# Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the “Fingerprint” Region

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## Abstract

**:**

## 1. Introduction

## 2. Experimental Setup

#### 2.1. Tissue Samples

#### 2.2. Apparatus

## 3. Principal Component Analysis

**X**(M × N) is reduced and may be expressed as

**T**(M × A) is the scores matrix and

**P**(A × N) is the loadings matrix, and

**E**is the error matrix. The principal components were computed using the princomp function in MatLab. In general, for discrimination purposes, we retain those principal components that contained 99% of the information content.

## 4. Results

## 5. Summary and Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References and Note

- Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin.
**2011**, 61, 64–90. [Google Scholar] [CrossRef] [PubMed] - Abogunrin, S.; DiTanna, G.L.; Keeping, S.; Carroll, S.; Iheanacho, I. Prevalence of human papillomavirus in head and neck cancers in European populations: A meta-analysis. BMC Cancer
**2014**, 14, 968. [Google Scholar] [CrossRef] [PubMed] - Elrefaey, S.; Massaro, M.A.; Chiocca, S.; Chiesa, F.; Ansarin, M. HPV in oropharyngeal cancer: The basics to know in clinical practice. Acta Otorhinolaryngol. Ital.
**2014**, 34, 299–309. [Google Scholar] [PubMed] - Calin, M.A.; Parasca, S.V.; Savastru, R.; Calin, M.R.; Dontu, S. Optical techniques for the noninvasive diagnosis of skin cancer. J. Cancer Res. Clin. Oncol.
**2013**, 139, 1083–1104. [Google Scholar] [CrossRef] [PubMed] - Escobedo, J.O.; Rusin, O.; Lim, S.; Strongin, R.M. NIR Dyes for Bioimaging Applications. Curr. Opin. Chem. Bio.
**2010**, 14, 64–70. [Google Scholar] [CrossRef] [PubMed] - Ardeshirpour, Y.; Chernomordik, V.; Capala, J.; Hassan, M.; Zielinsky, R.; Griffiths, G.; Achilefu, S.; Smith, P.; Gandjbakche, A. Using In-Vivo Fluorescence Imaging in Personalized Cancer Diagnostics and Therapy, an Image and Treat Paradigm. Technol. Cancer Res. Treat.
**2011**, 10, 549–560. [Google Scholar] [CrossRef] [PubMed] - Harris, A.T.; Rennie, A.; Waqar-Uddin, H.; Wheatley, S.R.; Ghosh, S.K.; Martin-Hirsch, D.P.; Fisher, S.E.; High, A.S.; Kirkham, J.; Upile, T. Raman spectroscopy in head and neck cancer. Head Neck Oncol.
**2010**, 2, 26. [Google Scholar] [CrossRef] [PubMed] - Krafft, C.; Dietzek, B.; Schmitt, M.; Popp, J. Raman and coherent anti-Stokes Raman scattering microspectroscopy for biomedical applications. J. Biomed. Opt.
**2012**, 17, 040801. [Google Scholar] [CrossRef] [PubMed] - Knipfer, C.; Motz, J.; Adler, W.; Brunner, K.; Gebrekidan, M.T.; Hankel, R.; Agaimy, A.; Wil, S.; Braeuer, A.; Neukam, F.W.; et al. Raman difference spectroscopy: A non-invasive method for identification of oral squamous cell carcinoma. Biomed. Opt. Exp.
**2014**, 5, 3252–3265. [Google Scholar] [CrossRef] [PubMed] - Wang, W.; Zhao, J.; Short, M.; Zeng, H. Real-time in vivo cancer diagnosis using Raman spectroscopy. J. Biophoton.
