Skip to main content

Advertisement

SpringerLink
Log in

Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Colorectal carcinoma (CRC) as a major health problem in industrialized countries is highly preventable and can be successfully treated in the early stages. However, incidence and mortality of CRC has increased over the last two decades. The reason could be that the current recommended options for screening are costly, unpleasant for patients, have low sensitivity and poor accessibility for screening. These reasons provide a strong rationale for the development of a new method. Opto-magnetic imaging spectroscopy (OMIS) as a new imaging method for the characterisation of various materials, including human tissues, is based on light-matter interaction, using a Poincare sphere for light properties and a Bloch sphere for electron properties, and allows the detection of biophysical characteristics within human tissue samples. Compared with histopathology examination, the OMIS method achieved an accuracy of 92.59% using Multilayer Perceptron Neural Network as a classifier, and 89.87% using Naïve-Bayes, respectively. The obtained results, based on the investigation of 316 samples, both tumour and normal mucosa (162 cancer cases), strongly suggest that the new non-invasive OMIS method might be used for tissue characterization ex vivo to discriminate between the healthy and carcinoma state of the colon. However, it opens up the possibility of using the same method in in vivo studies to assist physicians in targeting biopsies of colorectal tissue.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Opto-magnetic imaging spectroscopy.

  2. MLP—multilayer perceptron neural network.

  3. TNM Classification of Malignant Tumours.

  4. Normalized arbitrary unites.

  5. Fourier Transformed Infrared spectroscopy.

References

  1. Cancer/The problem. Retrieved November 13, 2017 http://www.who.int/mediacentre/factsheets/fs297/en/.

  2. National cancer institute/Cancer statistics. Retrieved November 13, 2017 https://www.cancer.gov/about-cancer/understanding/statistics.

  3. Colorectal cancer statistics. 2017. Retrieved November 13, 2017 http://pressroom.cancer.org/CRCstats2017.

  4. Sali, L., et al. (2013). Screening for colorectal cancer with FOBT, virtual colonoscopy and optical colonoscopy: study protocol for a randomized controlled trial in the Florence district (SAVE study). Trials. https://doi.org/10.1186/1745-6215-14-74.

    Article  Google Scholar 

  5. Franz, M., Scholz, M., Henze, I., Röcki, S., & Gomez, L. (2013). Detection of colon polyps by a novel, polymer pattern-based full blood test. Journal of Translation Medicine. https://doi.org/10.1186/1479-5876-11-278.

    Article  Google Scholar 

  6. Zhan, T., Hielscher, T., Hahn, F., Hauf, C., Betge, J., Ebert, M. P., et al. (2016). Risk factors for local recurrence of large, flat colorectal polyps after endoscopic mucosal resection. Digestion, 93(4), 311–317.

    Article  Google Scholar 

  7. Koruga, D., Tomic, A. (2009). System and method for analysis of light-matter interaction based on spectral convolution. US Patent Pub. No. 2009/0245603.

  8. Coey, J. M. D. (2015). Magnetism and magnetic materials. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  9. Lakshminarayanan, V., Calvo, M. L., & Alieva, T. (2013). Mathematical optics: Classical, quantum, and computational methods. Boca Raton, FL: CRC Press Taylor & Francis Group.

    MATH  Google Scholar 

  10. Koruga, D. (2017). Hyperpolarized light: Fundamentals of nanobiomedical photonics. Belgrade: Zepter World Book.

    Google Scholar 

  11. Alonso, M., & Finn, E. J. (1992). Physics. New York: Addison-Wesley Publishing Company.

    Google Scholar 

  12. Malacara, D. (2011). Color vision and colorimetry: Theory and applications (2nd ed.). Washington, DC: SPIE Press.

    Book  Google Scholar 

  13. Rex, D. K. (2000). Colon tumours and colonoscopy. Endoscopy, 32(11), 833–874.

    Article  Google Scholar 

  14. Matija, L., Jeftic, B., Nikolic, G., Dragicevic, A., Mileusnic, I., Muncan, J., et al. (2014). Nanophysical approach to diagnosis of epithelial tissue using opto-magnetic imaging spectroscopy. In A. Seifalian, A. Mel, & D. M. Kalaskar (Eds.), Nanomedicine (pp. 156–186). London: One Central Press.

    Google Scholar 

  15. Koruga, D., Tomic, A. (2009). Method and algorithm for analysis of light-matter interaction based on spectral convolution. US Pat. App. No. 61/061,852, 2008, PCT/US2009/030347, Publication No: WO/2009/089292.

