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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 16, Iss. 19 — Sep. 15, 2008
  • pp: 14961–14978

Model based and empirical spectral analysis for the diagnosis of breast cancer

Changfang Zhu, Tara M. Breslin, Josephine Harter, and Nirmala Ramanujam  »View Author Affiliations

Optics Express, Vol. 16, Issue 19, pp. 14961-14978 (2008)

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We explored the use of both empirical (Partial Least Squares, PLS) and Monte Carlo model based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from freshly excised breast tissues and for the diagnosis of breast cancer. Features extracted using both approaches, i.e. principal components (PCs) obtained from empirical analysis or tissue properties obtained from model based analysis, displayed statistically significant difference between malignant and non-malignant tissues, and can be used to discriminate breast malignancy with comparable sensitivity and specificity of up to 90%. The PC scores of a subset of PCs also displayed significant correlation with the tissue properties extracted from the model based analysis, suggesting both approaches likely probe the same sources of contrast in the tissue spectra that discriminate between malignant and non-malignant breast tissues but in different ways.

© 2008 Optical Society of America

OCIS Codes
(170.0170) Medical optics and biotechnology : Medical optics and biotechnology
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(300.0300) Spectroscopy : Spectroscopy

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: April 14, 2008
Revised Manuscript: August 6, 2008
Manuscript Accepted: August 6, 2008
Published: September 9, 2008

Virtual Issues
Vol. 3, Iss. 11 Virtual Journal for Biomedical Optics

Changfang Zhu, Tara M. Breslin, Josephine Harter, and Nirmala Ramanujam, "Model based and empirical spectral analysis for the diagnosis of breast cancer," Opt. Express 16, 14961-14978 (2008)

