Top

Diagnostic Pathology

Published in:

Open Access 01-12-2024 | Pathology | Research

Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system

Authors: Catriona Dunn, David Brettle, Martin Cockroft, Elizabeth Keating, Craig Revie, Darren Treanor

Published in: Diagnostic Pathology | Issue 1/2024

Abstract

Background

Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.

Methods

The stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.

Results

Stain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98–0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.

Conclusion

The proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.

Available only for authorised users

Titford M. A Short History of Histopathology Technique, Journal of Histotechnology, vol. 29, no. 2, pp. 99–110,2006/06/01 2006, https://doi.org/10.1179/his.2006.29.2.99.

Hussein I, Raad M, Safa R, Jurjus RA, Jurjus A. Once upon a microscopic slide: the story of histology. J Cytol Histol, 6, 2015.

Lyon HO et al. Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes, stains and chromogenic reagents. Histochemical J J Article 26, 7, pp. 533–44, July 01 1994, https://doi.org/10.1007/bf00158587.

Bejnordi BE, Timofeeva N, Otte-Höller I, Karssemeijer N. W. M. v. d. Laak, quantitative analysis of stain variability in histology slides and an algorithm for standardization. Med Imaging 2014: Digit Pathol. 2014;9041. https://doi.org/10.1117/12.2043683.

Gray A, Wright A, Jackson P, Hale M, Treanor D. Quantification of histochemical stains using whole slide imaging: development of a method and demonstration of its usefulness in laboratory quality control, (in eng). J Clin Pathol. Mar 2014;68(3):192–9. https://doi.org/10.1136/jclinpath-2014-202526.

Bejnordi BE et al. Stain Specific Standardization of Whole-Slide Histopathological Images, (in eng), IEEE transactions on medical imaging, vol. 35, no. 2, pp. 404 – 15, Feb 2016, https://doi.org/10.1109/tmi.2015.2476509.

Howard FM, et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun. 2021;12(1):1–13.ADSCrossRef

The Royal College of Pathologists., How to Assess the Quality of a Pathology Service, www.rcpath.org/uploads/assets/1c3aac02-3f31-4246-83b9da4aa04899ca/How-to-Assess-the-Quality-of-a-Pathology-Service-https://www.rcpath.org/uploads/assets/1c3aac02-3f31-4246-83b9da4aa04899ca/How-to-Assess-the-Quality-of-a-Pathology-Service-meeting-report.pdf 2011. [Online]. Available:

Robert Lott JT, Sheppard E, Santiago J, Hladik C, Nasim M, Zeitner K, Haas T. Shane Kohl, Saeid Movahedi-Lankaran, Practical Guide to Specimen Handling in Surgical Pathology, <https://cap.objects.frb.io/documents/practical-guide-specimen-handling.pdf> College of American Pathologists, 2020. [Online]. Available: https://cap.objects.frb.io/documents/practical-guide-specimen-handling.pdf.

10.

Williams BJ, Knowles C, Treanor D. Maintaining quality diagnosis with digital pathology: a practical guide to ISO 15189 accreditation. J Clin Pathol. 2019;72(10):663–8.CrossRefPubMed

11.

Griffin J, Treanor D. Digital pathology in clinical use: where are we now and what is holding us back? Histopathology, vol. 70, no. 1, pp. 134–145, 2017.

12.

Gupta R, Kurc T, Sharma A, Almeida JS, Saltz J. The emergence of pathomics. Curr Pathobiol Rep. 2019;7(3):73–84.CrossRef

13.

Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol, 35, 1, pp. 23–32, 2022/01/01 2022, https://doi.org/10.1038/s41379-021-00919-2.

14.

Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol, 20, 5, pp. e253-e261, 2019/05/01/ 2019.

15.

Williams BJ, Bottoms D, Treanor D. Future-proofing pathology: the case for clinical adoption of digital pathology. J Clin Pathol. 2017;70(12):1010–8.CrossRefPubMed

16.

