Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-28T17:53:27.824Z Has data issue: false hasContentIssue false
  • English
  • Français

Validating Whole Slide Digital Morphometric Analysis as a Microscopy Tool

Published online by Cambridge University Press:  17 November 2014

Robert B. Diller  and
Robert S. Kellar
Robert B. Diller
Affiliation:
Department of Biological Sciences, Northern Arizona University, 617 S. Beaver St., P.O. Box 5640, Flagstaff, AZ 86011-5640, USA
Robert S. Kellar*
Affiliation:
Department of Biological Sciences, Northern Arizona University, 617 S. Beaver St., P.O. Box 5640, Flagstaff, AZ 86011-5640, USA Development Engineering Sciences, LLC, 708 N. Fox Hill Rd, Flagstaff, AZ 86004, USA
*
*Corresponding author. [email protected]
Get access

Abstract

Whole slide imaging (WSI) can be used to quantify multiple responses within tissue sections during histological analysis. Feature Analysis on Consecutive Tissue Sections (FACTS®) allows the investigator to perform digital morphometric analysis (DMA) within specified regions of interest (ROI) across multiple serial sections at faster rates when compared with manual morphometry methods. Using FACTS® in conjunction with WSI is a powerful analysis tool, which allows DMA to target specific ROI across multiple tissue sections stained for different biomarkers. DMA may serve as an appropriate alternative to classic, manual, histologic morphometric measures, which have historically relied on the selection of high-powered fields of views and manual scoring (e.g., a gold standard). In the current study, existing preserved samples were used to determine if DMA would provide similar results to manual counting methods. Rodent hearts (n=14, left ventricles) were stained with Masson’s trichrome, and reacted for cluster of differentiation 68 (CD-68). This study found no statistical significant difference between a classic, manual method and the use of digital algorithms to perform the similar counts (p=0.38). DMA offers researchers the ability to accurately evaluate morphological characteristics in a reproducible fashion without investigator bias and with higher throughput.

