Fisher Natalie C, Loughrey Maurice B, Coleman Helen G, Gelbard Melvin D, Bankhead Peter, Dunne Philip D
The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK.
Histopathology. 2022 Feb;80(3):485-500. doi: 10.1111/his.14574. Epub 2021 Nov 10.
Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software.
TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores.
We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.
肿瘤芽生（TB）是多种癌症中已确定的预后特征，但在病理实践中并非常规评估项目。将评估标准化和自动化的努力已从苏木精和伊红（H&E）染色图像转向细胞角蛋白免疫组织化学。本研究的目的是将手动H&E和细胞角蛋白评估方法与在QuPath开源软件中构建的半自动方法进行比较。
在一组II/III期结肠癌（n = 186）的肿瘤进展边缘的组织芯中评估TB。每种方法检测到的芽总数如下：手动H&E，n = 503；手动细胞角蛋白，n = 2290；半自动，n = 5138。细胞角蛋白评估手动识别的芽数量是H&E评估的四倍多。在应用于细胞角蛋白图像的手动和半自动评估中，共识别出1734个单个芽，占手动识别芽数（n = 2290）的75.7%，以及半自动方法检测到的芽数（n = 5138）的33.7%。较高的半自动TB分数是由于在可接受的面积范围内任何离散的细胞角蛋白免疫阳性区域被识别为一个芽，而不考虑形状或定义的清晰度，以及腺腔内包含肿瘤细胞簇（“腔内假芽”）。尽管绝对数量不同，但跨组织芯的半自动和手动芽计数高度相关（ρ = 0.81，P < 0.0001）。所有TB评估方法均显示，TB分数越高，生存率越低。
我们提出了一种基于QuPath的新的TB评估方法，该方法与既定方法相比具有优势，并提供了一种免费、快速且透明的工具，该工具也适用于全切片图像。
