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Reproducibility of Immunostaining Quantification and Description of a New Digital Image Processing Procedure for Quantitative Evaluation of Immunohistochemistry in Pathology

Published online by Cambridge University Press:  03 July 2009

Vagner Bernardo*
Affiliation:
Universidade Federal Fluminense, Faculdade de Medicina, Programa de Pós-graduação em Patologia, Rua Marquês do Paraná, 303 - 4o andar – sala 1, Hospital Universitário Antônio Pedro - Centro, 24033-900, Niterói, RJ, Brazil
Simone Q.C. Lourenço
Affiliation:
Universidade Federal Fluminense, Faculdade de Medicina, Programa de Pós-graduação em Patologia, Rua Marquês do Paraná, 303 - 4o andar – sala 1, Hospital Universitário Antônio Pedro - Centro, 24033-900, Niterói, RJ, Brazil
Renato Cruz
Affiliation:
Universidade Federal Fluminense, Faculdade de Medicina, Programa de Pós-graduação em Patologia, Rua Marquês do Paraná, 303 - 4o andar – sala 1, Hospital Universitário Antônio Pedro - Centro, 24033-900, Niterói, RJ, Brazil
Luiz H. Monteiro-Leal
Affiliation:
Universidade do Estado do Rio de Janeiro, Departamento de Histologia e Embriologia, Laboratório de Microscopia e Processamento de Imagens, Av. Prof. Manoel de Abreu, 48 - 3o andar - Maracanã, 20550-170, Rio de Janeiro, RJ, Brazil
Licínio E. Silva
Affiliation:
Universidade Federal Fluminense, Instituto de Matemática, Departamento de Estatística, Rua Mário Santos Braga s/n - 7o andar Campus do Valonguinho - Centro 24020-140, Niterói, RJ, Brazil
Danielle R. Camisasca
Affiliation:
Universidade Federal Fluminense, Faculdade de Medicina, Programa de Pós-graduação em Patologia, Rua Marquês do Paraná, 303 - 4o andar – sala 1, Hospital Universitário Antônio Pedro - Centro, 24033-900, Niterói, RJ, Brazil
Marcos Farina
Affiliation:
Universidade Federal do Rio de Janeiro, Instituto de Ciências Biomédicas, Laboratório de Biomineralização. CCS, Bloco F, Sala F2-027, 21941-590, Rio de Janeiro, RJ, Brazil
Ulysses Lins
Affiliation:
Universidade Federal do Rio de Janeiro, Centro de Ciências da Saúde, Bloco I, Instituto de Microbiologia Professor Paulo de Góes, Av Carlos Chagas Filho, 373 Cidade Universitária, 21941-902, Rio de Janeiro, RJ, Brazil
*
Corresponding author. E-mail: vagnerbernardorj@gmail.com
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Abstract

Quantification of immunostaining is a widely used technique in pathology. Nonetheless, techniques that rely on human vision are prone to inter- and intraobserver variability, and they are tedious and time consuming. Digital image analysis (DIA), now available in a variety of platforms, improves quantification performance: however, the stability of these different DIA systems is largely unknown. Here, we describe a method to measure the reproducibility of DIA systems. In addition, we describe a new image-processing strategy for quantitative evaluation of immunostained tissue sections using DAB/hematoxylin-stained slides. This approach is based on image subtraction, using a blue low pass filter in the optical train, followed by digital contrast and brightness enhancement. Results showed that our DIA system yields stable counts, and that this method can be used to evaluate the performance of DIA systems. The new image-processing approach creates an image that aids both human visual observation and DIA systems in assessing immunostained slides, delivers a quantitative performance similar to that of bright field imaging, gives thresholds with smaller ranges, and allows the segmentation of strongly immunostained areas, all resulting in a higher probability of representing specific staining. We believe that our approach offers important advantages to immunostaining quantification in pathology.

