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Data Mining for Cancer Biomarkers with Raman Spectroscopy

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Book cover Data Mining for Biomarker Discovery

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 65))

Abstract

Raman spectroscopy has the potential to play an important role in the diagnosis and treatment of cancer as a unique type of biomarker technology. Raman spectra can provide a collective picture of the overall composition of biological samples as well as highly sensitive, targeting of specific biomolecular moieties depending upon the application. In the field of Oncology, Raman Spectroscopy can help in the identification of biomarkers for use in drug discovery, cancer-risk assessment, histopathology, and in vivo clinical applications. Continued advancements to data analysis techniques could prove vital in realization of such biomedical applications. This chapter provides a brief overview of some of the more common data analysis methods as well as outlines several of the technical challenges encountered in the implementation of these methods. The development of standardized data techniques with incorporation into fully functional integrated software platforms will also be necessary for clinical applications in future.

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References

  1. P. Anand, A.B. Kunnumakara, C. Sundaram, K.B. Harikumar, S.T. Tharakan, O.S. Lai, B. Sung, and B.B. Aggarwal. Cancer is a preventable disease that requires major lifestyle changes. Pharmaceutical research, 25(9):2097–2116, 2008.

    Article  Google Scholar 

  2. P.B. Bach, M.J. Kelley, R.C. Tate, and D.C. McCrory. Screening for lung cancer: a review of the current literature. Chest, 123(1):72S–82S, 2003.

    Article  Google Scholar 

  3. A. Barhoumi, D. Zhang, F. Tam, and N.J. Halas. Surface-enhanced Raman spectroscopy of DNA. Journal of the American Chemical Society, 130(16):5523–5529, 2008.

    Article  Google Scholar 

  4. M.S. Bergholt, W. Zheng, K. Lin, K.Y. Ho, M. Teh, K.G. Yeoh, J.B.Y. So, and Z. Huang. Raman endoscopy for in vivo differentiation between benign and malignant ulcers in the stomach. Analyst, 135:3162–3168, 2010.

    Article  Google Scholar 

  5. M.S. Bergholt, W. Zheng, K. Lin, K.Y. Ho, M. Teh, K.G. Yeoh, J.B.Y. So, and Z. Huang. Characterizing variability in in vivo Raman spectra of different anatomical locations in the upper gastrointestinal tract toward cancer detection. Journal of Biomedical Optics, 16:037003, 2011.

    Article  Google Scholar 

  6. M.S. Bergholt, W. Zheng, K. Lin, K.Y. Ho, M. Teh, K.G. Yeoh, J.B.Y. So, and Z. Huang. Combining near-infrared-excited autofluorescence and raman spectroscopy improves in vivo diagnosis of gastric cancer. Biosensors and Bioelectronics, 26(10):4104–4110, 2011.

    Article  Google Scholar 

  7. B. Bodanese, L. Silveira Jr, R. Albertini, R.A. Zângaro, and M.T.T. Pacheco. Differentiating normal and basal cell carcinoma human skin tissues in vitro using dispersive Raman spectroscopy: A comparison between principal components analysis and simplified biochemical models. Photomedicine and Laser Surgery, 28(S1):119–127, 2010.

    Google Scholar 

  8. R. Booton, F. Blackhall, and K. Kerr. Individualised treatment in non-small cell lung cancer: precise tissue diagnosis for all? Thorax, 66(4):273, 2011.

    Google Scholar 

  9. N.F. Boyd, J.M. Rommens, K. Vogt, V. Lee, J.L. Hopper, M.J. Yaffe, and A.D. Paterson. Mammographic breast density as an intermediate phenotype for breast cancer. The lancet oncology, 6(10):798–808, 2005.

    Article  Google Scholar 

  10. J.F. Brennan, Y. Wang, R.R. Dasari, and M.S. Feld. Near-infrared Raman spectrometer systems for human tissue studies. Applied spectroscopy, 51(2):201–208, 1997.

    Article  Google Scholar 

  11. A. Campion and P. Kambhampati. Surface-enhanced raman scattering. Chemical Society Reviews, 27(4):241–250, 1998.

    Article  Google Scholar 

  12. A. Cao, A.K. Pandya, G.K. Serhatkulu, R.E. Weber, H. Dai, J.S. Thakur, V.M. Naik, R. Naik, G.W. Auner, R. Rabah, et al. A robust method for automated background subtraction of tissue fluorescence. Journal of Raman Spectroscopy, 38(9):1199–1205, 2007.

