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计算机辅助病理免疫组化评分比传统评分更有效率,但没有分析优势。

Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage.

机构信息

Department of Pathology, National University of Singapore, 5 Lower Kent Ridge Road, Singapore, Singapore.

出版信息

Histopathology. 2010 Mar;56(4):523-9. doi: 10.1111/j.1365-2559.2010.03496.x.

Abstract

AIMS

Interpretation of immunohistochemistry is primarily done through human visual scoring while computer-assisted scoring is relatively uncommon. This study aimed to examine (i) the level of agreement between human visual and computer-assisted pathological scoring of immunoreactivity expression in colorectal cancers, (ii) whether computer-assisted scoring affects the prognostic significance of biomarkers, and (iii) whether computer-assisted pathological scoring provides any time-saving or reproducibility advantages.

METHODS AND RESULTS

Tissue microarray blocks were constructed from the primary colorectal adenocarcinoma specimens of 486 patients. Scoring of the six markers [cytokeratin (CK) 7, CK20, cyclooxygenase-2, Ki67, p27 and p53] was done independently by a qualified pathologist, a trained scientist and the Ariol SL-50 (Applied Imaging). Univariate analysis showed that human visual and computer-assisted scoring were strongly correlated (all kappa values >0.8). Both human visual and computer-assisted pathological scoring identified the same set of biomarkers with significant association with survival. Computer-assisted pathological scoring was shown to be a time-effective means of scoring larger numbers of slides (for high-throughput studies).

CONCLUSIONS

Our results suggest that computer-assisted pathological scoring can be a viable alternative to pathologist scoring in a manner that is more practical and time-effective, but, interestingly, providing no analytical advantage.

摘要

目的

免疫组织化学的解读主要通过人工视觉评分来完成,而计算机辅助评分则相对较少见。本研究旨在:(i)评估在结直肠癌中,人工视觉和计算机辅助病理评分在免疫反应表达方面的一致性水平;(ii)评估计算机辅助评分是否会影响生物标志物的预后意义;(iii)评估计算机辅助病理评分是否具有节省时间或提高重现性的优势。

方法和结果

从 486 例原发性结直肠腺癌标本中构建组织微阵列块。由一名合格的病理学家、一名受过培训的科学家和 Ariol SL-50(Applied Imaging)独立对六种标志物[细胞角蛋白(CK)7、CK20、环氧化酶-2、Ki67、p27 和 p53]进行评分。单因素分析显示,人工视觉和计算机辅助评分具有很强的相关性(所有kappa 值均大于 0.8)。人工视觉和计算机辅助病理评分都确定了与生存具有显著关联的相同生物标志物集。计算机辅助病理评分被证明是一种高效的评分大量切片的方法(适用于高通量研究)。

结论

我们的研究结果表明,计算机辅助病理评分可以作为病理学家评分的一种可行替代方法,这种方法更实用、更高效,但有趣的是,在分析方面并没有优势。

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