Biochemical imaging of normal, adenoma, and colorectal adenocarcinoma tissues by Fourier transform infrared spectroscopy (FTIR) and morphological correlation by histopathological analysis: preliminary results
Piva, Juliana Aparecida de Almeida Chaves; Silva, João Lucas Rangel; Raniero, Leandro José; Lima, Carmen Silvia Passos; Arisawa, Emilia Ângela L.; Oliveira, Cristiane de; Canevari, Renata de Azevedo; Ferreira, Juliana; Martin, Airton Abrahão
http://dx.doi.org/10.1590/2446-4740.0321
Res. Biomed. Eng., vol.31, n1, p.10-18, 2015
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Views: 903
Abstract
.Introduction: The colorectal cancer is a major health problem worldwide. Histology is considered the gold standard for differential diagnosis. However, it depends on the observer’s experience, which can lead to discrepancies and poor results. Spectroscopic imaging by Fourier transform infrared (FTIR) is a technique that may be able to improve the diagnosis, because it is based on biochemical differences of the structural constituents of tissue. Therefore, the main goal of this study was to explore the use of FTIR imaging technique in normal colon tissue, colorectal adenoma, and adenocarcinoma in order to correlate their morphological structures with their biochemical imaging. Methods: Samples were collected from normal (n = 4), adenoma (n = 4), and adenocarcinoma human colorectal tissue (n = 4) from patients undergoing colonoscopy or surgical resection of colon lesions. The samples were sectioned with a cryostat in sequential sections; the fi rst slice was placed on CaF2 slide and the second slice was placed on glass slide for histological analysis (HE staining). The cluster analyses were performed by the software Cytospec (1.4.02). Results: In normal samples, biochemical analysis classifi ed six different structures, namely the lamina propria of mucous glands (epithelial cells and goblet cells), central lumen of the gland, mucin, and conjunctive tissue. In samples with adenoma and adenocarcinoma, altered regions could also be identifi ed with high sensitivity and specifi city. Conclusion: The results of this study demonstrate the potential and viability of using infrared spectroscopy to identify and classify colorectal tissues.
Keywords
Infrared imaging, Artifi cial neural network, Human colon cancer, Biochemical correlation, Tissue classifi cation, FTIR.
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Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks (InICNN-93); 1993; San Francisco, CA. p. 586-91.
Sala O. Fundamentos da espectroscopia Raman e no infravermelho. 2. ed. Unesp; 2008.
Schultz CP, Mantsch HH. Biochemical imaging and 2D classification of keratin pearl structures in oral squamous cell carcinoma. Cellular and Molecular Biology 1998; 44(1):203-10. PMid:9551651.
Stone S. Raman spectroscopy for identification of epithelial cancers. Faraday Discussions 2004; 126:141-57.
Torres JRN, Arcieri JS, Teixeira FR. Aspectos epidemiológicos dos pólipos e lesões plano-elevadas colorretais. Revista Brasileira de Coloproctologia 2011; 30(4):419-29.
Udelhoven T, Naumann D, Schmitt J. Development of a hierarchical classification system with artificial neural networks and FT-IR specta for the identification of bacteria. Applied Spectroscopy 2000; 54(10):1471-9. http://dx.doi.org/10.1366/0003702001948619.
Winawer SJ. The multidisciplinary management of gastrointestinal cancer: colorectal cancer screening. Best Practice & Research. Clinical Gastroenterology 2007; 21(6):1031-48. http://dx.doi.org/10.1016/j.bpg.2007.09.004.PMid:18070702
Zhang L, Small GW, Haka AS, Kidder LH, Lewis EN. Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks. Applied Spectroscopy 2003; 57(1):14-22. http://dx.doi.org/10.1366/000370203321165151.PMid:14610931
American Cancer Society - ACS. Detailed guide: colon and rectum cancer. Atlanta: American Cancer Society; 2013.
