Quantification of myocardial fibrosis by digital image analysis and interactive stereology

Diagnostic Pathology

Volumn 9, Issue 1, 2014, Pages

  Daunoravicius, Dainius ^a   Besusparis, Justinas ^a   Zurauskas, Edvardas ^a   Laurinaviciene, Aida ^a   Bironaite, Daiva ^b   Pankuweit, Sabine ^c   Plancoulaine, Benoit ^d   Herlin, Paulette ^a   Bogomolovas, Julius ^e   Grabauskiene, Virginija ^a   Laurinavicius, Arvydas ^a  

^a VILNIUS UNIVERSITY   (Lithuania)

^b Center for Innovative Medicine   (Lithuania)

^c UNIVERSITY HOSPITAL MARBURG   (Germany)

^d UNIVERSITÉ DE CAEN   (France)

^e UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

Cardiac; Digital; Fibrosis; Quantification; Stereology

Indexed keywords

ADULT; ALGORITHM; BIOPSY; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; CONGESTIVE CARDIOMYOPATHY; EVALUATION STUDY; FEMALE; FIBROSIS; HEART MUSCLE; HUMAN; MALE; MICROSCOPY; MIDDLE AGED; PATHOLOGY; PREDICTIVE VALUE; SEVERITY OF ILLNESS INDEX;

ADULT; ALGORITHMS; BIOPSY; CARDIOMYOPATHY, DILATED; FEMALE; FIBROSIS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MICROSCOPY; MIDDLE AGED; MYOCARDIUM; PREDICTIVE VALUE OF TESTS; SEVERITY OF ILLNESS INDEX; SOFTWARE;

EID: 84903180689     PISSN: None     EISSN: 17461596     Source Type: Journal    
DOI: 10.1186/1746-1596-9-114     Document Type: Article

References (46)

