Che Yuxuan, Ren Fei, Zhang Xueyuan, Cui Li, Wu Huanwen, Zhao Ze
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China.
Diagnostics (Basel). 2023 Jan 10;13(2):263. doi: 10.3390/diagnostics13020263.
Breast cancer is one of the common malignant tumors in women. It seriously endangers women's life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation criteria mainly includes three indexes: staining intensity, circumferential membrane staining pattern, and proportion of positive cells. Manually scoring HER2 IHC images is an error-prone, variable, and time-consuming work. To solve these problems, this study proposes an automated predictive method for scoring whole-slide images (WSI) of HER2 slides based on a deep learning network. A total of 95 HER2 pathological slides from September 2021 to December 2021 were included. The average patch level precision and f1 score were 95.77% and 83.09%, respectively. The overall accuracy of automated scoring for slide-level classification was 97.9%. The proposed method showed excellent specificity for all IHC 0 and 3+ slides and most 1+ and 2+ slides. The evaluation effect of the integrated method is better than the effect of using the staining result only.
乳腺癌是女性常见的恶性肿瘤之一。它严重危及女性的生命和健康。人表皮生长因子受体2（HER2）蛋白负责健康乳腺细胞的分裂和生长。HER2蛋白的过表达通常通过免疫组织化学（IHC）进行评估。IHC评估标准主要包括三个指标：染色强度、细胞膜周围染色模式和阳性细胞比例。手动对HER2 IHC图像进行评分是一项容易出错、结果可变且耗时的工作。为了解决这些问题，本研究提出了一种基于深度学习网络的HER2玻片全切片图像（WSI）自动评分预测方法。共纳入了2021年9月至2021年12月的95张HER2病理玻片。平均斑块水平精度和F1分数分别为95.77%和83.09%。玻片水平分类的自动评分总体准确率为97.9%。所提出的方法对所有IHC 0和3+玻片以及大多数1+和2+玻片显示出优异的特异性。综合方法的评估效果优于仅使用染色结果的效果。