  16. Koruga, Đ., Miljković, S., Ribar, S., Matija, L., & Kojić, D. (2010). Water hydrogen bonds study by opto-magnetic fingerprint technique. Acta Physica Polonica A, 117(5), 777–781.

    Article  Google Scholar 

  17. Papić-Obradović, M., Kojić, D., & Matija, L. (2010). Opto-magnetic method for Epstein–Barr virus and cytomegalovirus detection in blood plasma samples. Acta Physica Polonica A, 117(5), 782–785.

    Article  Google Scholar 

  18. Dragicevic, A., Krivokapic, Z., Dimitrijevic, I., Markovic, V., Matija, L., & Koruga, D. (2015). Ex vivo preclinical study of colon cancer using opto-magnetic imaging spectroscopy and dual speed spinner magnetometer. European Journal of Cancer, 51(3), S130–S131.

    Article  Google Scholar 

  19. Koruga, Đ., Bandić, J., Janjić, G., Lalović, Č., Munćan, J., & Dobrosavljević-Vukojević, D. (2012). Epidermal layers’ characterisation by opto-magnetic spectroscopy based on digital image of skin. Acta Phisica Polonica A, 121(3), 606–610.

    Article  Google Scholar 

  20. Jeftić, B., Papic-Obradović, M., Munćan, J., Matija, L., & Koruga, Đ. (2017). Optomagnetic imaging spectroscopy application in cervical dysplasia and cancer detection: Comparation of stained 10 and unstained papanicolaou smears. Journal of Medical and Biological Engineering. https://doi.org/10.1007/s40846-017-0255-z.

    Article  Google Scholar 

  21. Papic-Obradovic, M. (2012). Early diagnostics of epithelial tissue cancer (in Serbian). Belgrade: Don Vas.

    Google Scholar 

  22. Papic-Obradovic, M., Jeftic, B., Dragicevic, A., Muncan, J., Matija, L., & Koruga, D. (2015). Optomagnetic Imaging Spectroscopy in characterisation of cervical tissue and cancer detection using unstained sample approach. European Journal of Cancer, 51(Supplement 3), S130.

    Article  Google Scholar 

  23. Stamenković, D., Kojić, D., Matija, L., Miljković, Z., & Babić, B. (2010). Physical properties of contact lenses characterized by scanning probe microscopy and optomagnetic fingerprint. International Journal of Modern Physics B, 24, 825–834.

    Article  Google Scholar 

  24. Iyer, R., Menon, V., Buice, M., Koch, C., & Mihalas, S. (2013). The influence of synaptic weight distribution on neuronal population dynamics. PLoS Computational Biology, 9(10), e1003248.

    Article  Google Scholar 

  25. Ratanamahatana, C., & Gunopulos, D. (2003). Feature selection for the naïve bayesian classifier using decision trees. Applied Artificial Intelligence, 17(5–6), 475–487.

    Article  Google Scholar 

  26. Wang, L.-M., Li, X.-L., Cao, C.-H., & Yuan, S.-M. (2006). Combining decision tree and Naïve Bayes for classification. Knowledge-Based Systems, 19(7), 511–515.

    Article  Google Scholar 

  27. Muralidharan, V., & Sugumaran, V. (2012). A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), 2023–2029.

    Article  Google Scholar 

  28. Zhang, H. (2004). The Optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004, AAAI Press.

  29. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103–130.

    Article  MATH  Google Scholar 

  30. Mackanos, M. A., & Contag, C. H. (2010). Fiber-optic probes enable cancer detection with FTIR spectroscopy. Trends in Biotechnology, 28(6), 317–323.

    Article  Google Scholar 

  31. Tuchin, V. V. (2005). Optical clearing of tissues and blood. Washington, DC: SPIE Press.

    Book  Google Scholar 

  32. Wiesner, W., Mortelé, K. J., Ji, H., & Ros, P. R. (2002). Normal colonic wall thickness at CT and its relation to colonic distension. Journal of Computer Assisted Tomography, 26(1), 102–106.

    Article  Google Scholar 

  33. Atanackovic, M., Bacetić, D., Begić-Janeva, G., Boričić, I., Brašanac, D., Cvetković-Dožić, D., et al. (2003). Patologija. Beograd: Medicinski fakultet Univerziteta u Beogradu, Katedra za patološku anatoniju.