1. Heymans S, Pauschinger M, De Palma A, Kallwellis-Opara A, Rutschow S, Swinnen M, Vanhoutte D, Gao F, Torpai R, Baker AH, Padalko E, Neyts J, Schultheiss HP, Van de Werf F, Carmeliet P, Pinto YM. Inhibition of urokinase-type plasminogen activator or matrix metalloproteinases prevents cardiac injury and dysfunction during viral myocarditis. Circulation 2006, 114:565-573.
2. Kapur NK. Transforming growth factor-beta: governing the transition from inflammation to fibrosis in heart failure with preserved left ventricular function. Circ Heart Fail 2011, 4:5-7.
3. Van Heerebeek L, Hamdani N, Handoko ML, Falcao-Pires I, Musters RJ, Kupreishvili K, Ijsselmuiden AJ, Schalkwijk CG, Bronzwaer JG, Diamant M, Borbely A, van der Velden J, Stienen GJ, Laarman GJ, Niessen HW, Paulus WJ. Diastolic stiffness of the failing diabetic heart: importance of fibrosis, advanced glycation end products, and myocyte resting tension. Circulation 2008, 117:43-51.
4. Pelliccia F, d'Amati G, Cianfrocca C, Bernucci P, Nigri A, Marino B, Gallo P. Histomorphometric features predict 1-year outcome of patients with idiopathic dilated cardiomyopathy considered to be at low priority for cardiac transplantation. Am Heart J 1994, 128:316-325.
5. Hadi AM, Mouchaers KT, Schalij I, Grunberg K, Meijer GA, Vonk-Noordegraaf A, van der Laarse WJ, Belien JA. Rapid quantification of myocardial fibrosis: a new macro-based automated analysis. Anal Cell Pathol (Amst) 2010, 33:257-269.
6. Caballero T, Perez-Milena A, Masseroli M, O'Valle F, Salmeron FJ, Del Moral RM, Sanchez-Salgado G. Liver fibrosis assessment with semiquantitative indexes and image analysis quantification in sustained-responder and non-responder interferon-treated patients with chronic hepatitis C. J Hepatol 2001, 34:740-747.
7. Dahab GM, Kheriza MM, El-Beltagi HM, Fouda AM, El-Din OA. Digital quantification of fibrosis in liver biopsy sections: description of a new method by Photoshop software. J Gastroenterol Hepatol 2004, 19:78-85.
8. Farris AB, Adams CD, Brousaides N, Della Pelle PA, Collins AB, Moradi E, Smith RN, Grimm PC, Colvin RB. Morphometric and visual evaluation of fibrosis in renal biopsies. J Am Soc Nephrol 2011, 22:176-186.
9. Manousou P, Burroughs AK, Tsochatzis E, Isgro G, Hall A, Green A, Calvaruso V, Ma GL, Gale J, Burgess G, O'Beirne J, Patch D, Thorburn D, Leandro G, Dhillon AP. Digital image analysis of collagen assessment of progression of fibrosis in recurrent HCV after liver transplantation. J Hepatol 2013, 58:962-968.
10. Lazzarini AL, Levine RA, Ploutz-Snyder RJ, Sanderson SO. Advances in digital quantification technique enhance discrimination between mild and advanced liver fibrosis in chronic hepatitis C. Liver Int 2005, 25:1142-1149.
11. Whittaker P, Kloner RA, Boughner DR, Pickering JG. Quantitative assessment of myocardial collagen with picrosirius red staining and circularly polarized light. Basic Res Cardiol 1994, 89:397-410.
12. Nicoletti A, Heudes D, Mandet C, Hinglais N, Bariety J, Michel JB. Inflammatory cells and myocardial fibrosis: spatial and temporal distribution in renovascular hypertensive rats. Cardiovasc Res 1996, 32:1096-1107.
13. Vasiljevic JD, Popovic ZB, Otasevic P, Popovic ZV, Vidakovic R, Miric M, Neskovic AN. Myocardial fibrosis assessment by semiquantitative, point-counting and computer-based methods in patients with heart muscle disease: a comparative study. Histopathology 2001, 38:338-343.
14. Borbely A, van der Velden J, Papp Z, Bronzwaer JG, Edes I, Stienen GJ, Paulus WJ. Cardiomyocyte stiffness in diastolic heart failure. Circulation 2005, 111:774-781.
15. Gaspard GJ, Pasumarthi KB. Quantification of cardiac fibrosis by colour-subtractive computer-assisted image analysis. Clin Exp Pharmacol Physiol 2008, 35:679-686.
16. Hadi AM, Mouchaers KT, Schalij I, Grunberg K, Meijer GA, Vonk-Noordegraaf A, van der Laarse WJ, Belien JA. Rapid quantification of myocardial fibrosis: a new macro-based automated analysis. Cell Oncol (Dordr) 2011, 34:343-354.
17. Sibley CT, Noureldin RA, Gai N, Nacif MS, Liu S, Turkbey EB, Mudd JO, van der Geest RJ, Lima JA, Halushka MK, Bluemke DA. T1 Mapping in cardiomyopathy at cardiac MR: comparison with endomyocardial biopsy. Radiology 2012, 265:724-732.
18. Tang Y, Nyengaard JR, Andersen JB, Baandrup U, Gundersen HJ. The application of stereological methods for estimating structural parameters in the human heart. Anat Rec (Hoboken) 2009, 292:1630-1647.
19. Gho JM, Van Es R, Stathonikos N, Harakalova M, Te Rijdt WP, Suurmeijer AJ, van der Heijden JF, De Jonge N, Chamuleau SA, De Weger RA, Asselbergs FW, Vink A. High resolution systematic digital histological quantification of cardiac fibrosis and adipose tissue in phospholamban p.Arg14del mutation associated cardiomyopathy. PLoS One 2014, 9:e94820.