    Article  Google Scholar 

  13. G.W. Carlson and W.C. Wood. Management of axillary lymph node metastasis in breast cancer. JAMA: The Journal of the American Medical Association, 305(6):606, 2011.

    Google Scholar 

  14. D.B. Chase. Fourier transform Raman spectroscopy. Journal of the American Chemical Society, 108(24):7485–7488, 1986.

    Article  Google Scholar 

  15. L.G. Collins, C. Haines, R. Perkel, and R.E. Enck. Lung cancer: diagnosis and management. American family physician, 75(1):56, 2007.

    Google Scholar 

  16. C.E. Cox, S. Pendas, J.M. Cox, E. Joseph, A.R. Shons, T. Yeatman, N.N. Ku, G.H. Lyman, C. Berman, F. Haddad, et al. Guidelines for sentinel node biopsy and lymphatic mapping of patients with breast cancer. Annals of surgery, 227(5):645, 1998.

    Google Scholar 

  17. C. DeSantis, MM Center, R. Sighel, and A. Jemal. Breast cancer facts and figures. American Caner Society, Department of Surveillance and Health Policy Research, Atlanta, Georgia, 2010:1–40, 2009.

    Google Scholar 

  18. F. Draux, P. Jeannesson, A. Beljebbar, A. Tfayli, N. Fourre, M. Manfait, J. Sulé-Suso, and G.D. Sockalingum. Raman spectral imaging of single living cancer cells: a preliminary study. Analyst, 134(3):542–548, 2009.

    Article  Google Scholar 

  19. C. Eliasson, A. Loren, J. Engelbrektsson, M. Josefson, J. Abrahamsson, and K. Abrahamsson. Surface-enhanced raman scattering imaging of single living lymphocytes with multivariate evaluation. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 61(4):755–760, 2005.

    Article  Google Scholar 

  20. F.W.L. Esmonde-White and M.D. Morris. Raman imaging and raman mapping. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields, pp. 97–110, 2010.

    Google Scholar 

  21. S. Feng, R. Chen, J. Lin, J. Pan, G. Chen, Y. Li, M. Cheng, Z. Huang, J. Chen, and H. Zeng. Nasopharyngeal cancer detection based on blood plasma surface-enhanced Raman spectroscopy and multivariate analysis. Biosensors and Bioelectronics, 25(11):2414–2419, 2010.

    Article  Google Scholar 

  22. S.Y. Feng, J.J. Pan, Y.A. Wu, D. Lin, Y.P. Chen, G.Q. Xi, J.Q. Lin, and R. Chen. Study on gastric cancer blood plasma based on surface-enhanced raman spectroscopy combined with multivariate analysis. SCIENCE CHINA Life Sciences, pp. 1–7, 2011.

    Google Scholar 

  23. M.B. Fenn, P. Xanthopoulos, G. Pyrgiotakis, S.R. Grobmyer, P.M. Pardalos, and L.L. Hench. Raman spectroscopy for clinical oncology. Advances in Optical Technologies, 2011:20, 2011, Article ID 213783, doi:10.1155/2011/213783

    Google Scholar 

  24. R.W. Field, B.J. Smith, C.E. Platz, R.A. Robinson, J.S. Neuberger, C.P. Brus, and C.F. Lynch. Lung cancer histologic type in the surveillance, epidemiology, and end results registry versus independent review. Journal of the National Cancer Institute, 96(14):1105, 2004.

    Google Scholar 

  25. M. Gao, G. Lewis, G.M. Turner, A. Soubret, and V. Ntziachristos. Effects of background fluorescence in fluorescence molecular tomography. Applied optics, 44(26):5468–5474, 2005.

    Article  Google Scholar 

  26. A.E. Giuliano, K.K. Hunt, K.V. Ballman, P.D. Beitsch, P.W. Whitworth, P.W. Blumencranz, A.M. Leitch, S. Saha, L.M. McCall, and M. Morrow. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis. JAMA: The Journal of the American Medical Association, 305(6):569, 2011.

    Google Scholar 

  27. A.E. Giuliano, L. McCall, P. Beitsch, P.W. Whitworth, P. Blumencranz, A.M. Leitch, S. Saha, K.K. Hunt, M. Morrow, and K. Ballman. Locoregional recurrence after sentinel lymph node dissection with or without axillary dissection in patients with sentinel lymph node metastases: the american college of surgeons oncology group z0011 randomized trial. Annals of surgery, 252(3):426, 2010.