Type
Biological Applications
Copyright
Copyright © Microscopy Society of America 2009

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References

REFERENCES

Bacus, S., Chin, D., Stewart, J., Zelnick, C., Mahvi, D. & Gilchrist, K. (1997). Potential use of image analysis for the evaluation of cellular predicting factors for therapeutic response in breast cancers. Anal Quant Cytol Histol 19, 316328.Google ScholarPubMed
Baxes, G.A. (1994). Digital Image Processing. Principles and Applications, 1st ed. New York: John Wiley & Sons.Google Scholar
Benali, A., Leefken, I., Eysel, U.T. & Weiler, E. (2003). A computerized image analysis system for quantitative analysis of cells in histological brain sections. J Neurosc Methods 125, 3343.CrossRefGoogle ScholarPubMed
Bilous, M., Dowsett, M., Hanna, W., Isola, J., Lebeau, A., Moreno, A., Penault-Llorca, F., Rüschoff, J., Tomasic, G. & Van De Vijver, M. (2003). Current perspectives on HER2 testing: A review of national testing guidelines. Mod Pathol 16, 173182.CrossRefGoogle ScholarPubMed
Bishop, P.W. (2002). An immunohistochemical vademecum. Curr Diag Pathol 8, 123127.CrossRefGoogle Scholar
Bloom, K. & Harrington, D. (2004). Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. Am J Clin Pathol 121, 620630.CrossRefGoogle ScholarPubMed
Brey, E.M., Lalani, Z., Johnston, C., Wong, M., Mcintire, L.V., Duke, P.J. & Patrick, C.W. Jr. (2003). Automated selection of DAB-labeled tissue for immunohistochemical quantification. J Histochem Cytochem 51, 575584.CrossRefGoogle ScholarPubMed
Camisasca, D.R., Honorato, J., Bernardo, V., Silva, L.E., Fonseca, E.C., Faria, P.A.S., Dias, F.L. & Lourenço, S.Q.C. (2009). Expression of Bcl-2 family proteins and associated clinicopathologic factors predict survival outcome in patients with oral squamous cell carcinoma. Oral Oncol 45, 225233.CrossRefGoogle ScholarPubMed
Caulet, S., Lesty, C., Raphael, M., Schoevaert, D., Brousset, P., Binet, J.-L., Diebold, J. & Delsol, G. (1992). Comparative quantitative study of Ki-67 antibody staining in 78 B and T cell malignant lymphoma (ML) using two image analyser systems. Pathol Res Pract 188, 490496.CrossRefGoogle Scholar
Chen, W., Reiss, M. & Foran, D.J. (2004). A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics. IEEE Trans Inf Technol Biomed 8, 8996.CrossRefGoogle ScholarPubMed
De la Grandmaison, G.L., Dorandeu, A., Carton, M., Patey, A. & Durigon, M. (1999). Increase of pulmonary density of macrophages in sudden infant death syndrome. For Sci Intern 104, 179187.Google Scholar
Derkx, P., Nigg, A.L., Bosman, F.T., Birkenhäger-Frenkel, D.H., Houtmuller, A.B., Pols, H.A.P. & Van Leeuwen, J.P.T.M. (1998). Immunolocalization and quantification of noncollagenous bone matrix proteins in methylmethacrylate-embedded adult human bone in combination with histomorphometry. Bone 22, 367373.CrossRefGoogle ScholarPubMed
Ednaggar, A.K., Lai, S., Clayman, G.L., Zhou, J.-H., Tucker, S.A., Myers, J., Luna, M.A. & Benedict, W.F. (1999). Expression of p 16, Rb, and cyclin D1 gene products in oral and laryngeal squamous carcinoma: Biological and clinical implications. Hum Pathol 30, 10131018.CrossRefGoogle Scholar
Ellis, C.M., Dyson, M.J., Stephenson, T.J. & Maltby, E.L. (2005). HER2 amplification status in breast cancer: A comparison between immunohistochemical staining and fluorescence in situ hybridisation using manual and automated quantitative image analysis scoring techniques. J Clin Pathol 58, 710714.CrossRefGoogle ScholarPubMed
Fritz, P., Wu, X., Tuczek, H., Multhaupt, H. & Schwarzmann, P. (1995). Quantitation in immunohistochemistry. A research method or a diagnostic tool in surgical pathology? Pathologica 87, 300309.Google ScholarPubMed
