首页> 外文期刊>The Journal of Pathology: Clinical Research >Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium
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Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

机译:乳腺癌协会联合会在乳腺癌组织微阵列中对ER,PR,HER2,CK5 / 6和EGFR进行自动评分的表现

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AbstractBreast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.
机译:摘要乳腺癌的危险因素和临床结果因肿瘤标志物的表达而异。但是,个别研究通常缺乏评估这些关系所需的能力,并且大规模分析受限于对高通量,标准化评分方法的需求。为了解决这些局限性,我们评估了免疫组织化学染色的组织微阵列的自动图像分析是否可以通过多项研究对肿瘤标志物进行快速,标准化的评分。在9项研究中制备的组织微阵列切片包含来自8267个乳腺癌的20263个核心,分别染色为两个核(雌激素受体,孕激素受体),两个膜(人表皮生长因子受体2和表皮生长因子受体)和一个胞浆(细胞角蛋白5/6) )标记被扫描为数字图像。使用Ariol系统,使用自动化算法对肿瘤细胞中的标记物进行评分。我们比较了视觉评分和自动评分与乳腺癌生存率的关系。约65-70%的组织微阵列核心得分令人满意。在令人满意的核心中,雌激素受体的二分自动评分与视觉评分之间的一致性最高(Kappa = 0.76),其次是人表皮生长因子受体2(Kappa = 0.69)和孕激素受体(Kappa = 0.67)。这些标记物的自动化定量评分与乳腺癌死亡率的危险比呈剂量反应关系。以表皮生长因子受体或细胞角蛋白5/6的视觉评分为参考,自动评分获得了极好的阴性预测值(96-98%),但产生了许多假阳性(阳性预测值= 30-32%)。对于所有标记,我们在组织微阵列的自动评分性能中观察到很大的异质性。自动化分析是对财团中可用的免疫组织化学染色组织微阵列进行大规模,定量评分的潜在有用工具。但是,需要持续的优化,严格的标记物特异性质量控制措施以及组织微阵列设计,染色和评分方案的标准化,以提高结果。

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