Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

Michielli, Nicola; Caputo, Alessandro; Scotto, Manuela; Mogetta, Alessandro; Pennisi, Orazio Antonino Maria; Molinari, Filippo; Balmativola, Davide; Bosco, Martino; Gambella, Alessandro; Metović, Jasna; Tota, Donatella; Carpenito, Laura; Gasparri, Paolo; Salvi, Massimo

doi:10.1016/j.jpi.2022.100145

Cited by 21 publications

(8 citation statements)

References 34 publications

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“…For digital pathology, stain-normalization is important, especially in patches [ 42 , 43 ]. The trained datasets are normalization datasets in our study.…”

Section: Discussionmentioning

confidence: 99%

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Li,

Chou,

Lin

et al. 2023

J Transl Med

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Background Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. Methods The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation. A deep convolutional survival model (DeepConvSurv) extracted histopathological information from the image patches of nine different tissue types, including tumors, lymphocytes, stroma, and mucus. The tissue map of the WSIs was segmented using image processing techniques that involved localizing and quantifying the tissue region. Six survival models with the concordance index (C-index) were used as the evaluation metrics. Results We extracted 128 histopathological features from four histological types and five tissue area features from WSIs to predict colorectal cancer survival. Our method performed better in six distinct survival models than the Whole Slide Histopathological Images Survival Analysis framework (WSISA), which adaptively sampled patches using K-means from WSIs. The best performance using histopathological features was 0.679 using LASSO-Cox. Compared to histopathological features alone, tissue area features increased the C-index by 2.5%. Based on histopathological features and tissue area features, our approach achieved performance of 0.704 with RIDGE-Cox. Conclusions A deep learning-based algorithm combining histopathological features with tissue area proved clinically relevant and effective for predicting cancer survival.

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“…For digital pathology, stain-normalization is important, especially in patches [ 42 , 43 ]. The trained datasets are normalization datasets in our study.…”

Section: Discussionmentioning

confidence: 99%

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Li,

Chou,

Lin

et al. 2023

J Transl Med

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“…There are a number of studies that show the negative impact of color variation in WSIs on the performance of AI and automated analysis, and demonstrate the need to mitigate the variation. [1][2][3][4][5][6][7][11][12][13][14] We mentioned three inconsistencies in the whole slide imaging system that induce color variation in WSIs, and in this article, we focused on addressing the color variation among different WSI scanners using a color calibration slide. To further improve the color homogeneity in WSIs for digital pathology applications, integrating stain normalization into the process is the next step to resolve most of the evident inconsistencies in the system.…”

Section: Author Contributionsmentioning

confidence: 99%

“…The inconsistent preparation process of tissue to glass slides, including but not limited to non-standardized staining protocols across laboratories, can generate different intensities and contrasts of the stain color on tissue samples. 3,7,11…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology

Chu,

Ge,

Eastham

et al. 2023

Toxicol Pathol

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Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.

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“…Whilst automated QC tools exist for digitised pathology WSI [5,8,10,17], these are variably suited to be integrated into a real-time clinical workflow. Most are specifically designed to detect or modify a single quality issue such as focus quality [18] or image sharpness [19], whilst other tools exist to aid the 'normalisation' of colour in WSI [5,20]. As such, these tools are more suited to the research setting, potentially in the development or validation of AI tools, rather than the clinical workflow where, in reality, a wide range of quality issues exist.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Browning,

Jesus,

Malacrino

et al. 2024

Diagnostics

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Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.

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Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

Cited by 21 publications

References 34 publications

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

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