Tellez D, et al. Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to Train Distilled Stain-Invariant Convolutional Networks. IEEE Trans Med Imaging. 2018;37(9):2126–36. https://doi.org/10.1109/TMI.2018.2820199.CrossRef

17.

Schömig-Markiefka B et al. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod Pathol, 34, 12, pp. 2098–2108, 2021/12/01 2021, https://doi.org/10.1038/s41379-021-00859-x.

18.

Wright AI, Dunn CM, Hale M, Hutchins GGA, Treanor DE. The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis, (in eng). IEEE J Biomed Health Inf. Feb 2021;25(2):307–14. https://doi.org/10.1109/JBHI.2020.3046094.

19.

Bejnordi BE et al. Stain specific standardization of whole-slide histopathological images, (in eng). IEEE Trans Med Imaging, 35, 2, pp. 404 – 15, Feb 2015.

20.

Ciompi F et al. The importance of stain normalization in colorectal tissue classification with convolutional networks, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 160–163, 18–21 April 2017 2017, https://doi.org/10.1109/ISBI.2017.7950492.

21.

Khan AM, Rajpoot N, Treanor D, Magee D. A nonlinear Mapping Approach to Stain normalization in Digital Histopathology images using image-specific Color Deconvolution. IEEE Trans Biomed Eng. 2014;61(6):1729–38. https://doi.org/10.1109/TBME.2014.2303294.CrossRefPubMed

22.

Komura D, Ishikawa S. Machine learning methods for histopathological image analysis. Comput Struct Biotechnol J. 2018;16:34–42. https://doi.org/10.1016/j.csbj.2018.01.001. (in eng).CrossRefPubMedPubMedCentral

23.

Tellez D et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal, 58, p. 101544, 2019/12/01/ 2019, doi: https://doi.org/10.1016/j.media.2019.101544.

24.

Anghel A, et al. A high-performance system for robust stain normalization of whole-slide images in histopathology. Front Med. 2019;6:193.CrossRef

25.

Swiderska-Chadaj Z, et al. Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer. Sci Rep. 2020;10(1):14398.ADSCrossRefPubMedPubMedCentral

26.

Ciompi F et al. The importance of stain normalization in colorectal tissue classification with convolutional networks, in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017: IEEE, pp. 160–163.

27.

Salvi M, Molinari F, Acharya UR, Molinaro L, Meiburger KM. Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification. Comput Methods Programs Biomed Update. 2021;1:100004.CrossRef

28.

Salvi M et al. Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study, Cancers, vol. 15, no. 5, p. 1503, 2023.

29.

Tosta TAA, de Faria PR, Neves LA, do Nascimento MZ. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artif Intell Med. 2019;95:118–32.CrossRef

30.

Beena M. A Survey on Color Normalization Approach to Histopathology images. Int J Adv Eng Res Sci. 2016;3(4):258867.

31.

Howard FM et al. The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias, bioRxiv, 2020.

32.

Cox B, Colgan E. 1 - Pathology laboratory management, in Bancroft’s Theory and Practice of Histological Techniques (Eighth Edition), S. K. Suvarna, C. Layton, and J. D. Bancroft Eds.: Elsevier, 2019, pp. 1–11.

33.

NSH. Histology Quality Improvement Program (HistoQIP). https://www.nsh.org/learn/histoqip (accessed 15/09/2020.

34.

UKNEQAS. UK National External Quality Assessment Service. https://ukneqas.org.uk (accessed.

35.

CAP. College of American Pathologists, Laboratory Accreditation Program. https://www.cap.org/laboratory-improvement/accreditation/laboratory-accreditation-program (accessed.

36.

Allison RT, Vincent JFV. Measuring the forces acting during microtomy by the use of load cells. J Microsc. 1990;159(2):203–10. https://doi.org/10.1111/j.1365-2818.1990.tb04776.x.CrossRefPubMed

37.

McCampbell AS, et al. Tissue thickness effects on immunohistochemical staining intensity of markers of Cancer. Appl Immunohistochem Mol Morphology. 2019;27(5):345–55. https://doi.org/10.1097/pai.0000000000000593.CrossRef

38.

