| Title |
Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology / |
| Authors |
Elie, Nicolas ; Giffard, Florence ; Blanc-Fournier, Cécile ; Morice, Pierre-Marie ; Brachet, Pierre-Emmanuel ; Dutoit, Soizic ; Plancoulaine, Benoit ; Poulain, Laurent |
| DOI |
10.3389/fonc.2022.931035 |
| Full Text |
|
| Is Part of |
Frontiers in oncology.. Lausanne : Frontiers Media S.A.. 2022, vol. 12, art. no. 931035, p. [1-16].. ISSN 2234-943X |
| Keywords [eng] |
image processing ; immunostaining evaluation ; quality control ; stereology ; whole slide image |
| Abstract [eng] |
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 ex vivo 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. |
| Published |
Lausanne : Frontiers Media S.A |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
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