8%，在宏AUC上提高了19%。
