How to implement digital pathology in tissue-based diagnosis (surgical pathology)?
Digital pathology has proven to become a reliable tool to be applied in pathology education and experimental measurements. However, it is still waiting at the front door of routine surgical pathology or tissue – based diagnosis. Why?
Tissue – based diagnoses includes all diagnostic tools and algorithms that serve or can serve for diagnostic purposes in general. Any medical diagnosis procedure maps a panel of “findings” to a set of discrete diseases that include recommendations for the most efficient treatment. In surgical pathology, the “findings” include data that are derived from a) image content information, b) clinical history, c) expertise of the pathologist, d) knowledge about the disease. These are transferred using a statistical decision algorithm (neural network, discriminate analysis, factor analysis, etc.) into a diagnosis.
Image content information (ICI)
Several commonly not well trained pathologists assume that microscopic images or any morphology is representative for all kinds of diagnoses. They overestimate ICI which includes the information that can be derived from an image without any external knowledge. In fact, a disease can manifest with different morphologies and vice versa. Therefore, a correct diagnosis has to include all contributions of information, especially when computerized algorithms are used. Electronic communication is a useful tool to collect all available information for diagnostic purposes. Such a system should consider the different clinical impact and the level (details) of a stated diagnosis. It ranges from crude diagnosis such as cancer, infection, etc. to predictive diagnosis or even the evaluation of a disease risk (beast cancer associated genes) prior to its manifestation.
Preparations of image content information measurements
Any measurement is a communication to transfer image information to interpretation. This procedure has to be standardized by pre - analysis algorithms. They include optical issues (shading, gray value distribution, image size, object size, etc.) and issues of interpretation (regions of interest, object features, structures, and texture. They can be used to control and improve the laboratory’s quality.
Digital pathology offers numerous tools that can be composed to an effective and appropriate diagnosis system. The tools should collect data from all contributing information sources, standardize the collection and interpretation algorithms and grade the obtained diseases in appropriate disease levels that direct the available treatments. The coordination and standardization of the essential factors seem to be a main constraint of digital pathology implementation into routine pathology.
2. Kronqvist, P., et al., Management of uncertainty in breast cancer grading with Bayesian belief networks. Anal Quant Cytol Histol, 1995. 17(5): p. 300-8.
3. Lepor, H., et al., Quantitative morphometry of the adult human bladder. J Urol, 1992. 148(2 Pt 1): p. 414-7.
4. Mariuzzi, G.M. and Y.U. Collan, Some reflections on the history, and presence of quantitative pathology. Pathologica, 1995. 87(3): p. 215-20.
5. Mireskandari, M., et al., Teleconsultation in diagnostic pathology: experience from Iran and Germany with the use of two European telepathology servers. J Telemed Telecare, 2004. 10(2): p. 99-103.
6. Kayser, K., et al., AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis. Folia Histochem Cytobiol, 2009. 47(3): p. 355-61.
7. Kayser, K., et al., Combined analysis of tumor growth pattern and expression of endogenous lectins as a prognostic tool in primary testicular cancer and its lung metastases. Histol Histopathol, 2003. 18(3): p. 771-9.
8. Kayser, K., et al., Texture- and object-related automated information analysis in histological still images of various organs. Anal Quant Cytol Histol, 2008. 30(6): p. 323-35.
9. Kayser, K. and G. Kayser, Virtual Microscopy and Automated Diagnosis in Virtual Microscopy and Virtual Slides in Teaching, Diagnosis and Research, J. Gu and R. Ogilvie, Editors. 2005, Taylor Francis: Boca Raton.
10. Kayser, K., et al., How to measure diagnosis-associated information in virtual slides. Diagn Pathol, 2013. 6 Suppl 1: p. S9.
11. Kayser, K. and O. Hagemeyer, Stage related morphometry of sarcoid granulomas and inflammatory cell types in broncho-alveolar lavage. Anal Cell Pathol, 1991. 3(6): p. 335-42.
12. Kayser, K., et al., Preneoplasia-associated expression of calcyclin and of binding sites for synthetic blood group A/H trisaccharide--exposing neoglycoconjugates in human lung. Cancer Biochem Biophys, 1997. 15(4): p. 235-43.
13. Kayser, K., et al., Texture and object related image analysis in microscopic images. Diagnostic Pathology, 2015. 1.
14. Kayser, K. and H.J. Gabius, Graph theory and the entropy concept in histochemistry. Theoretical considerations, application in histopathology and the combination with receptor-specific approaches. Prog Histochem Cytochem, 1997. 32(2): p. 1-106.
15. Kayser, K. and H.J. Gabius, The application of thermodynamic principles to histochemical and morphometric tissue research: principles and practical outline with focus on the glycosciences. Cell Tissue Res, 1999. 296(3): p. 443-55.
16. Kayser, K., et al., Expression of endogenous lectins (galectins, receptors for ABH-epitopes) and the MIB-1 antigen in esophageal carcinomas and their syntactic structure analysis in relation to post-surgical tumor stage and lymph node involvement. Anticancer Res, 2001. 21(2B): p. 1439-44.