    Google Scholar 

  28. M. Gniadecka, P.A. Philipsen, S. Sigurdsson, S. Wessel, O.F. Nielsen, D.H. Christensen, J. Hercogova, K. Rossen, H.K. Thomsen, R. Gniadecki, et al. Melanoma diagnosis by Raman spectroscopy and neural networks: structure alterations in proteins and lipids in intact cancer tissue. Journal of investigative dermatology, 122(2):443–449, 2004.

    Article  Google Scholar 

  29. D.S. Grubisha, R.J. Lipert, H.Y. Park, J. Driskell, and M.D. Porter. Femtomolar detection of prostate-specific antigen: an immunoassay based on surface-enhanced raman scattering and immunogold labels. Analytical chemistry, 75(21):5936–5943, 2003.

    Article  Google Scholar 

  30. P. Hainaut and A. Plymoth. Biomarkers in cancer research and treatment: promises and challenges. Current Opinion in Oncology, 23(1):61, 2011.

    Google Scholar 

  31. A.S. Haka, K.E. Shafer-Peltier, M. Fitzmaurice, J. Crowe, R.R. Dasari, and M.S. Feld. Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using raman spectroscopy. Cancer research, 62(18):5375, 2002.

    Google Scholar 

  32. A.S. Haka, K.E. Shafer-Peltier, M. Fitzmaurice, J. Crowe, R.R. Dasari, and M.S. Feld. Diagnosing breast cancer by using Raman spectroscopy. Proceedings of the National Academy of Sciences of the United States of America, 102(35):12371, 2005.

    Google Scholar 

  33. K.Y. Ho, M. Teh, K.G. Yeoh, and J.B.Y. So. In vivo diagnosis of esophageal cancer using image-guided raman endoscopy and biomolecular modeling. Technology in Cancer Research and Treatment, 10(2), 2011.

    Google Scholar 

  34. J. Horsnell, P. Stonelake, J. Christie-Brown, G. Shetty, J. Hutchings, C. Kendall, and N. Stone. Raman spectroscopy a new method for the intra-operative assessment of axillary lymph nodes. Analyst, 135:3042–3047, 2010.

    Article  Google Scholar 

  35. X. Huang, I.H. El-Sayed, W. Qian, and M.A. El-Sayed. Cancer cells assemble and align gold nanorods conjugated to antibodies to produce highly enhanced, sharp, and polarized surface raman spectra: a potential cancer diagnostic marker. Nano letters, 7(6):1591–1597, 2007.

    Article  Google Scholar 

  36. Z. Huang, S.K. Teh, W. Zheng, K. Lin, K.Y. Ho, M. Teh, and K.G. Yeoh. In vivo detection of epithelial neoplasia in the stomach using image-guided raman endoscopy. Biosensors and Bioelectronics, 26(2):383–389, 2010.

    Article  Google Scholar 

  37. Z. Huang, S.K. Teh, W. Zheng, J. Mo, K. Lin, X. Shao, K.Y. Ho, M. Teh, and K.G. Yeoh. Integrated Raman spectroscopy and trimodal wide-field imaging techniques for real-time in vivo tissue raman measurements at endoscopy. Optics letters, 34(6):758–760, 2009.

    Article  Google Scholar 

  38. A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, and D. Forman. Global cancer statistics. CA: a cancer journal for clinicians, 61:69–90, 2011.

    Article  Google Scholar 

  39. A. Jemal, M.M. Center, C. DeSantis, and E.M. Ward. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiology Biomarkers & Prevention, 19(8):1893, 2010.

    Google Scholar 

  40. A. Jemal, R. Siegel, J. Xu, and E. Ward. Cancer statistics, 2010. CA: a cancer journal for clinicians, 60(5):277–300, 2010.

    Article  Google Scholar 

  41. L. Kass, J.T. Erler, M. Dembo, and V.M. Weaver. Mammary epithelial cell: influence of extracellular matrix composition and organization during development and tumorigenesis. The international journal of biochemistry & cell biology, 39(11):1987–1994, 2007.

    Article  Google Scholar 

  42. M.D. Keller, E.M. Kanter, C.A. Lieber, S.K. Majumder, J. Hutchings, D.L. Ellis, R.B. Beaven, N. Stone, and A. Mahadevan-Jansen. Detecting temporal and spatial effects of epithelial cancers with Raman spectroscopy. Disease markers, 25(6):323–337, 2008.