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  1. C. Zhu, G. M. Palmer, T. M. Breslin, F. Xu, and N. Ramanujam, "The Use of a Multi-separation Fiber Optic Probe for the Optical Diagnosis of Breast Cancer," J. Biomed. Opt. 10, 024032 (2005). [CrossRef] [PubMed]
  2. C. Zhu, G. M. Palmer, T. M. Breslin, J. Harter, and N. Ramanujam, "Diagnosis of Breast Cancer using Fluorescence and Diffuse Reflectance Spectroscopy: a Monte Carlo Model Based Approach," J. Biomed. Opt. 13, 034015 (2008). [CrossRef] [PubMed]
  3. C. Zhu, G. M. Palmer, T. M. Breslin, J. Harter, and N. Ramanujam, "Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique," Lasers Surg. Med. 38, 714-724 (2006). [CrossRef] [PubMed]
  4. Y. Yang, A. Katz, E. J. Celmer, M. Zurawska-Szczepaniak, and R. R. Alfano, "Fundamental differences of excitation spectrum between malignant and benign breast tissues," Photochem. Photobiol. 66, 518-522 (1997). [CrossRef] [PubMed]
  5. Y. Yang, A. Katz, E. J. Celmer, M. Zurawska Szczepaniak, and R. R. Alfano, "Optical spectroscopy of benign and malignant breast tissues," Lasers in the Life Sciences 7, 115-127 (1996).
  6. Y. Yang, E. J. Celmer, M. Zurawska Szczepaniak, and R. R. Alfano, "Excitation spectrum of malignant and benign breast tissues: a potential optical biopsy approach," Lasers in the Life Sciences 7, 249-265 (1997).
  7. G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, "Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003)," IEEE Trans. Biomed. Eng. 50, 1233-1242 (2003). [CrossRef] [PubMed]
  8. G. M. Palmer, and N. Ramanujam, "Diagnosis of Breast Cancer Using Optical Spectroscopy," Medical Laser Application 18, 233-248 (2003). [CrossRef]
  9. P. K. Gupta, S. K. Majumder, and A. Uppal, "Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy," Lasers Surg. Med. 21, 417-422 (1997). [CrossRef] [PubMed]
  10. I. J. Bigio, S. G. Bown, G. Briggs, C. Kelley, S. Lakhani, D. Pickard, P. M. Ripley, I. G. Rose, and C. Saunders, "Diagnosis of breast cancer using elastic-scattering spectroscopy: preliminary clinical results," J. Biomed. Opt. 5, 221-228 (2000). [CrossRef] [PubMed]
  11. I. J. Bigio, and J. R. Mourant, "Ultraviolet and visible spectroscopies for tissue diagnostics: fluorescence spectroscopy and elastic-scattering spectroscopy," Phys. Med. Biol. 42, 803-814 (1997). [CrossRef] [PubMed]
  12. C.-H. Liu, B. B. Das, W. L. Sha Glassman, G. C. Tang, K. M. Yoo, H. R. Zhu, D. L. Akins, S. S. Lubicz, J. Cleary, R. Prudente, E. J. Celmer, A. Caron, and R. R. Alfano, "Raman, fluorescence, and time-resolved light scattering as optical diagnostic techniques to separate diseased and normal biomedical media," J Photochem. Photobiol., B: Biol. 16, 187-209 (1992). [CrossRef]
  13. S. K. Majumder, P. K. Gupta, B. Jain, and A. Uppal, "UV excited autofluorescence spectroscopy of human breast tissues for discriminating cancerous tissue from benign tumor and normal tissue," Lasers in the Life Sciences 8, 249-264 (1999).
  14. S. V. Pushkarev, S. A. Naumov, S. M. Vovk, V. A. Volovodenko, and V. V. Udut, "Application of laser fluorescence spectroscopy and diffuse reflection spectroscopy in diagnosing the states of mammary gland tissue," Optoelectronics-Instrumentation and Data Processing 2, 71-76 (1999).
  15. R. R. Alfano, B. B. Das, J. Cleary, R. Prudente, and E. J. Celmer, "Light sheds light on cancer--distinguishing malignant tumors from benign tissues and tumors," Bull N. Y. Acad. Med. 67, 143-150 (1991). [PubMed]
  16. Y. Yang, E. J. Celmer, J. A. Koutcher, and R. R. Alfano, "UV reflectance spectroscopy probes DNA and protein changes in human breast tissues," J. Clin. Laser Med. Surg. 19, 35-39 (2001). [CrossRef] [PubMed]
  17. V. G. Peters, D. R. Wyman, M. S. Patterson, and G. L. Frank, "Optical properties of normal and diseased human breast tissues in the visible and near infrared," Phys. Med. Biol. 35, 1317-1334 (1990). [CrossRef] [PubMed]
  18. N. Ghosh, S. K. Mohanty, S. K. Majumder, and P. K. Gupta, "Measurement of optical transport properties of normal and malignant human breast tissue," Appl. Opt. 40, 176-184 (2001). [CrossRef]
  19. G. M. Palmer, C. Zhu, T. M. Breslin, F. Xu, K. W. Gilchrist, and N. Ramanujam, "Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis," Appl. Opt. 45, 1072-1078 (2006). [CrossRef] [PubMed]
  20. G. M. Palmer, and N. Ramanujam, "Monte Carlo based Model to Extract Intrinsic Fluorescence from Turbid Media: Theory and Phantom Validation," (2007).
  21. H. Martens, Multivariate Calibration (John Wiley & Sons, New York, 1989).
  22. G. M. Palmer, and N. Ramanujam, "Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms," Appl. Opt. 45, 1062-1071 (2006). [CrossRef] [PubMed]
  23. R. Tauler, "Multivariate Curve Resolution, MCR-ALS Command Line Toolbox," (2006).
  24. R. M. Bethea, B. S. Duran, and T. L. Boullion, Statistical methods for engineers and scientists (M. Dekker, New York, 1995).
  25. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Min. Knowledge Discov. 2, 121-167 (1998). [CrossRef]
  26. N. Cristianini, and J. Shawe-Taylor, An Introduction to Support Vector Machines: and other Kernel-based Learning Methods (Cambridge University Press, Cambridge, New York, 2000).
  27. J. S. U. Hjorth, Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap (Chapman & Hall, London, New York, 1994).

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