Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 239706, 10 pages
http://dx.doi.org/10.1155/2014/239706
Research Article

Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network

1Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
2Department of Medical Informatics, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
3Department of Histology, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
4Department of Pathology, University of Medicine and Pharmacy “Carol Davilla,” Bucharest, Bulevardul Eroii Sanitari 8, 050474 București, Romania
52nd Medical Department, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania
6Department of Surgery, University of Medicine and Pharmacy of Craiova, Petru Rares Street, No. 2, 200349 Craiova, Romania

Received 8 December 2013; Revised 10 March 2014; Accepted 25 March 2014; Published 16 June 2014

Academic Editor: Wei Mike Liu

Copyright © 2014 Dan Ionuț Gheonea et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. B. El-Serag and K. L. Rudolph, “Hepatocellular carcinoma: epidemiology and molecular carcinogenesis,” Gastroenterology, vol. 132, no. 7, pp. 2557–2576, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. M. A. Gomes, D. G. Priolli, J. G. Tralhão, and M. F. Botelho, “Hepatocellular carcinoma: epidemiology, biology, diagnosis, and therapies,” Revista da Associação Médica Brasileira, vol. 59, no. 5, pp. 514–524, 2013. View at Publisher · View at Google Scholar
  3. D. M. Parkin, F. Bray, J. Ferlay, and P. Pisani, “Global cancer statistics, 2002,” CA: A Cancer Journal for Clinicians, vol. 55, no. 2, pp. 74–108, 2005. View at Google Scholar · View at Scopus
  4. H. B. El-Serag, “Hepatocellular carcinoma,” The New England Journal of Medicine, vol. 365, no. 12, pp. 1118–1127, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Bruix and M. Sherman, “Management of hepatocellular carcinoma,” Hepatology, vol. 42, no. 5, pp. 1208–1236, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Bruix and M. Sherman, “Management of hepatocellular carcinoma: an update,” Hepatology, vol. 53, no. 3, pp. 1020–1022, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. S. F. Altekruse, K. A. McGlynn, and M. E. Reichman, “Hepatocellular carcinoma incidence, mortality, and survival trends in the United States from 1975 to 2005,” Journal of Clinical Oncology, vol. 27, no. 9, pp. 1485–1491, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. European Association for the Study of the Liver and European Organisation for Research and Treatment of Cancer, “EASL—EORTC clinical practice guidelines: management of hepatocellular carcinoma,” Journal of Hepatology, vol. 56, no. 4, pp. 908–943, 2012. View at Publisher · View at Google Scholar
  9. G. A. Losa, “The fractal geometry of life,” Rivista di Biologia, vol. 102, no. 1, pp. 29–60, 2009. View at Google Scholar · View at Scopus
  10. Y. Gousseau and F. Roueff, “Modeling occlusion and scaling in natural images,” Multiscale Modeling & Simulation, vol. 6, no. 1, pp. 105–134, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Tambasco, B. M. Costello, A. Kouznetsov, A. Yau, and A. M. Magliocco, “Quantifying the architectural complexity of microscopic images of histology specimens,” Micron, vol. 40, no. 4, pp. 486–494, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Mancardi, G. Varetto, E. Bucci, F. Maniero, and C. Guiot, “Fractal parameters and vascular networks: facts & artifacts,” Theoretical Biology and Medical Modelling, vol. 5, article 12, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Chauveau, D. Rousseau, P. Richard, and F. Chapeau-Blondeau, “Multifractal analysis of three-dimensional histogram from color images,” Chaos, Solitons & Fractals, vol. 43, no. 1–12, pp. 57–67, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. A. di Ieva, “Angioarchitectural morphometrics of brain tumors: are there any potential histopathological biomarkers?” Microvascular Research, vol. 80, no. 3, pp. 522–533, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Goutzanis, N. Papadogeorgakis, P. M. Pavlopoulos et al., “Nuclear fractal dimension as a prognostic factor in oral squamous cell carcinoma,” Oral Oncology, vol. 44, no. 4, pp. 345–353, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Delides, I. Panayiotides, A. Alegakis et al., “Fractal dimension as a prognostic factor for laryngeal carcinoma,” Anticancer Research, vol. 25, no. 3, pp. 2141–2144, 2005. View at Google Scholar · View at Scopus
  17. B. B. Mandelbrot, “Stochastic models for the Earth's relief, the shape and the fractal dimension of the coastlines, and the number area rule for islands,” Proceedings of the National Academy of Sciences of the United States of America, vol. 72, no. 10, pp. 3825–3828, 1975. View at Google Scholar · View at Scopus
  18. S. S. Cross, “Fractals in pathology,” The Journal of Pathology, vol. 182, no. 1, pp. 1–8, 1997. View at Google Scholar
  19. P. Dey, “Basic principles and applications of fractal geometry in pathology: a review,” Analytical and Quantitative Cytology and Histology, vol. 27, no. 5, pp. 284–290, 2005. View at Google Scholar · View at Scopus