    Google Scholar 

  43. C. Kendall, N. Stone, N. Shepherd, K. Geboes, B. Warren, R. Bennett, and H. Barr. Raman spectroscopy, a potential tool for the objective identification and classification of neoplasia in barrett’s oesophagus. The Journal of pathology, 200(5):602–609, 2003.

    Article  Google Scholar 

  44. J.H. Kim, J.S. Kim, H. Choi, S.M. Lee, B.H. Jun, K.N. Yu, E. Kuk, Y.K. Kim, D.H. Jeong, M.H. Cho, et al. Nanoparticle probes with surface enhanced Raman spectroscopic tags for cellular cancer targeting. Analytical chemistry, 78(19):6967–6973, 2006.

    Article  Google Scholar 

  45. M. Kirsch, G. Schackert, R. Salzer, and C. Krafft. Raman spectroscopic imaging for in vivo detection of cerebral brain metastases. Analytical and Bioanalytical Chemistry, 398(4):1707–1713, 2010.

    Article  Google Scholar 

  46. C.N. Klabunde, P.M. Marcus, G.A. Silvestri, P.K.J. Han, T.B. Richards, G. Yuan, S.E. Marcus, and S.W. Vernon. Us primary care physicians’ lung cancer screening beliefs and recommendations. American journal of preventive medicine, 39(5):411–420, 2010.

    Article  Google Scholar 

  47. J. Kleeff, C. Michalski, H. Friess, and M.W. Büchler. Pancreatic cancer: from bench to 5-year survival. Pancreas, 33(2):111, 2006.

    Google Scholar 

  48. J. Kneipp, T.B. Schut, M. Kliffen, M. Menke-Pluijmers, and G. Puppels. Characterization of breast duct epithelia: a Raman spectroscopic study. Vibrational spectroscopy, 32(1):67–74, 2003.

    Article  Google Scholar 

  49. S. Koljenovic, T.B. Schut, A. Vincent, J.M. Kros, and G.J. Puppels. Detection of meningioma in dura mater by Raman spectroscopy. Analytical chemistry, 77(24):7958–7965, 2005.

    Article  Google Scholar 

  50. S. Koljenović, T.C.B. Schut, J.P. van Meerbeeck, A.P.W.M. Maat, S.A. Burgers, P.E. Zondervan, J.M. Kros, and G.J. Puppels. Raman microspectroscopic mapping studies of human bronchial tissue. Journal of Biomedical Optics, 9:1187, 2004.

    Article  Google Scholar 

  51. V. Kulasingam and E.P. Diamandis. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nature Clinical Practice Oncology, 5(10):588–599, 2008.

    Article  Google Scholar 

  52. C.A. Lieber and A. Mahadevan-Jansen. Automated method for subtraction of fluorescence from biological Raman spectra. Applied spectroscopy, 57(11):1363–1367, 2003.

    Article  Google Scholar 

  53. G.Y. Locker, S. Hamilton, J. Harris, J.M. Jessup, N. Kemeny, J.S. Macdonald, M.R. Somerfield, D.F. Hayes, and R.C. Bast. Asco 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. Journal of Clinical Oncology, 24(33):5313, 2006.

    Google Scholar 

  54. F.M. Lyng, EO Faolain, J. Conroy, AD Meade, P. Knief, B. Duffy, MB Hunter, JM Byrne, P. Kelehan, and H.J. Byrne. Vibrational spectroscopy for cervical cancer pathology, from biochemical analysis to diagnostic tool. Experimental and Molecular Pathology, 82(2):121–129, 2007.

    Google Scholar 

  55. N.D. Magee, R.J. Beattie, R. Gray, M. Imrie, M. Ennis, J. McGarvey, and J.S. Elborn. Raman spectroscopy analysis of induced sputum in lung cancer. American Journal of Respiratory and Critical Care Medicine, 181(1 MeetingAbstracts):A3492, 2010.

    Google Scholar 

  56. L.J. Martin and N. Boyd. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. Breast Cancer Res, 10(1):1–14, 2008.

    Article  Google Scholar 

  57. A. Matschulat, D. Drescher, and J. Kneipp. Surface-enhanced raman scattering hybrid nanoprobe multiplexing and imaging in biological systems. ACS nano, 4(6):3259–3269, 2010.