20. Nassar A, Cohen C, Agersborg SS, Zhou W, Lynch KA, Albitar M, Barker EA, Vanderbilt BL, Thompson J, Heyman ER, Lange H, Olson A, Siddiqui MT. Trainable immunohistochemical HER2/neu image analysis: a multisite performance study using 260 breast tissue specimens. Arch Pathol Lab Med 2011, 135:896-902.
21. Klapczynski M, Gagne GD, Morgan SJ, Larson KJ, Leroy BE, Blomme EA, Cox BF, Shek EW. Computer-assisted imaging algorithms facilitate histomorphometric quantification of kidney damage in rodent renal failure models. J Pathol Inform 2012, 3:20.
22. Kayser K, Gortler J, Borkenfeld S, Kayser G. Interactive and automated application of virtual microscopy. Diagn Pathol 2011, 6(Suppl 1):S10.
23. Potts SJ, Young GD, Voelker FA. The role and impact of quantitative discovery pathology. Drug Discov Today 2010, 15:943-950.
24. Rizzardi AE, Johnson AT, Vogel RI, Pambuccian SE, Henriksen J, Skubitz AP, Metzger GJ, Schmechel SC. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol 2012, 7:42.
25. Ong CW, Kim LG, Kong HH, Low LY, Wang TT, Supriya S, Kathiresan M, Soong R, Salto-Tellez M. Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage. Histopathology 2010, 56:523-529.
26. Conway C, Dobson L, O'Grady A, Kay E, Costello S, O'Shea D. Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAs. Histochem Cell Biol 2008, 130:447-463.
27. Rojo MG, Bueno G, Slodkowska J. Review of imaging solutions for integrated quantitative immunohistochemistry in the Pathology daily practice. Folia Histochem Cytobiol 2009, 47:349-354.
28. Webster JD, Dunstan RW. Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol 2014, 51:211-223.
29. Ohlschlegel C, Kradolfer D, Hell M, Jochum W. Comparison of automated and manual FISH for evaluation of HER2 gene status on breast carcinoma core biopsies. BMC Clin Pathol 2013, 13:13.
30. Kayser G, Kayser K. Quantitative pathology in virtual microscopy: history, applications, perspectives. Acta Histochem 2013, 115:527-532.
31. Riber-Hansen R, Vainer BEN, Steiniche T. Digital image analysis: a review of reproducibility, stability and basic requirements for optimal results. APMIS 2012, 120:276-289.
32. Korzynska A, Roszkowiak L, Lopez C, Bosch R, Witkowski L, Lejeune M. Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3'-Diaminobenzidine&Haematoxylin. Diagn Pathol 2013, 8:48.
33. Gutiérrez R, Romero E. A visual model approach to extract regions of interest in microscopical images of basal cell carcinoma. Diagn Pathol 2013, 8:1-4.
34. Kayser K, Gortler J, Borkenfeld S, Kayser G. How to measure diagnosis-associated information in virtual slides. Diagn Pathol 2011, 6(Suppl 1):S9.
35. Nakane K, Tsuchihashi Y, Matsuura N. A simple mathematical model utilizing topological invariants for automatic detection of tumor areas in digital tissue images. Diagn Pathol 2013, 8:1-4.
36. Laurinavicius A, Laurinaviciene A, Dasevicius D, Elie N, Plancoulaine B, Bor C, Herlin P. Digital image analysis in pathology: benefits and obligation. Anal Cell Pathol (Amst) 2012, 35:75-78.
37. Elie N, Labiche A, Michels J-J, Herlin P. Control of low-resolution scanning of ovarian tumor stromal compartment. Image Anal Stereology 2005, 24:85-93.
38. Signolle N, Plancoulaine B, Herlin P, Revenu M. Texture-based multiscale segmentation: application to stromal compartment characterization on ovarian carcinoma virtual slides. Image and Signal Processing. Volume 5099 2008, 173-182. Berlin Heidelberg: Springer, Elmoataz A, Lezoray O, Nouboud F, Mammass D, Lecture Notes in Computer Science].
39. Cooper LT, Baughman KL, Feldman AM, Frustaci A, Jessup M, Kuhl U, Levine GN, Narula J, Starling RC, Towbin J, Virmani R. The role of endomyocardial biopsy in the management of cardiovascular disease: a scientific statement from the American Heart Association, the American College of Cardiology, and the European Society of Cardiology. Circulation 2007, 116:2216-2233.
40. Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 2001, 23:291-299.
41. Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology 1992, Control and Artificial Intelligence: MIT Press.
42. Baddeley A. Stereology for Statisticians/Adrian J. Baddeley and Eva B 2005, Vedel Jensen. Boca Raton, Fla: London: Chapman & Hall/CRC.
43. Weibel ER. Stereological Methods, Volume 1, Practical Methods for Biological Morphometry 1979, London: Academic.
44. Gundersen HJ, Jensen EB. The efficiency of systematic sampling in stereology and its prediction. J Microsc 1987, 147:229-263.
45. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999, 8:135-160.
46. Krouwer JS. Why Bland-Altman plots should use X, not (Y + X)/2 when X is a reference method. Stat Med 2008, 27:778-780.