  20. P. J. Lisboa and A. F. G. Taktak, “The use of artificial neural networks in decision support in cancer: a systematic review,” Neural Networks, vol. 19, no. 4, pp. 408–415, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Grossi, A. Mancini, and M. Buscema, “International experience on the use of artificial neural networks in gastroenterology,” Digestive and Liver Disease, vol. 39, no. 3, pp. 278–285, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Cucchetti, F. Piscaglia, A. D. Grigioni et al., “Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study,” Journal of Hepatology, vol. 52, no. 6, pp. 880–888, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Frize, C. M. Ennett, M. Stevenson, and H. C. E. Trigg, “Clinical decision support systems for intensive care units: using artificial neural networks,” Medical Engineering and Physics, vol. 23, no. 3, pp. 217–225, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Jiang, P. Trundle, and J. Ren, “Medical image analysis with artificial neural networks,” Computerized Medical Imaging and Graphics, vol. 34, no. 8, pp. 617–631, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. C. T. Streba, D. Pirici, C. C. Vere, L. Mogoantǎ, C. Violeta, and I. Rogoveanu, “Fractal analysis differentiation of nuclear and vascular patterns in hepatocellular carcinomas and hepatic metastasis,” Romanian Journal of Morphology and Embryology, vol. 52, no. 3, pp. 845–854, 2011. View at Google Scholar · View at Scopus
  26. J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977. View at Google Scholar · View at Scopus
  27. C. T. Streba, M. Ionescu, D. I. Gheonea et al., “Using contrast-enhanced ultrasonography time-intensity curves as classifiers in neural network diagnosis of focal liver lesions,” World Journal of Gastroenterology, vol. 18, no. 32, pp. 4427–4434, 2012. View at Publisher · View at Google Scholar
  28. C. T. Streba, L. Sandulescu, C. C. Vere, L. Streba, and I. Rogoveanu, “Computer aided differentiation model for automatic classification of focal liver lesions based on contrast-enhanced ultrasonography (CEUS) time intensity curve (TIC) analysis,” Journal of Hepatology, vol. 56, supplement 2, p. S296, 2012. View at Publisher · View at Google Scholar
  29. C. T. Streba, C. C. Vere, L. D. Sandulescu et al., “Comparison of different machine learning systems in classifying focal liver lesions based on clinical and dynamic imaging data,” Gastroenterology, vol. 144, supplement 1, no. 5, pp. S1038–S1039, 2013. View at Google Scholar
  30. C. T. Streba, D. I. Gheonea, L. Sandulescu et al., “Using contrast-enhanced ultrasonography (CEUS) time-intensity curves (TICs) as classifiers in neural network diagnosis of focal liver lesions,” Gastroenterology, vol. 142, supplement 1, no. 5, p. S-1004, 2012. View at Google Scholar
  31. D. Pirici, L. Mogoantǎ, O. Mǎrgǎritescu, I. Pirici, V. Tudoricǎ, and M. Coconu, “Fractal analysis of astrocytes in stroke and dementia,” Romanian Journal of Morphology and Embryology, vol. 50, no. 3, pp. 381–390, 2008. View at Google Scholar · View at Scopus
  32. F. Moal, D. Chappard, J. Wang et al., “Fractal dimension can distinguish models and pharmacologic changes in liver fibrosis in rats,” Hepatology, vol. 36, no. 4, pp. 840–849, 2002. View at Publisher · View at Google Scholar · View at Scopus
  33. F. Grizzi, C. Russo, B. Franceschini et al., “Sampling variablity of computer-aided fractal-corrected measures of liver fibrosis in needle biopsy specimens,” World Journal of Gastroenterology, vol. 12, no. 47, pp. 7660–7665, 2006. View at Google Scholar · View at Scopus
  34. N. Dioguardi, F. Grizzi, B. Franceschini, P. Bossi, and C. Russo, “Liver fibrosis and tissue architectural change measurement using fractal-rectified metrics and Hurst's exponent,” World Journal of Gastroenterology, vol. 12, no. 14, pp. 2187–2194, 2006. View at Google Scholar · View at Scopus
  35. N. Dioguardi, B. Franceschini, G. Aletti, C. Russo, and F. Grizzi, “Fractal dimension rectified meter for quantification of liver fibrosis and other irregular microscopic objects,” Analytical and Quantitative Cytology and Histology, vol. 25, no. 6, pp. 312–320, 2003. View at Google Scholar · View at Scopus
  36. K. Sasaki, M. Matsuda, Y. Ohkura et al., “In hepatocellular carcinomas, any proportion of poorly differentiated components is associated with poor prognosis after hepatectomy,” World Journal of Surgery, vol. 38, no. 5, pp. 1147–1153, 2014. View at Publisher · View at Google Scholar
  37. X. Zhang, M. Kanematsu, H. Fujita et al., “Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging,” Radiological Physics and Technology, vol. 2, no. 2, pp. 175–182, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Guo, T. Qiu, J. Bian, W. Kang, and L. Zhang, “A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier,” Computerized Medical Imaging and Graphics, vol. 33, no. 8, pp. 588–592, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. J.-S. Chiu, Y.-F. Wang, Y.-C. Su, L.-H. Wei, J.-G. Liao, and Y.-C. Li, “Artificial neural network to predict skeletal metastasis in patients with prostate cancer,” Journal of Medical Systems, vol. 33, no. 2, pp. 91–100, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. V. E. Markaki, P. A. Asvestas, and G. K. Matsopoulos, “Application of Kohonen network for automatic point correspondence in 2D medical images,” Computers in Biology and Medicine, vol. 39, no. 7, pp. 630–645, 2009. View at Publisher · View at Google Scholar · View at Scopus