    Article  Google Scholar 

  58. S.T. McCain, R.M. Willett, and D.J. Brady. Multi-excitation Raman spectroscopy technique for fluorescence rejection. Optics Express, 16(15):10975–10991, 2008.

    Article  Google Scholar 

  59. J. Mo, W. Zheng, and Z. Huang. Fiber-optic raman probe couples ball lens for depth-selected raman measurements of epithelial tissue. Biomedical optics express, 1(1):17–30, 2010.

    Article  Google Scholar 

  60. J.T. Motz, M. Hunter, L.H. Galindo, J.A. Gardecki, J.R. Kramer, R.R. Dasari, and M.S. Feld. Optical fiber probe for biomedical Raman spectroscopy. Applied optics, 43(3):542–554, 2004.

    Article  Google Scholar 

  61. S.J. Nass and H.L. Moses. Cancer biomarkers: the promises and challenges of improving detection and treatment. National Academy Press, Washington, D.C., USA, 2007.

    Google Scholar 

  62. O.E. Nieweg, L. Jansen, R.A. Valdes Olmos, E.J.T. Rutgers, J.L. Peterse, K.A. Hoefnagel, and B.B.R. Kroon. Lymphatic mapping and sentinel lymph node biopsy in breast cancer. European journal of nuclear medicine, 26:11, 1999.

    Article  Google Scholar 

  63. A. Nijssen, S. Koljenović, T.C.B. Schut, P.J. Caspers, and G.J. Puppels. Towards oncological application of Raman spectroscopy. Journal of Biophotonics, 2(1–2):29–36, 2009.

    Article  Google Scholar 

  64. A. Nijssen, T.C.B. Schut, F. Heule, P.J. Caspers, D.P. Hayes, M.H.A. Neumann, and G.J. Puppels. Discriminating basal cell carcinoma from its surrounding tissue by Raman spectroscopy. Journal of investigative dermatology, 119(1):64–69, 2002.

    Article  Google Scholar 

  65. World Health Organization. World health statistics. World Health Organization, 2007.

    Google Scholar 

  66. J.B. OConnell, M.A. Maggard, and C.Y. Ko. Colon cancer survival rates with the new american joint committee on cancer sixth edition staging. Journal of the National Cancer Institute, 96(19):1420, 2004.

    Google Scholar 

  67. I.I. Patel and F.L. Martin. Discrimination of zone-specific spectral signatures in normal human prostate using Raman spectroscopy. Analyst, 135:3060–3069, 2010.

    Article  Google Scholar 

  68. GD Pitt, DN Batchelder, R. Bennett, RW Bormett, IP Hayward, BJE Smith, KPJ Williams, YY Yang, KJ Baldwin, and S. Webster. Engineering aspects and applications of the new raman instrumentation. In IEE Proceedings of Science, Measurement and Technology, volume 152, pp. 241–318. IET, 2005.

    Google Scholar 

  69. J.D. Potter. Morphogens, morphostats, microarchitecture and malignancy. Nature Reviews Cancer, 7(6):464–474, 2007.

    Article  MathSciNet  Google Scholar 

  70. G. Pyrgiotakis, O.E. Kundakcioglu, K. Finton, P.M. Pardalos, K. Powers, and B.M. Moudgil. Cell death discrimination with Raman spectroscopy and support vector machines. Annals of biomedical engineering, 37(7):1464–1473, 2009.

    Article  Google Scholar 

  71. X.M. Qian and SM Nie. Single-molecule and single-nanoparticle sers: from fundamental mechanisms to biomedical applications. Chemical Society Reviews, 37(5):912–920, 2008.

    Google Scholar 

  72. P.M. Ramos and I. Ruisánchez. Noise and background removal in Raman spectra of ancient pigments using wavelet transform. Journal of Raman Spectroscopy, 36(9):848–856, 2005.

    Article  Google Scholar 

  73. L.A. Reisner, A. Cao, and A.K. Pandya. An integrated software system for processing, analyzing, and classifying Raman spectra. Chemometrics and Intelligent Laboratory Systems, 105(1):83–90, 2010.

    Article  Google Scholar 

  74. V.L. Roggli, R.T. Vollmer, S.D. Greenberg, M.H. McGavran, H.J. Spjut, and R. Yesner. Lung cancer heterogeneity: a blinded and randomized study of 100 consecutive cases. Human pathology, 16(6):569–579, 1985.

