Detection of Melanoma Skin Cancer by Elastic Scattering Spectra: A Proposed Classification Method

Document Type : Original Paper


1 1.Medical physicist, Medical laser research center,ACECR 2. Laser &Plasma Research institute, Shahid Beheshti University

2 PhD, Professor of medical physics, Institute of advanced technologies in medicine (IAMT), Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences

3 assistant professor of dermatology, Medical Laser Research Center, Iranian Center for Medical Lasers (ICML), Academic Center for Education, Culture and Research (ACECR)

4 MSc of medical physics, Institute of advanced technologies in medicine (IAMT), Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences,


Introduction: There is a strong need for developing clinical technologies and instruments for prompt tissue assessment in a variety of oncological applications as smart methods. Elastic scattering spectroscopy (ESS) is a real-time, noninvasive, point-measurement, optical diagnostic technique for malignancy detection through changes at cellular and subcellular levels, especially important in early diagnosis of invasive skin cancer, melanoma. In fact, this preliminary study was conducted to provide a classification method for analyzing the ESS spectra. Elastic scattering spectra related to the normal skin and melanoma lesions, which were already confirmed pathologically, were provided as input from an ESS database.
Materials and Methods: A program was developed in MATLAB based on singular value decomposition and K-means algorithm for classification.
Results: Accuracy and sensitivity of the proposed classifying method for normal and melanoma spectra were 87.5% and 80%, respectively.
Conclusion: This method can be helpful for classification of melanoma and normal spectra. However, a large body of data and modifications are required to achieve better sensitivity for clinical applications.


Main Subjects

  1. Siegel R, Naishadham D, Jemal A. Cancer statistics. 2013; CA Cancer J Clin. 2013 Jan; 63(1):11-30.
  2. Murphy BW, Webster RJ, Turlach BA, Clay CJD, Heenan PJ., Sampson DD.toward the discrimination of early melanomafrom common and dysplastic nevus using fiber opticdiffuse reflectance spectroscopy. Journal of Biomedical Optics, 2005; 10:6, 0640201-9.
  3. A'Amar OM, Ley RD, Bigio IJ. Comparison between ultraviolet-visible and nearinfraredelastic scattering spectroscopy of chemicallyinduced melanomas in an animal model.  Journal of Biomedical Optics, 2004; 9(6), 1320–1326.
  4. American Cancer Society. Cancer Facts & Figures 2016. Atlanta: American Cancer Society; 2016.
  5. Garbe C, Peris K, Hauschild A, Saiag P, Middleton M, Bastholt L, Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline e Update 2016. European Journal of Cancer, 2016; 63, 201-217.
  6. Wang SQ, Setlow R, Berwick M, Polsky D, Marghoob AA, Kopf AW, Bart RS. Ultraviolet A and melanoma: A review. J Am Acad Dermatol, 2001; 44:837-46.
  7. Day CL Jr, Mihm MC Jr, Lew RA, Kopf AW, Sober AJ, Fitzpatrick TB. Cutaneous Malignant Melanoma: Prognostic Guidelines for Physicians and Patients. CA-A cancer journal for clinician, 1982; 32: 2.
  8. Bigio IJ and Mourant JR. Ultraviolet and visible spectroscopies for tissuediagnostics: fluorescence spectroscopy and elastic-scattering spectroscopy. Phys. Med. Biol, 1997; 42803–814.
  9. Bigio IJ, Bown SG, Briggs G, Kelley, Lakhani S, Pickard D, Ripley PM, Rose IG, Saunders C. Diagnosis of breast cancer C.using elastic-scatteringspectroscopy: preliminary clinical results. Journal of Biomedical Optics, 2000; 5(2), 221–228.
  10. Lovat LB, Bown SG. Elastic scattering spectroscopy for detection of dysplasiain Barrett’s esophagus”, gastrointestinal endoscopy clinicsof North America: optical biopsy, 14. Amsterdam: Elsevier, 2004; 507–17.
  11. Mourant JR, Canpolat M, Brocker C .Light scattering from cells: thecontribution of the nucleus and the effects of proliferative status. J Biomed Opt, 2000; 5:131–7.
  12. Zhu Y. Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis. PhD thesis, Department of Statistical Science, National Medical Laser Centre, University College London, 2009.
  13. Omar E, Current concepts and future of noninvasive procedures for diagnosing oral squamous cell carcinoma - a systematic review, Head & Face Medicine. 2015; 11:6.
  14. Upile T, Jerjes W, Johal O, Lew-Gor S, Mahil J, Sudhoff H. A new tool to inform intra-operative decision making in skin cancer treatment: the non-invasive assessment of basal cell carcinoma of the skin using elastic scattering spectroscopy. Head Neck Oncol. 2012 Oct 31; 4(3):74.
  15. Mourant JR, Hielscher AH, Eick AA. Evidence of intrinsic differences inthe light scattering properties of tumorigenic and nontumorigenic cells. Cancer, 1998; 84:366–74.
  16. Sharwani A, Jerjes W, Salih V, Swinson B, Bigio IJ, El-Maaytah M, Hopper C. Assessment of oral premalignancy using elastic scattering spectroscopy, Oral Oncology 42, 2006;  343–349.
  17. Upile T, Jerjes W, Radhi H, Mahil J, Rao A, Hopper C. Elastic scattering spectroscopy in assessing skin lesions: An ‘‘in vivo’’ study. Photodiagnosis and Photodynamic Therapy 9, 2012; 132-141.
  18. Dhar A, Johnson KS, Novelli MR, Bown SG, Bigio IJ, Lovat LB, Bloom SL. Elastic scattering spectroscopy for the diagnosis of colonic lesions: initial results of a novel optical biopsy technique. Gastrointestinal endoscopy, 2006; 63(2), 257-262.
  19. Vaswani N and Guo H, Correlated-PCA: Principal Components’ Analysis when Data and Noise are correlated, 30th Conference on Neural Information Processing Systems, NIPS 2016.
  20. Oblefias1WR., Soriano MN, Saloma CA. “SVD vs. PCA: Comparison of Performance in an Imaging Spectrometer. Science Diliman. 2004; 16:2, 74-78.
  21. Golchin E, Maghooli K, Overview of Manifold Learning and Its Application in Medical Data set, International journal of Biomedical Engineering and Science (IJBES), Vol. 1, No. 2, July 2014.
  22. Karamizadeh  S, Abdullah SM, Manaf AA, Zamani M, Hooman A, An Overview of Principal Component Analysis, Journal of Signal and Information Processing, 2013, 4, 173-175.
  23. Kalman D, A Singularly Valuable Decomposition: The SVD of a Matrix, The College Mathematics Journal 27 (1996), 2-23.

Petrıcek M, Components in Data Analysis, WDS'10 Proceedings of Contributed Papers, Part I, 82–87, 2010.