Keywords

digital pathologydigital analysisdigital morphometric analysiswhole slide analysiswhole slide quantitative analysis
Type
Biological and Biomaterials Applications
Information
Microscopy and Microanalysis , Volume 21 , Issue 1 , February 2015 , pp. 249 - 255
Copyright
© Microscopy Society of America 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Janabi, S., Huisman, A. & Van Diest, P.J. (2012). Digital pathology: Currents status and future perspectives. Histopathology 61, 1–9.CrossRefGoogle ScholarPubMed
Cole, B., Gomoll, A., Yanke, A., Pylawka, T., Lewis, P., MacGillivray, J. & Williams, J. (2007). Biocompatibility of a polymer patch for rotator cuff repair. Knee Surg Sports Traumatol Arthrosc 15(5), 632–637.Google Scholar
Crowley, R.S., Gadd, C.S., Naus, G., Becich, M. & Lowe, H.J. (2000). Defining the role of anatomic pathology images in the multimedia electronic medical record—a preliminary report. Proc AMIA Symp, 161–165.Google Scholar
Daniel, C., Rojo, M.G., Klossa, J., Mea, V.D., Booker, D., Beckwith, B.A. & Schrader, T. (2011). Standardizing the use of whole slide images in digital pathology. Comput Med Imaging Graph 35(7–8), 496–505.Google Scholar
Doussis, I.A., Gatter, K.C. & Mason, D.Y. (1993). CD68 reactivity of non-macrophage derived tumours in cytological specimens. Journal of clinical pathology 46(4), 334–336.Google Scholar
Evans, A.J., Henry, P.C., Van der Kwast, T.H., Tkachuk, D.C., Watson, K., Lockwood, G.A. & Srigley, J.R. (2008). Interobserver variability between expert urologic pathologists for extraprostatic extension and surgical margin status in radical prostatectomy specimens. Am J Surg Pathol 32(10), 1503–1512.Google Scholar
Feldman, M.D. (2008). Beyond morphology: Whole slide imaging, computer-aided detection, and other techniques. Arch Pathol Lab Med 132(5), 758–763.Google Scholar
Fujimoto, K.L., Tobita, K., Merryman, W.D., Guan, J., Momoi, N., Stolz, D.B. & Wagner, W.R. (2007). An elastic, biodegradable cardiac patch induces contractile smooth muscle and improves cardiac remodeling and function in subacute myocardial infarction. J Am Coll Cardiol 49(23), 2292–2300.CrossRefGoogle ScholarPubMed
Hipp, J., Flotte, T., Monaco, J., Cheng, J., Madabhushi, A., Yagi, Y., Rodriguez-Canales, J., Emmert-Buck, M., Dugan, M., Hewitt, S., Toner, M., Tompkins, R., Lucas, D., Gilbertson, J. & Balis, U. (2011). Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess. J Pathol Inform 2, 25.Google Scholar
Hsu, C.Y., Ho, D.M.T., Yang, C.F. & Chiang, H. (2003). Interobserver reproducibility of MIB-1 labeling index in astrocytic tumors using different counting methods. Mod Pathol 16(9), 951–957.Google Scholar
Huisman, A., Looijen, A., van den Brink, S.M. & van Diest, P.J. (2010). Creation of a fully digital pathology slide archive by high-volume tissue slide scanning. Hum Pathol 41(5), 751–757.CrossRefGoogle ScholarPubMed
Jara-Lazaro, A.R., Thamboo, T.P., Teh, M. & Tan, P.H. (2010). Digital pathology: Exploring its applications in diagnostic surgical pathology practice. Pathology 42(6), 512–518.CrossRefGoogle ScholarPubMed
Kellar, R.S., Lancaster, J.J., Thai, H.M., Juneman, E., Johnson, N.M., Byrne, H.G., Stansifer, M., Arsanjani, R., Baer, M., Bebbington, C., Flashner, M., Yarranton, G. & Goldman, S. (2011). Antibody to granulocyte macrophage colony-stimulating factor reduces the number of activated tissue macrophages and improves left ventricular function after myocardial infarction in a rat coronary artery ligation model. Journal of cardiovascular pharmacology 57(5), 568–574.Google Scholar
Kidd, K.R., Dal Ponte, D.B., Kellar, R.S. & Williams, S.K. (2001). A comparative evaluation of the tissue responses associated with polymeric implants in the rat and mouse. J Biomed Mater Res 59(4), 682–689.CrossRefGoogle Scholar
Klapczynski, M., Gagne, G.D., Morgan, S.J., Larson, K.J., LeRoy, B.E., Blomme, E.A. & Shek, E.W. (2011). Computer-assisted imaging algorithms facilitate histomorphometric quantification of kidney damage in rodent renal failure models. J Pathol Inform 3, 20.Google Scholar
Lofgren, K.A., Ostrander, J.H., Housa, D., Hubbard, G.K., Locatelli, A., Bliss, R.L. & Lange, C.A. (2011). Mammary gland specific expression of Brk/PTK6 promotes delayed involution and tumor formation associated with activation of p38 MAPK. Breast Cancer Res 13(5), R89.CrossRefGoogle ScholarPubMed
López, C., Lejeune, M., Salvadó, M.T., Escrivà, P., Bosch, R., Pons, L.E. & Jaén, J. (2008). Automated quantification of nuclear immunohistochemical markers with different complexity. Histochem Cell Biol 129(3), 379–387.Google Scholar
Mulrane, L., Rexhepaj, E., Penney, S., Callanan, J.J. & Gallagher, W.M. (2008). Automated image analysis in histopathology: a valuable tool in medical diagnostics. Expert Reviews of Molecular Diagostics 8.6: 707.Google Scholar
Nassar, A., Cohen, C., Albitar, M., Agersborg, S.S., Zhou, W., Lynch, K.A. & Siddiqui, M.T. (2011). Reading immunohistochemical slides on a computer monitor—a multisite performance study using 180 HER2-stained breast carcinomas. Appl Immunohistochem Mol Morphol 19(3), 212–217.Google Scholar
Potts, S.J., Johnson, T.D., Voelker, F.A., Lange, H. & Young, G.D. (2011). Multiplexed measurement of proteins in tissue in a clinical environment. Appl Immunohistochem Mol Morphol 19(6), 494–498.Google Scholar
Potts, S.J., Young, G.D. & Voelker, F.A. (2010). The role and impact of quantitative discovery pathology. Drug Discov Today 15(21–22), 943–950.Google Scholar
Rocha, R., Vassallo, J., Soares, F., Miller, K. & Gobbi, H. (2009). Digital slides: Present status of a tool for consultation, teaching, and quality control in pathology. Pathol Res Pract 205(11), 735–741.Google Scholar
Saraswati, S., Alfaro, M.P., Thorne, C.A., Atkinson, J., Lee, E., & Young, P.P. (2010). Pyrvinium, a potent small molecule Wnt inhibitor, promotes wound repair and post-MI cardiac remodeling. PLoS One 5(11), e15521.Google Scholar
Segura, A.M., Frazier, O.H., Demirozu, Z. & Buja, L.M. (2011). Histopathologic correlates of myocardial improvement in patients supported by a left ventricular assist device. Cardiovascular Pathology 20(3), 139–145.Google Scholar
Słodkowska, J., Filas, V., Buszkiewicz, E., Trzeciak, P., Wojciechowski, M., Koktysz, R. & Garcia Rojo, M. (2010). Study on breast carcinoma Her2/neu and hormonal receptors status assessed by automated images analysis systems: ACIS III (Dako) and ScanScope (Aperio). Folia Histochem Cytobiol 48(1), 19–25.Google Scholar
Wang, Y., Turner, R., Crookes, D., Diamond, J. & Hamilton, P. (2007). Investigation of methodologies for the segmentation of squamous epithelium from cervical histological virtual slides. In Machine Vision and Image Processing Conference, 2007 IMVIP 2007, pp. 83–90. IEEE, NUI Maynooth, Ireland.Google Scholar
Webster, J.D. & Dunstan, R.W. (2014). Whole-slide imaging and automated image analysis considerations and opportunities in the practice of pathology. Vet Pathol Online 51(1), 211–223.Google Scholar
Weinstein, R.S., Graham, A.R., Richter, L.C., Barker, G.P., Krupinski, E.A., Lopez, A.M. & Gilbertson, J.R. (2009). Overview of telepathology, virtual microscopy, and whole slide imaging: Prospects for the future. Hum Pathol 40(8), 1057–1069.Google Scholar