Liver fibrosis analysis using digital pathology.
Artificial intelligence
Digital pathology
Liver cancer
Liver disease
Liver fibrosis
Journal
Medical molecular morphology
ISSN: 1860-1499
Titre abrégé: Med Mol Morphol
Pays: Japan
ID NLM: 101239023
Informations de publication
Date de publication:
09 Jul 2024
09 Jul 2024
Historique:
received:
24
04
2024
accepted:
01
07
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
9
7
2024
Statut:
aheadofprint
Résumé
Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3 s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™.
Identifiants
pubmed: 38980407
doi: 10.1007/s00795-024-00395-y
pii: 10.1007/s00795-024-00395-y
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to The Japanese Society for Clinical Molecular Morphology.
Références
GLOBOCAN 2020 https://gco.iarc.fr/today/ data/factsheets/cancers/11-Liver-fact- sheet.pdf
O’Brien MJ, Keating NM, Elderiny S, Cerda S, Keaveny AP, Afdhal NH, Nunes DP (2000) An assessment of digital image analysis to measure fibrosis in liver biopsy specimens of patients with chronic hepatitis C. Am J Clin Pathol 114:712–718
doi: 10.1309/D7AU-EYW7-4B6C-K08Y
pubmed: 11068544
Calvaruso V, Burroughs AK, Standish R, Manousou P, Grillo F, Leandro G, Maimone S, Pleguezuelo M, Xirouchakis I, Guerrini GP, Patch D, Yu D, O’Beirne J, Dhillon AP (2009) Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient. Hepatology 49:1236–1244
doi: 10.1002/hep.22745
pubmed: 19133646
Huang Y, de Boer WB, Adams LA, MacQuillan G, Rossi E, Rigby P, Raftopoulos SC, Bulsara M, Jeffrey GP (2013) Image analysis of liver collagen using sirius red is more accurate and correlates better with serum fibrosis markers than trichrome. Liver Int 33:1249–1256
doi: 10.1111/liv.12184
pubmed: 23617278
Mukhopadhyay S, Feldman MD, Abels E, Ashfaq R, Beltaifa S, Cacciabeve NG, Cathro HP, Cheng L, Cooper K, Dickey GE, Gill RM, Heaton RP Jr, Kerstens R, Lindberg GM, Malhotra RK, Mandell JW, Manlucu ED, Mills AM, Mills SE, Moskaluk CA, Nelis M, Patil DT, Przybycin CG, Reynolds JP, Rubin BP, Saboorian MH, Salicru M, Samols MA, Sturgis CD, Turner KO, Wick MR, Yoon JY, Zhao P, Taylor CR (2018) Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (Pivotal Study). Am J Surg Pathol 42:39–52
doi: 10.1097/PAS.0000000000000948
pubmed: 28961557
Marti-Aguado D, Rodriguez-Ortega A, Mestre-Alagarda C, Bauza M, Valero-Perez E, Alfaro-Cervello C, Benlloch S, Perez-Rojas J, Ferrandez A, Alemany-Monraval P, Escudero-Garcia D, Monton C, Aguilera V, Alberich-Bayarri A, Serra MA, Marti-Bonmati L (2021) Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease. Aliment Pharmacol Ther 53:160–171
doi: 10.1111/apt.16100
pubmed: 32981113
Kamm DR, McCommis KS (2022) Hepatic stellate cells in physiology and pathology. J Physiol 600:1825–1837
doi: 10.1113/JP281061
pubmed: 35307840
Mladenic K, Lenartic M, Marinovic S, Polic B, Wensveen FM (2024) The “Domino effect” in MASLD: the inflammatory cascade of steatohepatitis. Eur J Immunol 54:e2149641
doi: 10.1002/eji.202149641
pubmed: 38314819
Rinella ME (2015) Nonalcoholic fatty liver disease: a systematic review. JAMA 313:2263–2273
doi: 10.1001/jama.2015.5370
pubmed: 26057287
Tsochatzis EA, Bosch J, Burroughs AK (2014) Liver cirrhosis. Lancet 383:1749–1761
doi: 10.1016/S0140-6736(14)60121-5
pubmed: 24480518
Hernandez-Gea V, Friedman SL (2011) Pathogenesis of liver fibrosis. Annu Rev Pathol 6:425–456
doi: 10.1146/annurev-pathol-011110-130246
pubmed: 21073339
Nakamura Y, Miyaaki H, Miuma S, Akazawa Y, Fukusima M, Sasaki R, Haraguchi M, Soyama A, Hidaka M, Eguchi S, Nakao K (2022) Automated fibrosis phenotyping of liver tissue from non-tumor lesions of patients with and without hepatocellular carcinoma after liver transplantation for non-alcoholic fatty liver disease. Hepatol Int 16:555–561
doi: 10.1007/s12072-022-10340-9
pubmed: 35553006
Naoumov NV, Brees D, Loeffler J, Chng E, Ren Y, Lopez P, Tai D, Lamle S, Sanyal AJ (2022) Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J Hepatol 77:1399–1409
doi: 10.1016/j.jhep.2022.06.018
pubmed: 35779659
Konigshofer P, Hofer BS, Brusilovskaya K, Simbrunner B, Petrenko O, Woran K, Herac M, Stift J, Lampichler K, Timelthaler G, Bauer D, Hartl L, Robl B, Sibila M, Podesser BK, Oberhuber G, Schwabl P, Mandorfer M, Trauner M, Reiberger T (2022) Distinct structural and dynamic components of portal hypertension in different animal models and human liver disease etiologies. Hepatology 75:610–662
