Beth Israel Deaconess Medical Center, Harvard Medical School, Department of Pathology, Boston, 02115, USA.
Kaiser-Permanente, Mid-Atlantic Group, Rockville, MD, USA.
Sci Rep. 2017 Feb 23;7:43286. doi: 10.1038/srep43286.
The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.
免疫组织化学（IHC）图像中蛋白质表达的评估为癌症的诊断和治疗提供了重要的诊断、预后和预测信息。IHC 图像的手动评分具有挑战性，因为该过程既耗费精力又耗时。自上一个十年以来，已经开发了计算方法来实现用于分析和解释 IHC 图像中蛋白质表达的定量方法的应用。这些方法尚未在大多数诊断实验室和许多大规模研究中取代 IHC 的手动评分。一种替代方法是将 IHC 图像的量化任务外包给一个未定义的人群。本研究的目的是使用两种不同的众包方法（图像标记和核标记）对 IHC 图像进行量化，并将其性能与自动化方法进行比较。与自动化方法（81%）相比，两种众包方法（图像标记和核标记）获得的评分与病理学家的解释具有更高的一致性（83%和 87%），基于 1853 名乳腺癌患者的 5338 个 TMA 图像。该分析表明，将 IHC 图像中蛋白质表达的评分外包给众包是一种用于大规模癌症分子病理学研究的有前途的新方法。
