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免疫染色定量评估自动化方法的影响:迈向数字病理学

Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology.

作者信息

Elie Nicolas, Giffard Florence, Blanc-Fournier Cécile, Morice Pierre-Marie, Brachet Pierre-Emmanuel, Dutoit Soizic, Plancoulaine Benoît, Poulain Laurent

机构信息

Normandie Univ, UNICAEN, Federative Structure 4207 'Normandie Oncologie', PLATON Services Unit, Virtual'His platform, Caen, France.

Normandie Univ, UNICAEN, Federative Structure 4207 'Normandie Oncologie', PLATON Services Unit, Caen, France.

出版信息

Front Oncol. 2022 Oct 11;12:931035. doi: 10.3389/fonc.2022.931035. eCollection 2022.

Abstract

INTRODUCTION

We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making.

METHODS

We studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM).

RESULTS

Stereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account.

CONCLUSION AND DISCUSSION

This simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist's classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care.

摘要

引言

我们试图开发一种全新的方法,用于对高级别浆液性卵巢肿瘤(HGSOC)上皮成分中的蛋白质生物标志物表达进行全自动、可靠的定量分析。本研究并非为给定的生物标志物设定阈值,而是针对一小群患者开发一种适用于多种需要对免疫标志物进行定量分析的临床情况的方法。我们旨在通过将生物标志物表达与染色异质性相关联,基于经典数学方法采用非主观的评分阈值选择来对生物标志物表达进行定量分析。这可能会产生一种通用方法,用于定量分析其他免疫组化标志物,以指导病理学家进行治疗决策。

方法

我们研究了25例HGSOC病例,其中三种预测对BH3模拟分子ABT - 737反应的生物标志物先前已由病理学家验证。我们使用两位专家进行的体视学分析来校准算法,以检测免疫组化染色以及上皮/间质区室。然后对每个组织切片在六边形体视学网格内进行免疫染色定量分析。为了从染色分布直方图中定义阈值,并将每个六边形内的染色分类为低、中或高,我们使用了高斯混合模型(GMM)。

结果

在校准过程的体视学分析中,专家们在上皮检测和免疫染色检测方面具有良好的相关性。专家与图像处理之间也具有良好的相关性。图像处理清晰地显示了单个肿瘤中低、中、高区域的各自比例,并表明这种异质性参数可以纳入综合评分中,从而降低了差异水平。因此,通过考虑异质性,与病理学家的一致性得到了提高。

结论与讨论

这种使用基本工具和已知参数的简单、可靠、经过校准的方法可用于定量分析和表征不同肿瘤区室内蛋白质生物标志物的表达。它基于已知的数学阈值,并考虑了肿瘤内染色的异质性。尽管仍需减少一些差异,但与病理学家分类的相关性令人满意。该方法具有可重复性,可用于分析其他生物学和医学问题。这种评估蛋白质生物标志物表达的非主观技术采用全自动的阈值选择(GMM)和定义的综合评分,同时考虑了肿瘤内免疫染色的异质性。它有助于避免对患者的错误分类及其对治疗护理的后续负面影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/802c/9592864/209e0ed112fc/fonc-12-931035-g001.jpg

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