    Google Scholar 

  34. Edge, S. B., Byrd, D. R., Compton, C. C., Fritz, A. G., Greene, F. L., & Trotti, A. (2010). AJCC cancer staging manual (7th ed.). New York: Springer.

    Google Scholar 

  35. Hecht-Nielsen, R. (1989). Neurocomputing. Boston, MA: Addison-Wesley Longman Publishing Co.

    Google Scholar 

  36. Recursive Feature Elimination. Retrieved November 21, 2017 http://topepo.github.io/caret/recursive-feature-elimination.html.

  37. Xie, Z. X., Hu, Q. H., & Yu, D. R. (2006). Improved feature selection algorithm based on SVM and correlation. In J. Wang, Z. Yi, J. M. Zurada, B. L. Lu, & H. Yin (Eds.), Advances in neural networks-ISNN 2006. Berlin: Springer.

    Google Scholar 

  38. Fawcett, T., & Provost, F. (1996). Combining data mining and machine learning for effective user profiling. In Simoudis, E., Han, J., & Fayyad, U. (Eds.), Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (pp. 8–13). Menlo Park, CA: AAAI Press. Provost et al. 1998.

  39. Fawcett, T., & Provost, F. (1997). Adaptive fraud detection. Data Mining and Knowledge Discovery, 1(3), 291–316.

    Article  Google Scholar 

  40. Winawer, S. J., Zauber, A. G., Ho, M. H., et al. (1993). The National Polyp Study Workgroup. Prevention of colorectal cancer by colonoscopy polypectomy. New England Journal of Medicine, 329(27), 1977–1981.

    Article  Google Scholar 

  41. American Cancer Society. Cancer Facts & Figures, 2010. American Cancer Society; Retrieved April 26, 2016 http://www.cancer.org/Research/CancerFactsFigures/index.

  42. Krivokapić, Z. (2012). Karcinom rektuma. Zavod za Udzbenike: Beograd.

    Google Scholar 

  43. Mamazza, J., & Gordon, P. H. (1982). The changing distribution of large intestinal cancer. Diseases of the Colon and Rectum, 25, 558–562.

    Article  Google Scholar 

  44. Endocrine Tumour-Risk Factors-Cncer.net. Retrieved May 27, 2016 http://www.cancer.net/node/19207.

  45. Rex, D. K., Johnson, D. A., Lieberman, D. A., Burt, R. W., & Sonnenberg, A. (2000). Colorectal cancer prevention 2000: Screening recommendations of the American College of Gastroenterology. American College of Gastroenterology. American Journal of Gastroenterology, 95(4), 868–877.

    Google Scholar 

  46. Pitris, C., Jesser, C., Boppart, S. A., Stamper, D., Brezinski, M. E., & Fujimoto, J. G. (2000). Feasibility of optical coherence tomography for high-resolution imaging of human gastrointestinal tract malignancies. Journal of Gastroenterology, 35(2), 87–92.

    Article  Google Scholar 

  47. Kong, K., Kendall, C., Stone, N., & Notingher, I. (2015). Raman spectroscopy for medical diagnostics: From in vitro biofluid assays to in vivo cancer detection. Advanced Drug Delivery Reviews, 89, 121–134.

    Article  Google Scholar 

  48. Lasch, P., Haensch, W., Naumann, D., & Diem, M. (2004). Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. Biochimica et Biophysica Acta (BBA), 1688(2), 176–186.

    Article  Google Scholar 

  49. Li, Q. B., Xu, Z., Zhang, N. W., Zhang, L., Wang, F., Yang, L. M., et al. (2005). In vivo and in situ detection of colorectal cancer using Fourier transform infrared spectroscopy. World Journal of Gastroenterology, 11(3), 327–330.

    Article  Google Scholar 

  50. Mavarani, L., Petersen, D., El-Mashtoly, S. F., Mosig, A., Tannapfel, A., Kötting, C., et al. (2013). Spectral histopathology of colon cancer tissue sections by Raman imaging with 532 nm excitation provides label free annotation of lymphocytes, erythrocytes and proliferating nuclei of cancer cells. Analyst, 138(14), 4035–4039.

    Article  Google Scholar 

  51. Shim, M. G., Song, L. M. W. K., Marcon, N. E., & Wilson, B. C. (2000). In vivo near-infrared raman spectroscopy: Demonstration of feasibility during clinical gastrointestinal endoscopy. Photochemistry and Photobiology, 72(1), 146–150.