    Article  Google Scholar 

  75. H. Sato, H. Shinzawa, and Y. Komachi. Fiber-optic raman probes for biomedical and pharmaceutical applications. Emerging Raman Applications and Techniques in Biomedical and Pharmaceutical Fields, pp. 25–45, 2010.

    Google Scholar 

  76. M. Sattlecker, C. Bessant, J. Smith, and N. Stone. Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics. Analyst, 135(5):895–901, 2010.

    Article  Google Scholar 

  77. S. Schlücker, B. Küstner, A. Punge, R. Bonfig, A. Marx, and P. Ströbel. Immuno-raman microspectroscopy: In situ detection of antigens in tissue specimens by surface-enhanced raman scattering. Journal of Raman Spectroscopy, 37(7):719–721, 2006.

    Article  Google Scholar 

  78. K.E. Shafer-Peltier, A.S. Haka, J.T. Motz, M. Fitzmaurice, R.R. Dasari, and M.S. Feld. Model-based biological Raman spectral imaging. Journal of Cellular Biochemistry, 87(S39):125–137, 2002.

    Google Scholar 

  79. G. Shetty, C. Kendall, N. Shepherd, N. Stone, and H. Barr. Raman spectroscopy: elucidation of biochemical changes in carcinogenesis of oesophagus. British journal of cancer, 94(10):1460–1464, 2006.

    Article  Google Scholar 

  80. K.W. Short, S. Carpenter, J.P. Freyer, and J.R. Mourant. Raman spectroscopy detects biochemical changes due to proliferation in mammalian cell cultures. Biophysical journal, 88(6):4274–4288, 2005.

    Article  Google Scholar 

  81. M.A. Short, S. Lam, A. McWilliams, J. Zhao, H. Lui, and H. Zeng. Development and preliminary results of an endoscopic raman probe for potential in vivo diagnosis of lung cancers. Optics letters, 33(7):711–713, 2008.

    Article  Google Scholar 

  82. L.M.W.K. Song, A. Molckovsky, K.K. Wang, L.J. Burgart, B. Dolenko, R.L. Somorjai, and B.C. Wilson. Diagnostic potential of Raman spectroscopy in barrett’s esophagus. In Proceedings of SPIE, volume 5692, p. 140, 2005.

    Google Scholar 

  83. P.R. Srinivas, B.S. Kramer, and S. Srivastava. Trends in biomarker research for cancer detection. The lancet oncology, 2(11):698–704, 2001.

    Article  Google Scholar 

  84. M. Sylvest Bergholt, Z. Wei, et al. In vivo diagnosis of gastric cancer using raman endoscopy and ant colony optimization techniques. International journal of cancer, 128(11):2673–2680, 2011.

    Article  Google Scholar 

  85. A. Taleb, J. Diamond, J.J. McGarvey, J.R. Beattie, C. Toland, and P.W. Hamilton. Raman microscopy for the chemometric analysis of tumor cells. The Journal of Physical Chemistry B, 110(39):19625–19631, 2006.

    Article  Google Scholar 

  86. C.M. Tammemagi, P.F. Pinsky, N.E. Caporaso, P.A. Kvale, W.G. Hocking, T.R. Church, T.L. Riley, J. Commins, M.M. Oken, C.D. Berg, et al. Lung cancer risk prediction: Prostate, lung, colorectal and ovarian cancer screening trial models and validation. Journal of the National Cancer Institute, 103(13):1058, 2011.

    Google Scholar 

  87. F.K. Tangka, J.G. Trogdon, L.C. Richardson, D. Howard, S.A. Sabatino, and E.A. Finkelstein. Cancer treatment cost in the United States. Cancer, 116(14):3477–3484, 2010.

    Article  Google Scholar 

  88. SK Teh, W. Zheng, KY Ho, M. Teh, KG Yeoh, and Z. Huang. Diagnostic potential of near-infrared Raman spectroscopy in the stomach: differentiating dysplasia from normal tissue. British journal of cancer, 98(2):457–465, 2008.

    Google Scholar 

  89. SK Teh, W. Zheng, KY Ho, M. Teh, KG Yeoh, and Z. Huang. Near-infrared Raman spectroscopy for early diagnosis and typing of adenocarcinoma in the stomach. British Journal of Surgery, 97(4):550–557, 2010.