Gala, J.-L., Guiot, Y., Delannoy, A., Scheiff, J.-M., Philippe, M. & Martiat, P. (1999). Use of image analysis and immunostaining of bone marrow trephine biopsy specimens to quantify residual disease in patients with B-cell chronic lymphocytic leukemia. Mod Pathol 12, 391399.Google ScholarPubMed
Giardina, C., Serio, G., Caniglia, D.M., Lettini, T., Ricco, R., Renzulli, G. & Pesce Delfino, V. (1994). Nuclear morphology and histological grading of oral squamous cell carcinoma (OSCC). A morphometric study. J Biol Res 70, 271279.Google ScholarPubMed
Goto, M., Nagatomo, Y., Hasui, K., Yamanaka, H., Murashima, S. & Sato, E. (1992). Chromacity analysis of immunostained tumor specimens. Path Res Pract 188, 433437.CrossRefGoogle Scholar
Hatanaka, Y., Hashizume, K., Nitta, K., Kato, T., Itoh, I. & Tani, Y. (2003). Cytometrical image analysis for immunohistochemical hormone receptor status in breast carcinomas. Pathol Int 53, 693699.CrossRefGoogle ScholarPubMed
Hendricks, J.B., Rainer, R. & Munakata, S. (1995). Computer-assisted and visual methods of assessing cellular proliferation in tissue sections from non-hodgkin's lymphoma. Anal Quant Cytol Histol 17, 383388.Google ScholarPubMed
Huang, X., Chen, S. & Tietz, E.I. (1996). Immunocytochemical detection of regional protein changes in rat brain sections using computer-assisted image analysis. J Histochem Cytochem 44, 981987.CrossRefGoogle ScholarPubMed
Inoué, S. & Spring, K.R. (1997). Video Microscopy. The Fundamentals, 2nd ed. New York: Plenum Press.CrossRefGoogle Scholar
Jacobs, J.J.L., Lehé, C., Cammans, K.D.A., Yoneda, K., Das, P.K. & Elliott, G.R. (2001). An automated method for the quantification of immunostained human Langerhans cells. J Immunol Methods 247, 7382.CrossRefGoogle ScholarPubMed
Jalava, P., Kuopio, T., Juntti-Patinen, L., Kotkansalo, T., Kronqvist, P. & Collan, Y. (2006). Ki67 immunohistochemistry: A valuable marker in prognostication but with a risk of misclassification: Proliferation subgroups formed based on ki67 immunoreactivity and standardized mitotic index. Histopathol 48, 674682.CrossRefGoogle ScholarPubMed
Karlsson, M.G., Davidsson, Å. & Hellquist, H.B. (1994). Quantitative computerized image analysis of immunostained lymphocites. A methodological approach. Path Res Pract 190, 799807.CrossRefGoogle Scholar
Katayama, A., Bandoh, N., Kishibe, K., Takahara, M., Ogino, T., Nonaka, S. & Harabuchi, Y. (2004). Expressions of matrix metalloproteinases in early-stage oral squamous cell carcinoma as predictive indicators for tumor metastases and prognosis. Clin Can Res 10, 634640.CrossRefGoogle ScholarPubMed
Kennedy, J.C., El-Badawy, N., Derose, P.B. & Cohen, C. (1992). Comparison of cell proliferation in breast carcinoma using image analysis (Ki-67) and flow cytometric systems. Anal Quant Cytol Histol 14, 304311.Google ScholarPubMed
Kinoshita, Y., Inoue, S., Honma, Y. & Shimura, K. (1992). Diagnostic significance of nuclear DNA content and nuclear area in oral hyperplasia, dysplasia, and carcinoma. J Oral Maxillofac Surg 50, 728733.CrossRefGoogle ScholarPubMed
Kirkegaard, T., Edwards, J., Tovey, S., McGlynn, L.M., Krishna, S.N., Mukherjee, R., Tam, L., Munro, A.F., Dunne, B. & Bartlett, J.M.S. (2006). Observer variation in immunohistochemical analysis of protein expression, time for a change? Histopathol 48, 787794.CrossRefGoogle ScholarPubMed
Kokolakis, G., Panagis, L., Stathopoulos, E., Giannikaki, E., Tosca, A. & Krüger-Krasagakis, S. (2008). From the protein to the graph: How to quantify immunohistochemistry staining of the skin using digital imaging. J Immunol Methods 331, 140146.CrossRefGoogle Scholar
Kraan, M.C., Haringman, J.J., Ahern, M.J., Breedveld, F.C., Smith, M.D. & Tak, P.P. (2000). Quantification of the cell infiltrate in synovial tissue by digital image analysis. Rheumatology 39, 4349.CrossRefGoogle ScholarPubMed
Kraan, M.C., Smith, M.D., Weedon, H., Ahern, M.J., Breedveld, F.C. & Tak, P.P. (2001). Measurement of cytokine and adhesion molecule expression in synovial tissue by digital image analysis. Ann Rheum Dis 60, 296298.CrossRefGoogle ScholarPubMed
