Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK.
Cellular Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, UK.
Histopathology. 2018 Aug;73(2):327-338. doi: 10.1111/his.13516. Epub 2018 May 21.
Output from biomarker studies involving immunohistochemistry applied to tissue microarrays (TMA) is limited by the lack of an efficient and reproducible scoring methodology. In this study, we examine the functionality and reproducibility of biomarker scoring using the new, open-source, digital image analysis software, QuPath.
Three different reviewers, with varying experience of digital pathology and image analysis, applied an agreed QuPath scoring methodology to CD3 and p53 immunohistochemically stained TMAs from a colon cancer cohort (n = 661). Manual assessment was conducted by one reviewer for CD3. Survival analyses were conducted and intra- and interobserver reproducibility assessed. Median raw scores differed significantly between reviewers, but this had little impact on subsequent analyses. Lower CD3 scores were detected in cases who died from colorectal cancer compared to control cases, and this finding was significant for all three reviewers (P-value range = 0.002-0.02). Higher median p53 scores were generated among cases who died from colorectal cancer compared with controls (P-value range = 0.04-0.12). The ability to dichomotise cases into high versus low expression of CD3 and p53 showed excellent agreement between all three reviewers (kappa score range = 0.82-0.93). All three reviewers produced dichotomised expression scores that resulted in very similar hazard ratios for colorectal cancer-specific survival for each biomarker. Results from manual and QuPath methods of CD3 scoring were comparable, but QuPath scoring revealed stronger prognostic stratification.
Scoring of immunohistochemically stained tumour TMAs using QuPath is functional and reproducible, even among users of limited experience of digital pathology images, and more accurate than manual scoring.
涉及应用于组织微阵列(TMA)的免疫组织化学的生物标志物研究的结果受到缺乏有效且可重复的评分方法的限制。在这项研究中,我们使用新的开源数字图像分析软件 QuPath 来检查生物标志物评分的功能和可重复性。
三位具有不同数字病理学和图像分析经验的不同审阅者应用了一种商定的 QuPath 评分方法,对来自结肠癌队列的 CD3 和 p53 免疫组织化学染色的 TMA(n = 661)进行评分。一位审阅者对 CD3 进行了手动评估。进行了生存分析,并评估了观察者内和观察者间的可重复性。审阅者之间的中位数原始评分存在显着差异,但这对后续分析影响不大。与对照病例相比,死于结直肠癌的病例中 CD3 评分较低,所有三位审阅者的发现均具有统计学意义(P 值范围= 0.002-0.02)。与对照组相比,死于结直肠癌的病例中 p53 的中位数评分较高(P 值范围= 0.04-0.12)。将病例分为 CD3 和 p53 高表达与低表达的能力,所有三位审阅者之间的一致性都非常好(kappa 评分范围= 0.82-0.93)。所有三位审阅者生成的二分类表达评分对于每个生物标志物的结直肠癌特异性生存都产生了非常相似的危险比。CD3 评分的手动和 QuPath 方法的结果相当,但 QuPath 评分显示出更强的预后分层。
使用 QuPath 对免疫组织化学染色的肿瘤 TMA 进行评分是可行且可重复的,即使在数字病理学图像经验有限的用户中也是如此,并且比手动评分更准确。