Chen Jia-Mei, Li Yan, Xu Jun, Gong Lei, Wang Lin-Wei, Liu Wen-Lou, Liu Juan
1 Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, China.
2 Department of Peritoneal Cancer Surgery, Beijing Shijitan Hospital of Capital Medical University, Beijing, China.
Tumour Biol. 2017 Mar;39(3):1010428317694550. doi: 10.1177/1010428317694550.
With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature-based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.
随着数字病理学的发展,图像分析已开始在苏木精和伊红组织病理学图像的信息分析中展现出其优势。一般来说,通过测量苏木精和伊红图像中的组织学特征来评估乳腺癌的肿瘤分级和预后。本综述总结了近期在苏木精和伊红组织病理学图像用于乳腺癌预后的图像分析方面的工作。首先,总结了基于苏木精和伊红组织病理学图像的乳腺癌预后因素。然后,系统回顾了乳腺癌预后图像分析的常规流程,包括图像采集、图像预处理、图像检测与分割以及特征提取。最后,评估了图像特征和基于图像特征的预后模型的预后价值。此外,我们讨论了当前分析中存在的问题以及未来研究的一些方向。