nach oben

Virchows Archiv

Erschienen in:

08.11.2017 | Original Article

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software

verfasst von: Andres Moon, Geoffrey H. Smith, Jun Kong, Thomas E. Rogers, Carla L. Ellis, Alton B. “Brad” Farris III

Erschienen in: Virchows Archiv | Ausgabe 2/2018

Einloggen, um Zugang zu erhalten

Abstract

Renal allograft rejection diagnosis depends on assessment of parameters such as interstitial inflammation; however, studies have shown interobserver variability regarding interstitial inflammation assessment. Since automated image analysis quantitation can be reproducible, we devised customized analysis methods for CD3+ T-cell staining density as a measure of rejection severity and compared them with established commercial methods along with visual assessment. Renal biopsy CD3 immunohistochemistry slides (n = 45), including renal allografts with various degrees of acute cellular rejection (ACR) were scanned for whole slide images (WSIs). Inflammation was quantitated in the WSIs using pathologist visual assessment, commercial algorithms (Aperio nuclear algorithm for CD3+ cells/mm² and Aperio positive pixel count algorithm), and customized open source algorithms developed in ImageJ with thresholding/positive pixel counting (custom CD3+%) and identification of pixels fulfilling “maxima” criteria for CD3 expression (custom CD3+ cells/mm²). Based on visual inspections of “markup” images, CD3 quantitation algorithms produced adequate accuracy. Additionally, CD3 quantitation algorithms correlated between each other and also with visual assessment in a statistically significant manner (r = 0.44 to 0.94, p = 0.003 to < 0.0001). Methods for assessing inflammation suggested a progression through the tubulointerstitial ACR grades, with statistically different results in borderline versus other ACR types, in all but the custom methods. Assessment of CD3-stained slides using various open source image analysis algorithms presents salient correlations with established methods of CD3 quantitation. These analysis techniques are promising and highly customizable, providing a form of on-slide “flow cytometry” that can facilitate additional diagnostic accuracy in tissue-based assessments.

Nur mit Berechtigung zugänglich

Solez K, Racusen LC (2013) The Banff classification revisited. Kidney Int 83:201–206. https://doi.org/10.1038/ki.2012.395 CrossRefPubMed

Solez K (2010) History of the Banff classification of allograft pathology as it approaches its 20th year. Curr Opin Organ Transplant 15:49–51. https://doi.org/10.1097/MOT.0b013e328334fedb CrossRefPubMed

Loupy A, Haas M, Solez K, Racusen L, Glotz D, Seron D, Nankivell BJ, Colvin RB, Afrouzian M, Akalin E, Alachkar N, Bagnasco S, Becker JU, Cornell L, Drachenberg C, Dragun D, de Kort H, Gibson IW, Kraus ES, Lefaucheur C, Legendre C, Liapis H, Muthukumar T, Nickeleit V, Orandi B, Park W, Rabant M, Randhawa P, Reed EF, Roufosse C, Seshan SV, Sis B, Singh HK, Schinstock C, Tambur A, Zeevi A, Mengel M (2017) The Banff 2015 kidney meeting report: current challenges in rejection classification and prospects for adopting molecular pathology. Am J Transplant 17:28–41. https://doi.org/10.1111/ajt.14107 CrossRefPubMed

Haas M, Sis B, Racusen LC, Solez K, Glotz D, Colvin RB, Castro MC, David DS, David-Neto E, Bagnasco SM, Cendales LC, Cornell LD, Demetris AJ, Drachenberg CB, Farver CF, Farris AB III, Gibson IW, Kraus E, Liapis H, Loupy A, Nickeleit V, Randhawa P, Rodriguez ER, Rush D, Smith RN, Tan CD, Wallace WD, Mengel M, Banff meeting report writing committee (2014) Banff 2013 meeting report: inclusion of c4d-negative antibody-mediated rejection and antibody-associated arterial lesions. Am J Transplant 14:272–283. https://doi.org/10.1111/ajt.12590 CrossRefPubMed

Racusen LC, Solez K, Colvin RB, Bonsib SM, Castro MC, Cavallo T, Croker BP, Demetris AJ, Drachenberg CB, Fogo AB, Furness P, Gaber LW, Gibson IW, Glotz D, Goldberg JC, Grande J, Halloran PF, Hansen HE, Hartley B, Hayry PJ, Hill CM, Hoffman EO, Hunsicker LG, Lindblad AS, Yamaguchi Y (1999) The Banff 97 working classification of renal allograft pathology. Kidney Int 55:713–723CrossRefPubMed