17. Kayser, K., C. Kremer, and M. Tacke, Integrated optical density and entropiefluss (current of entropy) in bronchial carcinoma. In Vivo, 1993. 7(4): p. 387-91.
18. Kayser, K., K. Kremer, and M. Tacke, [DNA and MST entropy and current of entropy. New parameters of tumor biological characterization]. Zentralbl Pathol, 1994. 139(6): p. 427-31.
19. Park, S., et al., The history of pathology informatics: A global perspective. J Pathol Inform, 2013. 4: p. 7.
20. Borkenfeld, S. and K. Kayser, Specificities of Electronic Publication in Medicine. Diagnostic Pathology, 2015. 1.
21. Kayser K, B.S., Carvalho R, Kayser G, How to create and implement diagnosis assistants in tissue-based diagnosis (surgical pathology)? Diagnostic Pathology, 2015. 1: in press.
22. Sharma H, Z.N., Lohmann S, Kayser K, Hellwich O, Hufnagl P, A review of graph-based methods for image analysis in digital histopathology. Diagnostic Pathology, 2015. 1:61.
23. Kayser, K., et al., Theory of sampling and its application in tissue based diagnosis. Diagn Pathol, 2009. 4: p. 6.
24. Kayser, K., Quantification of virtual slides: Approaches to analysis of content-based image information. J Pathol Inform, 2012. 2: p. 2.
25. Kayser, K., S. Borkenfeld, and G. Kayser, How to introduce virtual microscopy (VM) in routine diagnostic pathology: Constraints, ideas, and solutions. Analytical Cellular Pathology, 2012. 35: p. 3–10.
26. Bartels, P.H., D. Thompson, and R. Montironi, Knowledge-based image analysis in the precursors of prostatic adenocarcinoma. Eur Urol, 1996. 30(2): p. 234-42.
27. Bartels, P.H., J.E. Weber, and L. Duckstein, Machine learning in quantitative histopathology. Anal Quant Cytol Histol, 1988. 10(4): p. 299-306.
28. Bibbo, M., et al., Histometric features for the grading of prostatic carcinoma. Anal Quant Cytol Histol, 1991. 13(1): p. 61-8.
29. Hamilton, P.W., et al., Automated histometry in quantitative prostate pathology. Anal Quant Cytol Histol, 1998. 20(5): p. 443-60.
30. Kayser, K., et al., Digitized pathology: theory and experiences in automated tissue-based virtual diagnosis. Rom J Morphol Embryol, 2006. 47(1): p. 21-8.
31. Masseroli, M., et al., Design and validation of a new image analysis method for automatic quantification of interstitial fibrosis and glomerular morphometry. Lab Invest, 1998. 78(5): p. 511-22.
32. Montironi, R., et al., Prostatic intraepithelial neoplasia. Quantitation of the basal cell layer with machine vision system. Pathol Res Pract, 1995. 191(9): p. 917-23.
33. Thompson, D., et al., Image segmentation of cribriform gland tissue. Anal Quant Cytol Histol, 1995. 17(5): p. 314-22.
34. Thompson, D., et al., Knowledge-guided histometry of the basal cell layer in prostatic intraepithelial neoplasia. Anal Quant Cytol Histol, 1996. 18(2): p. 177-84.
35. Oger, M., et al., Automated region of interest retrieval and classification using spectral analysis. Diagn Pathol, 2008. 3 Suppl 1: p. S17.
36. Mattfeldt, T., U. Vogel, and H.W. Gottfried, [Three-dimensional spatial texture of adenocarcinoma of the prostate by a combination of stereology and digital image analysis]. Verh Dtsch Ges Pathol, 1993. 77: p. 73-7.
37. Kayser, K., Neighborhood Condition and Application of Syntactic Structure Analysis in Histo-Pathology. Acta Stereol, 1987. 6:(2): p. 373-384.
38. Kayser, K., Analytical Lung Pathology. 1992, Heidelberg, New York: Springer.
39. Kayser, K., et al., Application of attributed graphs in diagnostic pathology. Anal Quant Cytol Histol, 1996. 18(4): p. 286-92.
40. Voronoi, G., Nouvelles applications des parametres continus a la
theorie des formes quadratiques, deuxieme memoire: recherches sur les paralleloedres primitifs. J Reine Angew Math, 1902. 134: p. 188-287.
41. Voss, K. and H. Süsse, Praktische Bildverarbeitung. 1991: München, Wien: Carl Hanser Verlag.
42. van Diest, P.J., et al., Syntactic structure analysis. Pathologica, 1995. 87(3): p. 255-62.
43. Kayser, K. and H. Hoffgen, Pattern recognition in histopathology by orders of textures. Med Inform (Lond), 1984. 9(1): p. 55-9.
44. Kayser, K., et al., Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet. Diagn Pathol, 2006. 1: p. 10.
45. Kayser, K., et al., Interactive and automated application of virtual microscopy. Diagn Pathol, 2013. 6 Suppl 1: p. S10.
46. Kayser, K., et al., Image standardization in tissue-based diagnosis. Diagnostic Pathology, 2010. 5: p. S13.
47. Kayser, K., et al., Image standards in tissue-based diagnosis (diagnostic surgical pathology). Diagn Pathol, 2008. 3: p. 17.