Kuropkat, C., Venkatesan, T.K., Caldarelli, D.D., Panje, W.R., Hutchinson, J., Preisler, H.D., Coon, J.S. & Werner, J.A. (2002). Abnormalities of molecular regulators of proliferation and apoptosis in carcinoma of the oral cavity and oropharynx. Auris Nasus Larynx 29, 165174.CrossRefGoogle ScholarPubMed
Law, A.K.W., Lam, K.Y., Lam, F.K., Wong, T.K.W., Poon, J.L.S. & Chan, F.H.Y. (2003). Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in esophageal squamous cell carcinoma. Comput Methods Programs Biomed 70, 3745.CrossRefGoogle Scholar
Layfield, L.J., Saria, E.A., Conlon, D.H. & Kerns, B.-J. M. (1996). Estrogen and progesterone receptor status determined by the ventana ES320 automated immunohistochemical stainer and the CAS 200 Image analyser in 236 early-stage breast carcinomas: Prognostic significance. J Surg Oncol 61, 177184.3.0.CO;2-8>CrossRefGoogle ScholarPubMed
Lehr, H.-A., Mankoff, D.A., Corwin, D., Santeusanio, G. & Gown, A.M. (1997). Application of photoshop-based image analysis to quantification of hormone receptor expression in breast cancer. J Histochem Cytochem 45, 15591565.CrossRefGoogle ScholarPubMed
Lehr, H.-A., Van Der Loos, C.M., Teeling, P. & Gown, A.M. (1999). Complete chromogen separation and analysis in double immunohistochemical stains using photoshop-based image analysis. J Histochem Cytochem 47, 119125.CrossRefGoogle ScholarPubMed
Leong, A.S.-Y. (2004). Quantitation in immunohistology: Fact or fiction? A discussion of variables that influence results. Appl Immunohistochem Mol Morphol 12, 17.CrossRefGoogle ScholarPubMed
Mao, K.Z., Zhao, P. & Tan, P.-H. (2006). Supervised learning-based cell image segmentation for P53 immunohistochemistry. IEEE Trans Biomed Eng 53, 11531163.CrossRefGoogle ScholarPubMed
Marioni, G., Blandamura, S., Giacomelli, L., Calgaro, N., Segato, P., Leo, G., Fischetto, D., Staffieri, A. & De Filippis, C. (2005). Nuclear expression of maspin is associated with a lower recurrence rate and a longer DISBEase-free interval after surgery for squamous cell carcinoma of the larynx. Histopathology 46, 576582.CrossRefGoogle Scholar
Maudelonde, T., Brouillet, J.P., Roger, P., Giraudier, V., Pages, A. & Rochefort, H. (1992). Immunostaining of cathepsin D in breast cancer: Quantification by computerised image analysis and correlation with cytosolic assay. Eur J Cancer 28A, 16861691.CrossRefGoogle ScholarPubMed
Molenaar, W.M., Plaat, B.E.C., Berends, E.R. & Te Meerman, G.J. (2000). Observer reliability in assessment of mitotic activity and MIB-1–determined proliferation rate in pediatric sarcomas. Ann Diagn Pathol 4, 228235.CrossRefGoogle ScholarPubMed
Murray, T.J., Fowler, P.A., Abramovich, D.R., Haites, N. & Lea, R.G. (2000). Human fetal testis: Second trimester proliferative and steroidogenic capacities. J Clin Endocrinol Metab 85, 48124817.Google ScholarPubMed
Nagler, R.M., Kerner, H., Laufer, D., Ben-Eliezer, S., Minkov, I. & Ben-Itzhak, O. (2002). Squamous cell carcinoma of the tongue: The prevalence and prognostic roles of p53, Bcl-2, c-erbB-2 and apoptotic rate as related to clinical and pathological characteristics in a retrospective study. Cancer Lett 186, 137150.CrossRefGoogle ScholarPubMed
Obenauer-Kutner, L.J., Halperin, R., Ihnat, P.M., Tully, C.P., Bordens, R.W. & Grace, M.J. (2005). Use of an automated image processing program to quantify recombinant adenovirus particles. Microsc Microanal 11, 3741.CrossRefGoogle ScholarPubMed
Ornberg, R.L., Woerner, B.M. & Edwards, D.A. (1999). Analysis of stained objects in histological sections by spectral imaging and differential absorption. J Histochem Cytochem 47, 13071313.CrossRefGoogle ScholarPubMed