Solez K, Axelsen RA, Benediktsson H, Burdick JF, Cohen AH, Colvin RB, Croker BP, Droz D, Dunnill MS, Halloran PF, Häyry P, Jennette JC, Keown PA, Marcussen N, Mihatsch MJ, Moruzumi K, Myers BD, Nast CC, Olsen S, Racusen LC, Ramos EL, Rosen S, Sachs DH, Salomon DR, Sanfilippo F, Verani R, von Willebrand E, Yamaguchi Y (1993) International standardization of criteria for the histologic diagnosis of renal allograft rejection: the Banff working classification of kidney transplant pathology. Kidney Int 44:411–422CrossRefPubMed

Williams WW, Taheri D, Tolkoff-Rubin N, Colvin RB (2012) Clinical role of the renal transplant biopsy. Nat Rev Nephrol 8:110–121. https://doi.org/10.1038/nrneph.2011.213 CrossRefPubMedPubMedCentral

Furness PN, Taub N, Assmann KJ, Banfi G, Cosyns JP, Dorman AM, Hill CM, Kapper SK, Waldherr R, Laurinavicius A, Marcussen N, Martins AP, Nogueira M, Regele H, Seron D, Carrera M, Sund S, Taskinen EI, Paavonen T, Tihomirova T, Rosenthal R (2003) International variation in histologic grading is large, and persistent feedback does not improve reproducibility. Am J Surg Pathol 27:805–810CrossRefPubMed

Farris AB, Chan S, Climenhaga J, Adam B, Bellamy CO, Seron D, Colvin RB, Reeve J, Mengel M (2014) Banff fibrosis study: multicenter visual assessment and computerized analysis of interstitial fibrosis in kidney biopsies. Am J Transplant 14:897–907. https://doi.org/10.1111/ajt.12641 CrossRefPubMed

10.

Nicholson ML, McCulloch TA, Harper SJ, Wheatley TJ, Edwards CM, Feehally J, Furness PN (1996) Early measurement of interstitial fibrosis predicts long-term renal function and graft survival in renal transplantation. Br J Surg 83:1082–1085CrossRefPubMed

11.

Nicholson ML, Bailey E, Williams S, Harris KP, Furness PN (1999) Computerized histomorphometric assessment of protocol renal transplant biopsy specimens for surrogate markers of chronic rejection. Transplantation 68:236–241CrossRefPubMed

12.

Farris AB, Adams CD, Brousaides N, Della Pelle PA, Collins AB, Moradi E, Smith RN, Grimm PC, Colvin RB (2010) Morphometric and visual evaluation of fibrosis in renal biopsies. J Am Soc Nephrol 2:176–186. https://doi.org/10.1681/ASN.2009091005

13.

Grimm PC, Nickerson P, Gough J, McKenna R, Jeffery J, Birk P, Rush DN (1999) Quantitation of allograft fibrosis and chronic allograft nephropathy. Pediatr Transplant 3:257–270CrossRefPubMed

14.

Grimm PC, Nickerson P, Gough J, McKenna R, Stern E, Jeffery J, Rush DN (2003) Computerized image analysis of Sirius Red-stained renal allograft biopsies as a surrogate marker to predict long-term allograft function. J Am Soc Nephrol 14:1662–1668CrossRefPubMed

15.

Pape L, Henne T, Offner G, Strehlau J, Ehrich JH, Mengel M, Grimm PC (2003) Computer-assisted quantification of fibrosis in chronic allograft nephropaty by picosirius red-staining: a new tool for predicting long-term graft function. Transplantation 76:955–958. https://doi.org/10.1097/01.TP.0000078899.62040.E5 CrossRefPubMed

16.

Sund S, Grimm P, Reisaeter AV, Hovig T (2004) Computerized image analysis vs semiquantitative scoring in evaluation of kidney allograft fibrosis and prognosis. Nephrol Dial Transplant 19:2838–2845CrossRefPubMed

17.

Servais A, Meas-Yedid V, Buchler M, Morelon E, Olivo-Marin JC, Lebranchu Y, Legendre C, Thervet E (2007) Quantification of interstitial fibrosis by image analysis on routine renal biopsy in patients receiving cyclosporine. Transplantation 84:1595–1601. https://doi.org/10.1097/01.tp.0000295749.50525.bd CrossRefPubMed

18.

Servais A, Meas-Yedid V, Toupance O, Lebranchu Y, Thierry A, Moulin B, Etienne I, Presne C, Hurault de LB, Le Pogamp P, Le Meur Y, Glotz D, Hayem C, Olivo Marin JC, Thervet E (2009) Interstitial fibrosis quantification in renal transplant recipients randomized to continue cyclosporine or convert to sirolimus. Am J Transplant 9:2552–2560. https://doi.org/10.1111/j.1600-6143.2009.02803.x CrossRefPubMed

19.