Andrade PO, Bitar RA, Yassoyama K, Martinho H, Santo AM, Bruno PM, Martin AA. Study of normal colorectal tissue by FT-Raman spectroscopy. Analytical and Bioanalytical Chemistry 2007; 387(5):1643-8. http://dx.doi.org/10.1007/s00216-006-0819-1. PMid:17031621
Argov S. Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients. Journal of Biomedical Optics 2002; 7(2):1.
Auda G, Kamel M. Modular network classifiers: a comparative study. Journal of Intelligent & Robotic Systems 1998; 21(2):117-29. http://dx.doi.org/10.1023/A:1007925203918.
Bird B, Miljkovic M, Romeo MJ, Smith J, Stone N, George MW, Diem M. Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology. BMC Clinical Pathology 2008; 8(1):8. http://dx.doi.org/10.1186/1472-6890-8-8. PMid:18759967
Cohenford MA, Lim S, Brown C, Chaudhry MA, Sigdel S, Beckelhimer E, Rigas B. FT-IR microspectroscopy of mouse colon tissues: insight into the chemistry of carcinogenesis and diagnostic potential. American Journal of Pathology 2012; 181(6):1961-8. http://dx.doi.org/10.1016/j.ajpath.2012.08.039. PMid:23063512
Colagar AH, Chaichi MJ, Khadjvand T. Fourier transform infrared microspectroscopy as a diagnostic tool for distinguishing between normal and malignant human gastric tissue. Journal of Biosciences 2011; 36(4):669-77. http://dx.doi.org/10.1007/s12038-011-9090-5. PMid:21857113
CytoSpec. CytoSpec: an application for hyperspectral imaging. 2010. Available from: http://www.cytospec.com/index.html.
Di Giambattista L, Pozzi D, Grimaldi P, Gaudenzi S, Morrone S, Castellano AC. New marker of tumor cell death revealed by ATR-FTIR spectroscopy. Analytical and Bioanalytical Chemistry 2011; 399(8):2771-8. http://dx.doi.org/10.1007/s00216-011-4654-7. PMid:21249341
Díaz FR, López FFB. Bioestatística. São Paulo: Thomson; 2007. p. 57.
Diem M, Chiriboga L, Yee H. Infrared spectroscopy of human cells and tissue. VIII. Strategies for analysis of infrared tissue mapping data and applications to liver tissue. Biopolymers 2000; 57(5):282-90. http://dx.doi.org/10.1002/1097-0282(2000)57:5<282::AID-BIP50>3.0.CO;2-R. PMid:10958320
Fernandez DC, Bhargava R, Hewitt SM, Levin IW. Infrared spectroscopic imaging for histopathologic recognition. Nature Biotechnology 2005; 23(4):469-74. http://dx.doi.org/10.1038/nbt1080. PMid:15793574
Jackson M, Ramjiawan B, Hewko M, Mantsch HH. Infrared microscopic functional group mapping and spectral clustering analysis of hypercholesterolemic rabbit liver. Cellular and Molecular Biology 1998; 44(1):89-98. PMid:9551641.
Kallenbach-Thieltges A, Großerüschkamp F, Mosig A, Diem M, Tannapfel A, Gerwert K. Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. Journal of Biophotonics 2013; 6(1):88-100. http://dx.doi.org/10.1002/jbio.201200132. PMid:23225612
Karsa LV, Lignini TA, Patnick J, Lambert R, Sauvaget C. The dimensions of the CRC problem. Best Practice & Research. Clinical Gastroenterology 2010; 24(4):381-96. http://dx.doi.org/10.1016/j.bpg.2010.06.004. PMid:20833343
Krafft C, Codrich D, Pelizzo G, Sergo V. Raman and FTIR microscopic imaging of colon tissue: a comparative study. Journal of Biophotonics. 2008; 1(2):154-69. http://dx.doi.org/10.1002/jbio.200710005. PMid:19343646
Lasch P, Naumann D. FT-IR microspectroscopic imaging of human carcinoma thin sections based on pattern recognition techniques. Cellular and Molecular Biology 1998; 44(1):189-202. PMid:9551650.
