Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Overview

Journal Front Med (Lausanne)

Specialty General Medicine

Date 2020 Jan 24

PMID 31970160

Citations 14

Authors

Rossana C N Melo

Maximilian W D Raas

Cinthia Palazzi

Vitor H Neves

Kassia K Malta

Thiago P Silva

Affiliations

Soon will be listed here.

Abstract

Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

Citing Articles

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.

Ghezloo F, Chang O, Knezevich S, Shaw K, Thigpen K, Reisch L J Imaging Inform Med. 2024; 38(1):439-454.

PMID: 39122892 PMC: 11811336. DOI: 10.1007/s10278-024-01202-x.

Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis.

Socha P, Shumbayawonda E, Roy A, Langford C, Aljabar P, Wozniak M J Pathol Inform. 2024; 15:100372.

PMID: 38524918 PMC: 10959696. DOI: 10.1016/j.jpi.2024.100372.

Histology Image Viewer and Converter (HIVC): A High-Speed Freeware Software to View and Convert Whole Slide Histology Images.

Razian S, Jadidi M Comput Methods Biomech Biomed Eng Imaging Vis. 2023; 11(5):1652-1660.

PMID: 37994355 PMC: 10662701. DOI: 10.1080/21681163.2023.2174776.

Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach.

Koteles M, Vigdorovits A, Kumar D, Mihai I, Jurescu A, Gheju A Diagnostics (Basel). 2023; 13(14).

PMID: 37510069 PMC: 10377791. DOI: 10.3390/diagnostics13142326.

Application of digital pathology and machine learning in the liver, kidney and lung diseases.

Wu B, Moeckel G J Pathol Inform. 2023; 14:100184.

PMID: 36714454 PMC: 9874068. DOI: 10.1016/j.jpi.2022.100184.

References

Geramizadeh B, Malek-Hosseini S . Role of Histopathologist in Liver Transplantation. Int J Organ Transplant Med. 2017; 8(1):1-6. PMC: 5347400. View

Goodman Z . Grading and staging systems for inflammation and fibrosis in chronic liver diseases. J Hepatol. 2007; 47(4):598-607. DOI: 10.1016/j.jhep.2007.07.006. View

Howat W, Wilson B . Tissue fixation and the effect of molecular fixatives on downstream staining procedures. Methods. 2014; 70(1):12-9. PMC: 4240801. DOI: 10.1016/j.ymeth.2014.01.022. View

Aeffner F, Wilson K, Martin N, Black J, Luengo Hendriks C, Bolon B . The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth. Arch Pathol Lab Med. 2017; 141(9):1267-1275. DOI: 10.5858/arpa.2016-0386-RA. View

Bautista P, Hashimoto N, Yagi Y . Color standardization in whole slide imaging using a color calibration slide. J Pathol Inform. 2014; 5(1):4. PMC: 3952402. DOI: 10.4103/2153-3539.126153. View

Vu Q, Graham S, Kurc T, To M, Shaban M, Qaiser T . Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol. 2019; 7:53. PMC: 6454006. DOI: 10.3389/fbioe.2019.00053. View

Anderson N, Borlak J . Molecular mechanisms and therapeutic targets in steatosis and steatohepatitis. Pharmacol Rev. 2008; 60(3):311-57. DOI: 10.1124/pr.108.00001. View

Amaral K, Silva T, Malta K, Carmo L, Dias F, Almeida M . Natural Schistosoma mansoni Infection in the Wild Reservoir Nectomys squamipes Leads to Excessive Lipid Droplet Accumulation in Hepatocytes in the Absence of Liver Functional Impairment. PLoS One. 2016; 11(11):e0166979. PMC: 5120838. DOI: 10.1371/journal.pone.0166979. View

Xie C, Wei W, Schenk A, Schwen L, Zafarnia S, Schwier M . Visualization of Vascular and Parenchymal Regeneration after 70% Partial Hepatectomy in Normal Mice. J Vis Exp. 2016; (115). PMC: 5092013. DOI: 10.3791/53935. View

10.

Homeyer A, Schenk A, Arlt J, Dahmen U, Dirsch O, Hahn H . Practical quantification of necrosis in histological whole-slide images. Comput Med Imaging Graph. 2013; 37(4):313-22. DOI: 10.1016/j.compmedimag.2013.05.002. View

11.

Masugi Y, Abe T, Tsujikawa H, Effendi K, Hashiguchi A, Abe M . Quantitative assessment of liver fibrosis reveals a nonlinear association with fibrosis stage in nonalcoholic fatty liver disease. Hepatol Commun. 2018; 2(1):58-68. PMC: 5776870. DOI: 10.1002/hep4.1121. View

12.

Cabibi D, Bronte F, Porcasi R, Ingrao S, Giannone A, Maida M . Comparison of Histochemical Stainings in Evaluation of Liver Fibrosis and Correlation with Transient Elastography in Chronic Hepatitis. Anal Cell Pathol (Amst). 2015; 2015:431750. PMC: 4664783. DOI: 10.1155/2015/431750. View

13.

Andrade Z . Schistosomiasis and liver fibrosis. Parasite Immunol. 2009; 31(11):656-63. DOI: 10.1111/j.1365-3024.2009.01157.x. View

14.

Wei W, Dirsch O, Lawson McLean A, Zafarnia S, Schwier M, Dahmen U . Rodent models and imaging techniques to study liver regeneration. Eur Surg Res. 2014; 54(3-4):97-113. DOI: 10.1159/000368573. View

15.

Valls C, Iannacconne R, Alba E, Murakami T, Hori M, Passariello R . Fat in the liver: diagnosis and characterization. Eur Radiol. 2006; 16(10):2292-308. DOI: 10.1007/s00330-006-0146-0. View

16.

Anstee Q, Reeves H, Kotsiliti E, Govaere O, Heikenwalder M . From NASH to HCC: current concepts and future challenges. Nat Rev Gastroenterol Hepatol. 2019; 16(7):411-428. DOI: 10.1038/s41575-019-0145-7. View

17.

Choi E, Lamps L . Granulomas in the Liver, with a Focus on Infectious Causes. Surg Pathol Clin. 2018; 11(2):231-250. DOI: 10.1016/j.path.2018.02.008. View

18.

Yagi Y, Riedlinger G, Xu X, Nakamura A, Levy B, Iafrate A . Development of a database system and image viewer to assist in the correlation of histopathologic features and digital image analysis with clinical and molecular genetic information. Pathol Int. 2016; 66(2):63-74. DOI: 10.1111/pin.12382. View

19.

Webster J, Dunstan R . Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol. 2013; 51(1):211-23. DOI: 10.1177/0300985813503570. View

20.

Atupelage C, Nagahashi H, Yamaguchi M, Abe T, Hashiguchi A, Sakamoto M . Computational grading of hepatocellular carcinoma using multifractal feature description. Comput Med Imaging Graph. 2012; 37(1):61-71. DOI: 10.1016/j.compmedimag.2012.10.001. View