Meas-Yedid V, Servais A, Noel LH, Panterne C, Landais P, Herve N, Brousse N, Kreis H, Legendre C, Thervet E, Olivo-Marin JC, Morelon E (2011) New computerized color image analysis for the quantification of interstitial fibrosis in renal transplantation. Transplantation 92:890–899. https://doi.org/10.1097/TP.0b013e31822d879a CrossRefPubMed

20.

Servais A, Meas-Yedid V, Noel LH, Martinez F, Panterne C, Kreis H, Zuber J, Timsit MO, Legendre C, Olivo-Marin JC, Thervet E (2011) Interstitial fibrosis evolution on early sequential screening renal allograft biopsies using quantitative image analysis. Am J Transplant 11:1456–1463. https://doi.org/10.1111/j.1600-6143.2011.03594.x CrossRefPubMed

21.

Aperio Technologies (2009) Aperio | Support | Documentation. Aperio Technologies, Inc., Vista

22.

Aperio Technologies (2009) Image analysis | Aperio. Aperio Technologies, Inc., Vista

23.

Farris AB, Ellis CL, Rogers TE, Lawson D, Cohen C, Rosen S (2016) Renal medullary and cortical correlates in fibrosis, epithelial mass, microvascularity, and microanatomy using whole slide image analysis morphometry. PLoS One 11:e0161019. https://doi.org/10.1371/journal.pone.0161019 CrossRefPubMedPubMedCentral

24.

Farris AB III, Lauwers GY, Deshpande V (2010) Autoimmune pancreatitis-related diabetes: quantitative analysis of endocrine islet cells and inflammatory infiltrate. Virchows Arch 457:329–336. https://doi.org/10.1007/s00428-010-0948-y CrossRefPubMed

25.

Zack GW, Rogers WE, Latt SA (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25:741–753CrossRefPubMed

26.

Meyer F (1994) Topographic distance and watershed lines. Signal Process 38:113–125. https://doi.org/10.1016/0165-1684(94)90060-4 CrossRef

27.

Demetris AJ, Bellamy C, Hubscher SG, O'Leary J, Randhawa PS, Feng S, Neil D, Colvin RB, McCaughan G, Fung JJ, Del Bello A, Reinholt FP, Haga H, Adeyi O, Czaja AJ, Schiano T, Fiel MI, Smith ML, Sebagh M, Tanigawa RY, Yilmaz F, Alexander G, Baiocchi L, Balasubramanian M, Batal I, Bhan AK, Bucuvalas J, Cerski CT, Charlotte F, de Vera ME, ElMonayeri M, Fontes P, Furth EE, Gouw AS, Hafezi-Bakhtiari S, Hart J, Honsova E, Ismail W, Itoh T, Jhala NC, Khettry U, Klintmalm GB, Knechtle S, Koshiba T, Kozlowski T, Lassman CR, Lerut J, Levitsky J, Licini L, Liotta R, Mazariegos G, Minervini MI, Misdraji J, Mohanakumar T, Molne J, Nasser I, Neuberger J, O'Neil M, Pappo O, Petrovic L, Ruiz P, Sagol O, Sanchez Fueyo A, Sasatomi E, Shaked A, Shiller M, Shimizu T, Sis B, Sonzogni A, Stevenson HL, Thung SN, Tisone G, Tsamandas AC, Wernerson A, Wu T, Zeevi A, Zen Y (2016) 2016 comprehensive update of the Banff working group on liver allograft pathology: introduction of antibody-mediated rejection. Am J Transplant. https://doi.org/10.1111/ajt.13909

28.

Drachenberg CB, Torrealba JR, Nankivell BJ, Rangel EB, Bajema IM, Kim DU, Arend L, Bracamonte ER, Bromberg JS, Bruijn JA, Cantarovich D, Chapman JR, Farris AB, Gaber L, Goldberg JC, Haririan A, Honsova E, Iskandar SS, Klassen DK, Kraus E, Lower F, Odorico J, Olson JL, Mittalhenkle A, Munivenkatappa R, Paraskevas S, Papadimitriou JC, Randhawa P, Reinholt FP, Renaudin K, Revelo P, Ruiz P, Samaniego MD, Shapiro R, Stratta RJ, Sutherland DE, Troxell ML, Voska L, Seshan SV, Racusen LC, Bartlett ST (2011) Guidelines for the diagnosis of antibody-mediated rejection in pancreas allografts-updated Banff grading schema. Am J Transplant 11:1792–1802. https://doi.org/10.1111/j.1600-6143.2011.03670.x CrossRefPubMed

29.

Stewart S, Winters GL, Fishbein MC, Tazelaar HD, Kobashigawa J, Abrams J, Andersen CB, Angelini A, Berry GJ, Burke MM, Demetris AJ, Hammond E, Itescu S, Marboe CC, McManus B, Reed EF, Reinsmoen NL, Rodriguez ER, Rose AG, Rose M, Suciu-Focia N, Zeevi A, Billingham ME (2005) Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J Heart Lung Transplant 24:1710–1720. https://doi.org/10.1016/j.healun.2005.03.019 CrossRefPubMed

30.