Lasch P, Haensch W, Lewis EN, Kidder LH, Naumann D. Characterization of colorectal adenocarcinoma sections by spatially resolved FT-IR microspectroscopy. Applied Spectroscopy 2002; 56(1):1-9. http://dx.doi.org/10.1366/0003702021954322.
Lasch P, Haensch W, Naumann D, Diem M. Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. Biochimica et Biophysica Acta 2004; 1688(2):176-86. http://dx.doi.org/10.1016/j.bbadis.2003.12.006. PMid:14990348
Lasch P, Diem M, Hänsch W, Naumann D. Artificial neural networks as supervised techniques for FT-IR microspectroscopic imaging. Journal of Chemometrics 2006; 20(5):209-20. http://dx.doi.org/10.1002/cem.993. PMid:19960119
Mansfield JR, McIntosh LM, Crowson AN, Mantsch HH, Jackson M. A LDA-guided search engine for the non-subjective analysis of infrared microscopic maps. Applied Spectroscopy 1999; 53(11):1323-30. http://dx.doi.org/10.1366/0003702991945920.
Mansfield JR, Sowa MG, Scarth GB, Somorjai RL, Mantsch HH. Analysis of spectroscopic imaging data by fuzzy C-means clustering. Analytical Chemistry 1997; 69(16):3370-4. http://dx.doi.org/10.1021/ac970206r.
Marques de Sá JP. Pattern recognition: concepts, methods and applications. Berlin: Springer-Verlag; 2001. http://dx.doi.org/10.1007/978-3-642-56651-6.
Mostaço-Guidolin LB, Bachmann L. bachmann L. Application of FTIR Spectroscopy for identification of blood and leucemia biomarkers: A Review over the past 15 years. Applied Spectroscopy Reviews 2011; 46(5):388-404. http://dx.doi.org/10.1080/05704928.2011.565534.
NeuroDeveloper. Synthon’s NeuroDeveloper 2.5TM: software designed for the classification of spectroscopic data with artificial neural networks. 2010. Available from: http://www.synthon-analytics.de/synthon_01_content.html.
Petibois C, Déléris G. Chemical mapping of tumor progression by FT-IR imaging: towards molecular histopathology. Trends in Biotechnology 2006; 24(10):455-62. http://dx.doi.org/10.1016/j.tibtech.2006.08.005. PMid:16935373
Piva JAAC, Silva JLR, Raniero L, Martin AA, Bohr HG, Jalkanem KJ. Overview of the use of theory to understand infrared and Raman spectra and images of biomolecules: colorectal cancer as an example. Theoretical Chemistry accounts. Theory, Computation, and Modeling. 2011; 130(4-6):1261-73.
Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks (InICNN-93); 1993; San Francisco, CA. p. 586-91.
Sala O. Fundamentos da espectroscopia Raman e no infravermelho. 2. ed. Unesp; 2008.
Schultz CP, Mantsch HH. Biochemical imaging and 2D classification of keratin pearl structures in oral squamous cell carcinoma. Cellular and Molecular Biology 1998; 44(1):203-10. PMid:9551651.
Stone S. Raman spectroscopy for identification of epithelial cancers. Faraday Discussions 2004; 126:141-57.
Torres JRN, Arcieri JS, Teixeira FR. Aspectos epidemiológicos dos pólipos e lesões plano-elevadas colorretais. Revista Brasileira de Coloproctologia 2011; 30(4):419-29.
Udelhoven T, Naumann D, Schmitt J. Development of a hierarchical classification system with artificial neural networks and FT-IR specta for the identification of bacteria. Applied Spectroscopy 2000; 54(10):1471-9. http://dx.doi.org/10.1366/0003702001948619.
Winawer SJ. The multidisciplinary management of gastrointestinal cancer: colorectal cancer screening. Best Practice & Research. Clinical Gastroenterology 2007; 21(6):1031-48. http://dx.doi.org/10.1016/j.bpg.2007.09.004.PMid:18070702
Zhang L, Small GW, Haka AS, Kidder LH, Lewis EN. Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks. Applied Spectroscopy 2003; 57(1):14-22. http://dx.doi.org/10.1366/000370203321165151.PMid:14610931
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