基于乳腺癌全组织切片的 Ki-67 染色的自动定量分析和 HE 图像识别及配准。

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.

机构信息

Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.

Department of Pathology, West China Second University Hospital, Sichuan University & key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610041, China.

出版信息

Diagn Pathol. 2020 May 29;15(1):65. doi: 10.1186/s13000-020-00957-5.

DOI:10.1186/s13000-020-00957-5

PMID:32471471

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7257511/

Abstract

BACKGROUND

The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter-/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning.

METHODS

We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining.

RESULTS

The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

CONCLUSIONS

Our study demonstrates the enormous potential of automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on WTS, and the automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy. We will provide those labelled images as an open-free platform for researchers to assess the performance of computer algorithms for automated Ki-67 scoring on IHC stained slides.

摘要

背景

Ki-67 的评分对乳腺浸润性导管癌 (IDC) 的诊断、分类、预后和治疗具有重要意义。传统的 Ki-67 染色评分方法是手动计数，既费时又费力，且存在观察者间和观察者内的变异性，这可能限制了其临床价值。尽管已经开发出越来越多的算法和单个平台来评估 Ki-67 染色图像，以提高其准确性水平，但大多数算法缺乏对免疫组织化学 (IHC) 图像及其匹配的苏木精-伊红 (HE) 图像的准确配准，或者没有基于全组织切片 (WTS) 准确标记每个 Ki-67 染色的阳性和阴性细胞。鉴于此，我们引入了一种基于深度学习的准确图像配准方法和一种基于 WTS 的 Ki-67 自动识别和计数软件。

方法

我们标记了 1017 张乳腺 IDC 全 slides 成像 (WSI)，建立了一个基于以下步骤的研究工作流程：(i) 识别 IDC 区域；(ii) 对来自同一解剖区域的 HE 和 IHC 幻灯片进行配准；(iii) 计数阳性 Ki-67 染色。

结果

识别乳腺 IDC 区域的准确性、敏感性和特异性水平分别为 89.44%、85.05%和 95.23%，HE 和 Ki-67 染色连续切片完美配准。我们对 10 张 Ki-67 幻灯片的每张细胞进行计数和标记，作为在 WTS 上进行测试的标准，自动计算获得的 IDC 中 Ki-67 阳性率的准确率为 90.2%。在 Ki-67 评分的人机竞赛中，使用该软件在 1 个 GPU 上，平均每张幻灯片的时间为 2.3 分钟，准确率为 99.4%，超过了参赛医生提供结果的 90%。