Berry GJ, Burke MM, Andersen C, Bruneval P, Fedrigo M, Fishbein MC, Goddard M, Hammond EH, Leone O, Marboe C, Miller D, Neil D, Rassl D, Revelo MP, Rice A, Rene Rodriguez E, Stewart S, Tan CD, Winters GL, West L, Mehra MR, Angelini A (2013) The 2013 International Society for Heart and Lung Transplantation Working Formulation for the standardization of nomenclature in the pathologic diagnosis of antibody-mediated rejection in heart transplantation. J Heart Lung Transplant 32:1147–1162. https://doi.org/10.1016/j.healun.2013.08.011 CrossRefPubMed

31.

Stewart S, Fishbein MC, Snell GI, Berry GJ, Boehler A, Burke MM, Glanville A, Gould FK, Magro C, Marboe CC, McNeil KD, Reed EF, Reinsmoen NL, Scott JP, Studer SM, Tazelaar HD, Wallwork JL, Westall G, Zamora MR, Zeevi A, Yousem SA (2007) Revision of the 1996 working formulation for the standardization of nomenclature in the diagnosis of lung rejection. J Heart Lung Transplant 26:1229–1242. https://doi.org/10.1016/j.healun.2007.10.017 CrossRefPubMed

32.

Cendales LC, Kanitakis J, Schneeberger S, Burns C, Ruiz P, Landin L, Remmelink M, Hewitt CW, Landgren T, Lyons B, Drachenberg CB, Solez K, Kirk AD, Kleiner DE, Racusen L (2008) The Banff 2007 working classification of skin-containing composite tissue allograft pathology. Am J Transplant 8:1396–1400. https://doi.org/10.1111/j.1600-6143.2008.02243.x CrossRefPubMed

33.

Mannon RB, Matas AJ, Grande J, Leduc R, Connett J, Kasiske B, Cecka JM, Gaston RS, Cosio F, Gourishankar S, Halloran PF, Hunsicker L, Rush D (2010) Inflammation in areas of tubular atrophy in kidney allograft biopsies: a potent predictor of allograft failure. Am J Transplant 10:2066–2073. https://doi.org/10.1111/j.1600-6143.2010.03240.x CrossRefPubMedPubMedCentral

34.

Mengel M, Gwinner W, Schwarz A, Bajeski R, Franz I, Brocker V, Becker T, Neipp M, Klempnauer J, Haller H, Kreipe H (2007) Infiltrates in protocol biopsies from renal allografts. Am J Transplant 7:356–365CrossRefPubMed

35.

Farris AB, Cohen C, Rogers TE, Smith GH (2017) Whole slide imaging for analytical anatomic pathology and telepathology: practical applications today, promises, and perils. Arch Pathol Lab Med 141:542–550. https://doi.org/10.5858/arpa.2016-0265-SA CrossRefPubMed

36.

Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform 7:29. https://doi.org/10.4103/2153-3539.186902 CrossRefPubMedPubMedCentral

37.

Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35:1313–1321. https://doi.org/10.1109/TMI.2016.2528120 CrossRefPubMed

38.

Xu J, Luo X, Wang G, Gilmore H, Madabhushi A (2016) A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191:214–223. https://doi.org/10.1016/j.neucom.2016.01.034 CrossRefPubMedPubMedCentral

Titel: Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software
verfasst von: Andres Moon
Geoffrey H. Smith
Jun Kong
Thomas E. Rogers
Carla L. Ellis
Alton B. “Brad” Farris III
Publikationsdatum: 08.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Virchows Archiv / Ausgabe 2/2018
Print ISSN: 0945-6317
Elektronische ISSN: 1432-2307
DOI: https://doi.org/10.1007/s00428-017-2260-6

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software

Abstract

Neu im Fachgebiet Pathologie

Assistierter Suizid durch Infusion von Thiopental

Molekularpathologische Untersuchungen im Wandel der Zeit

Vergleichende Pathologie in der onkologischen Forschung

Gastrointestinale Stromatumoren

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 2/2018

Correction to: Abstracts

Tumor regression grading of gastrointestinal cancers after neoadjuvant therapy

Histologic analysis has a prognostical value in colorectal biopsies assessed for suspicion of graft-versus-host disease

Langerhans und Merkel: a nervous epidermal dispute

Pacemaker failure as a cause of sudden death

Correction to: Abstracts

Neu im Fachgebiet Pathologie

Assistierter Suizid durch Infusion von Thiopental

Molekularpathologische Untersuchungen im Wandel der Zeit

Vergleichende Pathologie in der onkologischen Forschung

Gastrointestinale Stromatumoren