Ostrowski, M.L., Chakraborty, S., Laucirica, R., Brown, R.W. & Greenberg, S.D. (1995). Quantitative image analysis of MIB-1 immunoreactivity. A comparison with flow cytometric assessment of proliferative activity in invasive carcinoma of the breast. Anal Quant Cytol Histol 17, 1524.Google ScholarPubMed
Persohn, E., Seewald, W., Bauer, J. & Schreiber, J. (2007). Cell proliferation measurement in cecum and colon of rats using scanned images and fully automated image analysis: Validation of method. Exp Toxicol Pathol 58, 411418.CrossRefGoogle ScholarPubMed
Pham, N.A., Morrison, A., Schwock, J., Aviel-Ronen, S., Iakovlev, V., Tsao, M.S., Ho, J. & Hedley, D.W. (2007). Quantitative image analysis of immunohistochemical stains using a CMYK color model. Diagn Pathol 2, 8.CrossRefGoogle ScholarPubMed
Ruifrok, A.C. (1997). Quantification of immunohistochemical staining by color translation and automated thresholding. Analyt Quant Cytol Histol 19, 107113.Google ScholarPubMed
Russ, J.C. (1990). Computer-Assisted Microscopy. The Measurement and Analysis of Images. New York: Plenum Press.CrossRefGoogle Scholar
Sarker, S.K. & Patel, K.S. (1997). Mean nuclear and chromossomal DNA content of squamous cell carcinomas of the oral cavity using computerized image analysis. J Laryngol Otol 111, 141144.CrossRefGoogle Scholar
Sarker, S.K. & Patel, K.S. (2001). Mean nuclear area of squamous cell carcinomas of the head and neck using image cytometry. Anal Quant Cytol Histol 23, 413417.Google ScholarPubMed
Schwartz, B.R., Pinkus, G., Bacus, S., Toder, M. & Weinberg, D.S. (1989). Cell proliferation in NH lymphomas. Digital image analysis of Ki-67 antibody staining. Am J Pathol 134, 327336.Google Scholar
Sekine, J., Uehara, M., Hideshima, K., Irie, A. & Inokuchi, T. (2003). Predictability of lymph node metastases by preoperative nuclear morphometry in squamous cell carcinoma of the tongue. Cancer Detect Prev 27, 427433.CrossRefGoogle ScholarPubMed
Stute, P., Wood, C.E., Kaplan, J.R. & Cline, J.M. (2004). Cyclic changes in the mammary gland of cynomolgus macaques. Fertil Steril 82(s3), 11601170.CrossRefGoogle ScholarPubMed
Taylor, C.R. & Levenson, R.M. (2006). Quantification of immunohistochemistry—Issues concerning methods, utility and semiquantitative assessment II. Histopathol 49, 411424.CrossRefGoogle ScholarPubMed
Tumuluri, V., Thomas, G.A. & Fraser, I.S. (2002). Analysis of the Ki-67 antigen at the invasive tumour front of human oral squamous cell carcinoma. J Oral Pathol Med 31, 598604.CrossRefGoogle ScholarPubMed
Walker, R.A. (2006). Quantification of immunohistochemistry—Issues concerning methods, utility and semiquantitative assessment I. Histopathol 49, 406410.CrossRefGoogle ScholarPubMed
Walsh, C.T., Wei, Y., Wientjes, M.G. & Au, J.L.S. (2008). Quantitative image analysis of intra-tumoral bFGF level as a molecular marker of paclitaxel resistance. J Trans Med 6, 4.CrossRefGoogle ScholarPubMed
Wu, C., Zhao, W., Lin, B. & Ginsberg, M.D. (2005). Semi-automated image processing system for micro- to macro-scale analysis of immunohistopathology: Application to ischemic brain tissue. Comput Methods Programs Biomed 78, 7586.CrossRefGoogle ScholarPubMed
Xu, Y.H., Sattler, G.L., Edwards, H. & Pitot, H.C. (2000). Nuclear-labeling index analysis (NLIA), a software package used to perform accurate automation of cell nuclear-labeling index analysis on immunohistochemically stained rat liver samples. Comput Methods Programs Biomed 63, 5570.CrossRefGoogle ScholarPubMed
Zhang, K., Prichard, J.W., Yoder, S., De, J. & Lin, F. (2007). Utility of SKP2 and MIB-1 in grading follicular lymphoma using quantitative imaging analysis. Hum Pathol 38, 878882.CrossRefGoogle ScholarPubMed
Zhou, R., Hammond, E.H. & Parker, D.L. (1996). A multiple wavelenght algorithm in color image analysis and its applications in stain decomposition in microscopy images. Med Phys 23, 19771986.CrossRefGoogle Scholar
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