    Google Scholar 

  90. J.S. Thomas, D. Lamb, T. Ashcroft, B. Corrin, CW Edwards, AR Gibbs, WE Kenyon, RJ Stephens, and WF Whimster. How reliable is the diagnosis of lung cancer using small biopsy specimens? report of a ukcccr lung cancer working party. Thorax, 48(11):1135, 1993.

    Google Scholar 

  91. T.J. Vickers, R.E. Wambles, and C.K. Mann. Curve fitting and linearity: data processing in Raman spectroscopy. Applied Spectroscopy, 55(4):389–393, 2001.

    Article  Google Scholar 

  92. T. Vo-Dinh, H.N. Wang, and J. Scaffidi. Plasmonic nanoprobes for sers biosensing and bioimaging. Journal of biophotonics, 3(1–2):89–102, 2010.

    Google Scholar 

  93. A.W. Wark, R.J. Stokes, S.B. Darby, W.E. Smith, and D. Graham. Dynamic imaging analysis of sers-active nanoparticle clusters in suspension. The Journal of Physical Chemistry C, 114(42):18115–18120, 2010.

    Article  Google Scholar 

  94. H.G. Welch, S. Woloshin, and L.M. Schwartz. The sea of uncertainty surrounding ductal carcinoma in situ the price of screening mammography. Journal of the National Cancer Institute, 100(4):228, 2008.

    Google Scholar 

  95. E. Widjaja, W. Zheng, and Z. Huang. Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines. International journal of oncology, 32(3):653–662, 2008.

    Google Scholar 

  96. P. Xanthopoulos, R. De Asmundis, M. Guarracino, G. Pyrgiotakis, and P. Pardalos. Supervised classification methods for mining cell differences as depicted by Raman spectroscopy. Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science (LNCS), volume 6685/2011, pp. 112–122, 2011.

    Google Scholar 

  97. S. Yegiyants, L.M. Romero, P.I. Haigh, and L.A. DiFronzo. Completion axillary lymph node dissection not required for regional control in patients with breast cancer who have micrometastases in a sentinel node. Archives of Surgery, 145(6):564, 2010.

    Google Scholar 

  98. C. Zavaleta, A. De La Zerda, Z. Liu, S. Keren, Z. Cheng, M. Schipper, X. Chen, H. Dai, and SS Gambhir. Noninvasive Raman spectroscopy in living mice for evaluation of tumor targeting with carbon nanotubes. Nano letters, 8(9):2800–2805, 2008.

    Google Scholar 

  99. C.L. Zavaleta, B.R. Smith, I. Walton, W. Doering, G. Davis, B. Shojaei, M.J. Natan, and S.S. Gambhir. Multiplexed imaging of surface enhanced raman scattering nanotags in living mice using noninvasive Raman spectroscopy. Proceedings of the National Academy of Sciences, 106(32):13511, 2009.

    Google Scholar 

  100. D. Zhang, K.N. Jallad, and D. Ben-Amotz. Stripping of cosmic spike spectral artifacts using a new upper-bound spectrum algorithm. Applied Spectroscopy, 55(11):1523–1531, 2001.

    Article  Google Scholar 

  101. J. Zhao. Image curvature correction and cosmic removal for high-throughput dispersive Raman spectroscopy. Applied spectroscopy, 57(11):1368–1375, 2003.

    Article  Google Scholar 

  102. J. Zhao, M.M. Carrabba, and F.S. Allen. Automated fluorescence rejection using shifted excitation raman difference spectroscopy. Applied spectroscopy, 56(7):834–845, 2002.

    Article  Google Scholar 

  103. J. Zhao, H. Lui, D.I. McLean, and H. Zeng. Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Applied spectroscopy, 61(11):1225–1232, 2007.

    Article  Google Scholar 

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Authors and Affiliations

  1. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Michael B. Fenn

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Michael B. Fenn & Vijay Pappu

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Vijay Pappu

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  1. Michael B. Fenn
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  2. Vijay Pappu
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Correspondence to Michael B. Fenn .

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  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

  2. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Petros Xanthopoulos

  3. Dept. Electronic & Computer, Engineering, Technical University of Crete, Chania, Crete, 731 00, Greece

    Michalis Zervakis

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Fenn, M.B., Pappu, V. (2012). Data Mining for Cancer Biomarkers with Raman Spectroscopy. In: Pardalos, P., Xanthopoulos, P., Zervakis, M. (eds) Data Mining for Biomarker Discovery. Springer Optimization and Its Applications(), vol 65. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2107-8_8

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