    Google Scholar 

  52. Molckovsky, A., Song, L. M. W. K., Shim, M. G., Marcon, N. E., & Wilson, B. C. (2003). Diagnostic potential of near-infrared Raman spectroscopy in the colon: Differentiating adenomatous from hyperplastic polyps. Gastrointestinal Endoscopy, 57(3), 396–402.

    Article  Google Scholar 

  53. Widjaja, E., Zheng, W., & Huang, Z. (2008). Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines. International Journal of Oncology, 32(3), 653–662.

    Google Scholar 

  54. Short, M. A., Tai, I. T., Owen, D., & Zeng, H. (2013). Using high frequency Raman spectra for colonic neoplasia detection. Optics Express, 21(4), 5025–5034.

    Article  Google Scholar 

  55. Argov, S., Ramesh, J., Salman, A., et al. (2002). Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients. Journal of Biomeical Optics, 7(2), 248–254.

    Article  Google Scholar 

  56. Swartling, J., Dam, J. S., & Andersson-Engels, S. (2003). Comparison of spatially and temporally resolved diffuse-reflectance measurement systems for determination of biomedical properties. Applied Optics, 42(22), 4612–4620.

    Article  Google Scholar 

  57. Mourant, J. R., Hielscher, A. H., Eick, A. A., Johnson, T. M., & Freyer, J. P. (1998). Evidence of intrinsic differences in the light scattering properties of tumorigenic and nontumorigenic cells. Cancer Cytopathology, 84(6), 366–374.

    Article  Google Scholar 

  58. Hidovic-Rowe, D., & Claridge, E. (2005). Modelling and validation of spectral reflectance for the colon. Physics in Medicine & Biology, 50, 1071–1093.

    Article  Google Scholar 

  59. Old, O. J., Fullwood, L. M., Scott, R., Lloyd, G. R., Almond, L. M., Shepherd, N. A., et al. (2014). Vibrational spectroscopy for cancer diagnostics. Analytical Methods, 6(12), 3901–3917.

    Article  Google Scholar 

  60. Kallenbach-Thieltges, A., Großerüschkamp, F., Mosig, A., Diem, M., Tannapfel, A., & Gerwert, K. (2013). Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. Journal of Biophotonics, 6(1), 88–100.

    Article  Google Scholar 

  61. Krafft, C., Ramoji, A. A., Bielecki, C., Vogler, N., Meyer, T., Akimov, D., et al. (2009). A comparative Raman and CARS imaging study of colon tissue. Journal of Biophotonics, 2(5), 303–312.

    Article  Google Scholar 

  62. Wei, H., Xing, D., Wu, G., Gu, H., Jin, Y., & Li, X.-Y. (2005). Differences in optical properties between healthy and pathological human colon tissues using a Ti:sapphire laser: An in vitro study using the Monte Carlo inversion technique. Journal of Biomedical Optics. https://doi.org/10.1117/1.1990125.

    Article  Google Scholar 

  63. Waterhouse, B. R., & Farmery, A. D. (2012). The organization and composition of body fluids. Anaesthesia and intensive care medicine, 13(12), 603–608.

    Article  Google Scholar 

Download references

Acknowledgments

The research is supported by the Ministry of Education, Science and Technological Development, project III41006.

Funding

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.

Author information

Authors and Affiliations

  1. Department for Biomedical Engineering, Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, Belgrade, 11120, Serbia

    Aleksandra Dragicevic, Lidija Matija & Djuro Koruga

  2. School of Medicine, First Surgical Hospital, Clinical Centre of Belgrade, University of Belgrade, Dr Koste Todorovica 6, Belgrade, 11000, Serbia

    Zoran Krivokapic & Ivan Dimitrijevic

  3. “Ecole Polytechnique Federale de Lausanne”, Research institute/University in Lausanne, Route Cantonale, 1015, Lausanne, Switzerland

    Marko Baros

Authors
  1. Aleksandra Dragicevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lidija Matija
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zoran Krivokapic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Dimitrijevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marko Baros
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Djuro Koruga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Djuro Koruga.

Ethics declarations

Ethical Approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dragicevic, A., Matija, L., Krivokapic, Z. et al. Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy. J. Med. Biol. Eng. 39, 367–380 (2019). https://doi.org/10.1007/s40846-018-0414-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-018-0414-x

Keywords

JEL Classification

Search

Navigation