48. Kayser, K., et al., How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology). Diagn Pathol, 2008. 3 Suppl 1: p. S11.
49. Bolender, R.P., Biological stereology: history, present state, future directions. Microsc Res Tech, 1992. 21(4): p. 255-61.
50. Della Mea, V., 25 years of telepathology research: a bibliometric analysis. Diagn Pathol, 2012. 6 Suppl 1: p. S26.
51. Kayser, K., et al., History and structures of telecommunication in pathology, focusing on open access platforms. Diagn Pathol, 2012. 6: p. 110.
52. Williams, B.H., et al., A national treasure goes online: the Armed Forces Institute of Pathology. MD Comput, 1998. 15(4): p. 260-5.
53. Park, S., et al., The history of pathology informatics: A global perspective. Journal of pathology informatics, 2013. 4.
54. Nap, M., R. Teunissen, and M. Pieters, A travel report of the implementation of virtual whole slide images in routine surgical pathology. Apmis, 2014. 120(4): p. 290-7.
55. Chiang, M.C., et al., 3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry. Neuroimage, 2007. 34(1): p. 44-60.
56. Debnath, A.K., Application of 3D-QSAR techniques in anti-HIV-1 drug design--an overview. Curr Pharm Des, 2005. 11(24): p. 3091-110.
57. Fuhrmann, R., A. Schnappauf, and P. Diedrich, [3-dimensional imaging diagnosis with a personal computer]. Fortschr Kieferorthop, 1993. 54(2): p. 58-63.
58. Stoyanov, D., I. Nedkov, and M. Ausloos, Retrieving true images through fine grid steps for enhancing the resolution beyond the classical limits: theory and simulations. J Microsc, 2007. 226(Pt 3): p. 270-83.
59. van Assen, H.C., et al., SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal, 2006. 10(2): p. 286-303.
60. Wearne, S.L., et al., New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience, 2005. 136(3): p. 661-80.
61. Weiskopf, D., T. Schafhitzel, and T. Ertl, Texture-based visualization of unsteady 3D flow by real-time advection and volumetric illumination. IEEE Trans Vis Comput Graph, 2007. 13(3): p. 569-82.
62. Conde, E., et al., The ALK translocation in advanced non-small-cell lung carcinomas: preapproval testing experience at a single cancer centre. Histopathology, 2013. 62(4): p. 609-16.
63. Xia, N., et al., Analysis of EGFR, EML4-ALK, KRAS, and c-MET mutations in Chinese lung adenocarcinoma patients. Exp Lung Res. 39(8): p. 328-35.
64. Pedersen, F., et al., HOPE-preservation of paraffin-embedded sputum samples--a new way of bioprofiling in COPD. Respir Med, 2013. 107(4): p. 587-95.
65. Vollmer, E. and T. Goldmann, Pathology on the edge of interdisciplinarity. A historical epitome. Rom J Morphol Embryol, 2012. 52(1 Suppl): p. 223-30.
66. Goldmann, T., D. Kahler, and E. Vollmer, Proteomics? Arch Pathol Lab Med, 2012. 136(3): p. 236-7; author reply 237.
67. Kayser, G., et al., Theory and implementation of an electronic, automated measurement system for images obtained from immunohistochemically stained slides. Anal Quant Cytol Histol, 2006. 28(1): p. 27-38.
68. Graschew, G., et al., Virtual hospital and digital medicine--why is the GRID needed? Stud Health Technol Inform, 2006. 120: p. 295-304.
69. Gortler, J., et al., Grid technology in tissue-based diagnosis: fundamentals and potential developments. Diagn Pathol, 2006. 1: p. 23.
70. Gilbertson, J.R., et al., Primary histologic diagnosis using automated whole slide imaging: a validation study. BMC Clin Pathol, 2006. 6: p. 4.
71. Ficsor, L., et al., Automated virtual microscopy of gastric biopsies. Cytometry B Clin Cytom, 2006. 70(6): p. 423-31.
72. Dee, F.R., Virtual microscopy for comparative pathology.
Toxicol Pathol, 2006. 34(7): p. 966-7.
73. Rogatko, A., T. Rebbeck, and S. Zacks, Risk prediction with linked markers: pedigree analysis. Am J Med Genet, 1995. 59(1): p. 24-32.
74. Tiseo, M., et al., ERCC1/BRCA1 expression and gene polymorphisms as prognostic and predictive factors in advanced NSCLC treated with or without cisplatin. Br J Cancer, 2013. 108(8): p. 1695-703.
75. Papadaki, C., et al., ERCC1 and BRAC1 mRNA expression levels in the primary tumor could predict the effectiveness of the second-line cisplatin-based chemotherapy in pretreated patients with metastatic non-small cell lung cancer. J Thorac Oncol, 2013. 7(4): p. 663-71.
76. Kayser, K., Introduction of virtual microscopy in routine surgical pathology--a hypothesis and personal view from Europe. Diagn Pathol, 2012. 7: p. 48.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
4. In case of virtual slide publication the authors agree to copy the article in a structural modified version to the journal's VS archive.