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首页> 外文期刊>Modern Pathology >Digital image analysis outperforms manual biomarker assessment in breast cancer
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Digital image analysis outperforms manual biomarker assessment in breast cancer

机译：数字图像分析优于人工生物标志物评估的乳腺癌

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In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 鈥楲uminal A,鈥?鈥楲uminal B,鈥?鈥楬ER2-enriched,鈥?and 鈥楤asal-like鈥?is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n=436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen鈥檚 魏 agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston鈥揈llis) was used as a prognostic surrogate, stronger Spearman鈥檚 rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio 蠂2 (LR 蠂2) was higher for DIA that also added significantly more prognostic information to the manual scores (LR鈭捨?i>蠂2). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.
机译：在乳腺癌中，根据四种基于基因表达的亚型，即“ A型”，“ B型”，“ ER2富集”和“ asal样”，进行分类。预后和预测价值的选择。由于基因表达测定尚未普遍可用，常规免疫组化染色剂可作为这些亚型的替代标记。因此，替代标记和基因表达测试的一致性至关重要。在这项研究中，通过人工和数字图像分析（DIA）对3组具有28年生存数据的原发性乳腺癌样本（总计n = 436）进行了Ki67，ER，PR和HER2状态的评分。然后比较结果对Luminal B亚型的敏感性和特异性，在亚型分类和预后能力上与PAM50测定的一致性。所使用的DIA系统是Visiopharm积分器系统。在对Luminal B亚型的敏感性和特异性方面，DIA优于手动评分，被广泛认为是替代亚类中最具挑战性的区别，并且与PAM50基因表达分析产生了更好的一致性和Cohen'魏一致性。人工生物标志物评分和DIA在全因死亡率方面的Cox回归风险比基本上相互匹配。当将诺丁汉组织学分级（Elston'llis）组合作为预后指标时，DIA产生了更强的Spearman'等级相关性。对于DIA，Ki67评分的似然比蠂2（LR蠂2）的预后价值更高，这也为人工评分增加了更多的预后信息（LR鈭舍> i>蠂2）。总而言之，此处评估的DIA系统在大多数方面是手动生物标记评分的替代方案。它也有可能减少病理学家的时间消耗，因为工作流中的许多步骤都是自动的，也可以在没有病理专业知识的情况下进行管理。

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