Bass BP, Engel KB, Greytak SR, Moore HM. A review of preanalytical factors affecting molecular, protein, and morphological analysis of formalin-fixed, paraffin-embedded (FFPE) tissue: how well do you know your FFPE specimen? Arch Pathol Lab Med. 2014;138(11):1520–30.CrossRefPubMed

39.

Chlipala EA, et al. Impact of preanalytical factors during histology processing on section suitability for digital image analysis. Toxicol Pathol. 2021;49(4):755–72.CrossRefPubMed

40.

Hotzel KJ et al. Synthetic Antigen Gels as Practical Controls for Standardized and Quantitative Immunohistochemistry, (in eng), The journal of histochemistry and cytochemistry: official journal of the Histochemistry Society, vol. 67, no. 5, pp. 309–334, May 2019, https://doi.org/10.1369/0022155419832002.

41.

Sompuram SR, Vani K, Tracey B, Kamstock DA, Bogen SA. Standardizing Immunohistochemistry: A New Reference Control for Detecting Staining Problems, (in eng), The journal of histochemistry and cytochemistry: official journal of the Histochemistry Society, vol. 63, no. 9, pp. 681 – 90, Sep 2015, https://doi.org/10.1369/0022155415588109.

42.

Bogen SA et al. Experimental validation of peptide immunohistochemistry controls, (in eng), Applied immunohistochemistry & molecular morphology: AIMM / official publication of the Society for Applied Immunohistochemistry, vol. 17, no. 3, pp. 239 – 46, May 2009, https://doi.org/10.1097/PAI.0b013e3181904379.

43.

Torlakovic EE, et al. Development and validation of measurement traceability for in situ immunoassays. Clin Chem. 2021;67(5):763–71.CrossRefPubMedPubMedCentral

44.

Bogen SA et al. A Consortium for Analytic standardization in immunohistochemistry. Arch Pathol Lab Med, 2022.

45.

Chlipala E, et al. Optical density-based image analysis method for the evaluation of hematoxylin and eosin staining precision. J Histotechnology. 2020;43(1):29–37.CrossRef

46.

Allen DW. Holmium Oxide Glass Wavelength standards, (in eng). J Res Natl Inst Stand Technol. 2007;112(6):303–6. https://doi.org/10.6028/jres.112.024.ADSCrossRefPubMedPubMedCentral

47.

Eckerle KL, Weidner VR, Hsia JJ, Kafadar K. Measurement Assurance Program Transmittance standards for Spectrophotometric Linearity Testing:* Preparation and Calibration. J Res Natl Bureau Stand, 88, 1, 1983.

48.

National Physical Laboratory, A National Measurement Good Practise Guide. https://www.npl.co.uk/special-pages/guides/mgpg97 (accessed 09/10/19, 2019).

49.

Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001;23(4):291–9.PubMed

Title: Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system
Authors: Catriona Dunn
David Brettle
Martin Cockroft
Elizabeth Keating
Craig Revie
Darren Treanor
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Pathology
Artificial Intelligence
Published in: Diagnostic Pathology / Issue 1/2024
Electronic ISSN: 1746-1596
DOI: https://doi.org/10.1186/s13000-024-01461-w

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2024

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity

Proposed diagnostic and prognostic markers of primary malignant hepatic vascular neoplasms

Erythroid variant evolving from chronic myeloid leukemia resistant to multiple tyrosine kinase inhibitors: a rare case report

Histopathology and molecular pathology confirmed a diagnosis of atypical Caroli’s syndrome: a case report

Evaluating the effects of curcumin nano-chitosan on miR-221 and miR-222 expression and Wnt/β-catenin pathways in MCF-7, MDA-MB-231 and SKBR3 cell lines

Cervical lymphoepithelioma-like carcinoma with deficient mismatch repair and loss of SMARCA4/BRG1: a case report and five related cases