doi: 10.1002/hep.32220
pubmed: 34716927
Ratziu V, Francque S, Behling CA, Cejvanovic V, Cortez-Pinto H, Iyer JS, Krarup N, Le Q, Sejling AS, Tiniakos D, Harrison SA (2023) Artificial intelligence scoring of liver biopsies in a phase II trial of semaglutide in nonalcoholic steatohepatitis. Hepatology 80:173–185
doi: 10.1097/HEP.0000000000000723
pubmed: 38112484
Kanta J, Velebny V, Mergancova J, Ettlerova E, Chlumska A (1990) Elastin content in human fibrotic and cirrhotic liver. Sb Ved Pr Lek Fak Karlovy Univerzity Hradci Kralove 33:489–494
pubmed: 2132721
Bedossa P, Lemaigre G, Paraf F, Martin E (1990) Deposition and remodelling of elastic fibres in chronic hepatitis. Virchows Arch A Pathol Anat Histopathol 417:159–162
doi: 10.1007/BF02190534
pubmed: 2114695
Scheuer PJ, Maggi G (1980) Hepatic fibrosis and collapse: histological distinction by orecin staining. Histopathology 4:487–490
doi: 10.1111/j.1365-2559.1980.tb02943.x
pubmed: 6159296
Thung SN, Gerber MA (1982) The formation of elastic fibers in livers with massive hepatic necrosis. Arch Pathol Lab Med 106:468–469
pubmed: 6896811
Yasui Y, Abe T, Kurosaki M, Higuchi M, Komiyama Y, Yoshida T, Hayashi T, Kuwabara K, Takaura K, Nakakuki N, Takada H, Tamaki N, Suzuki S, Nakanishi H, Tsuchiya K, Itakura J, Takahashi Y, Hashiguchi A, Sakamoto M, Izumi N (2016) Elastin fiber accumulation in liver correlates with the development of hepatocellular carcinoma. PLoS One 11:e0154558
doi: 10.1371/journal.pone.0154558
pubmed: 27128435
pmcid: 4851385
Kendall TJ, Dolman GE, Duff CM, Paish EC, Zaitoun A, Irving W, Fallowfield JA, Guha IN (2018) Hepatic elastin content is predictive of adverse outcome in advanced fibrotic liver disease. Histopathology 73:90–100
doi: 10.1111/his.13499
pubmed: 29464815
pmcid: 6033111
Maehara J, Masugi Y, Abe T, Tsujikawa H, Kurebayashi Y, Ueno A, Ojima H, Okuda S, Jinzaki M, Shinoda M, Kitagawa Y, Oda Y, Honda H, Sakamoto M (2020) Quantification of intratumoral collagen and elastin fibers within hepatocellular carcinoma tissues finds correlations with clinico-patho-radiological features. Hepatol Res 50:607–619
doi: 10.1111/hepr.13484
pubmed: 31886596
Masugi Y, Abe T, Tsujikawa H, Effendi K, Hashiguchi A, Abe M, Imai Y, Hino K, Hige S, Kawanaka M, Yamada G, Kage M, Korenaga M, Hiasa Y, Mizokami M, Sakamoto M (2018) Quantitative assessment of liver fibrosis reveals a nonlinear association with fibrosis stage in nonalcoholic fatty liver disease. Hepatol Commun 2:58–68
doi: 10.1002/hep4.1121
pubmed: 29404513
Gailhouste L, Le Grand Y, Odin C, Guyader D, Turlin B, Ezan F, Desille Y, Guilbert T, Bessard A, Fremin C, Theret N, Baffet G (2010) Fibrillar collagen scoring by second harmonic microscopy: a new tool in the assessment of liver fibrosis. J Hepatol 52:398–406
doi: 10.1016/j.jhep.2009.12.009
pubmed: 20149472
Chang PE, Goh GBB, Leow WQ, Shen L, Lim KH, Tan CK (2018) Second harmonic generation microscopy provides accurate automated staging of liver fibrosis in patients with non-alcoholic fatty liver disease. PLoS One 13:e0199166
doi: 10.1371/journal.pone.0199166
pubmed: 29924825
pmcid: 6010245
Wang TH, Chen TC, Teng X, Liang KH, Yeh CT (2015) Automated biphasic morphological assessment of hepatitis B-related liver fibrosis using second harmonic generation microscopy. Sci Rep 5:12962
doi: 10.1038/srep12962
pubmed: 26260921
pmcid: 4531344
Gole L, Liu F, Ong KH, Li L, Han H, Young D, Marini GPL, Wee A, Zhao J, Rao H, Yu W, Wei L (2023) Quantitative image-based collagen structural features predict the reversibility of hepatitis C virus-induced liver fibrosis post antiviral therapies. Sci Rep 13:6384
doi: 10.1038/s41598-023-33567-4
pubmed: 37076590
pmcid: 10115775
Zheng Y, Jiang Z, Zhang H, Xie F, Shi J, Xue C (2019) Adaptive color deconvolution for histological WSI normalization. Comput Methods Prog Biomed 170:107–120
doi: 10.1016/j.cmpb.2019.01.008
Ratziu V, Hompesch M, Petitjean M, Serdjebi C, Iyer JS, Parwani AV, Tai D, Bugianesi E, Cusi K, Friedman SL, Lawitz E, Romero-Gomez M, Schuppan D, Loomba R, Paradis V, Behling C, Sanyal AJ (2024) Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions. J Hepatol 80:335–351
doi: 10.1016/j.jhep.2023.10.015
pubmed: 37879461
Watson A, Petitjean L, Petitjean M, Pavlides M (2024) Liver fibrosis phenotyping and severity scoring by quantitative image analysis of biopsy slides. Liver Int 44:399–410
doi: 10.1111/liv.15768
pubmed: 38010988