**2015**, 8, 527–545. [Google Scholar] [CrossRef] [PubMed] - Tan, A.; Yildrimer, L.; Rajadas, J.; de la Pena, H.; Pastorin, G.; Seifalian, A. Quantum dots and carbon nanotubes in oncology: A review on emerging theranostic applications in nanomedicine. Nanomedicine
**2011**, 6, 1101–1114. [Google Scholar] [CrossRef] [PubMed] - Mahadevan-Jansen, A.; Richards-Kortum, R. Raman spectroscopy for the detection of cancers and precancers. J. Biomed. Opt.
**1996**, 1, 31–70. [Google Scholar] [CrossRef] [PubMed] - Su, L.; Sun, Y.F.; Chen, Y.; Chen, P.; Shen, A.G.; Wang, X.H.; Jia, J.; Zhao, Y.F.; Zhou, X.D.; Hu, J.M. Raman spectral properties of squamous cell carcinoma of oral tissues and cell. Laser Phys.
**2011**, 22, 311–316. [Google Scholar] [CrossRef] - Devpura, S.; Thakur, J.S.; Sethi, S.; Naik, V.M.; Naik, R. Diagnosis of head and neck squamous cell carcinoma using Raman spectroscopy: Tongue tissues. J. Raman Spectrosc.
**2012**, 43, 490–496. [Google Scholar] [CrossRef] - Singh, S.P.; Deshmukh, A.; Chaturvedi, P.; Murali Krishna, C. Raman spectroscopy in head and neck cancers: Toward oncological applications. J. Cancer Res. Ther.
**2012**, 8 (Suppl. 2), S126–S132. [Google Scholar] [PubMed] - Guze, K.; Pawluk, H.C.; Short, M.; Zeng, H.; Lorch, J.; Norris, C.; Sonis, S. Pilot study: Raman spectroscopy in differentiating premalignant and malignant oral lesions from normal mucosa and benign lesions in humans. Head Neck
**2014**, 37, 511–517. [Google Scholar] [CrossRef] [PubMed] - Valdés, R.; Stefanov, S.; Chiussi, S.; López-Alvarez, M.; González, P. Pilot research on the evaluation and detection of head and neck squamous cell carcinoma by Raman spectroscopy. J. Raman Spectrosc.
**2014**, 45, 550–557. [Google Scholar] [CrossRef] - Cals, F.L.J.; Schut, T.C.B.; Hardillo, J.A.; de Jong, R.J.B.; Koljenović, S.; Puppels, G.J. Investigation of the potential of Raman spectroscopy for oral cancer detection in surgical margins. Lab. Invest.
**2015**, 95, 1186–1195. [Google Scholar] [CrossRef] [PubMed] - Lin, K.; Zheng, W.; Lim, C.M.; Huang, Z. Real-time in vivo diagnosis of laryngeal carcinoma with rapid fiber-optic Raman spectroscopy. Biomed. Opt. Exp.
**2016**, 7, 3705–3715. [Google Scholar] [CrossRef] [PubMed] - Lin, K.; Cheng, D.L.P.; Huang, Z. Optical diagnosis of laryngeal cancer using high wavenumber Raman spectroscopy. Biosens. Bioelectron.
**2012**, 35, 213–217. [Google Scholar] [CrossRef] [PubMed] - Holler, S.; Wurtz, R.; Auyeung, K.; Auyeung, K.; Paspaley-Grbavac, M.; Mulroe, B.; Sobrero, M.; Miles, B.A. Ex-vivo holographic microscopy and spectroscopic analysis of head and neck cancer. In Proceedings of the SPIE 9328, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIII, 93281L, San Francisco, CA, USA, 2 March 2015. [Google Scholar]
- The nonuniform thickness is a function of pathologic processing; these are real life samples from actual surgery, not controlled scientific samples. The thickness of the sections examined is at the discretion of the attending pathologist during the surgery and accounts for the variability.
- Andronie, L.; Mireşan, V.; Coroian, A.; Pop, I.; Raducu, C.; Rotaru, A.; Cocan, D.; Cîntă Pînzaru, S.; Domsa, I.; Coroian, C.O. Raman spectroscopy of the Hematoxylin-Eoisin stained tissue. ProEnvironment
**2015**, 8, 590–600. [Google Scholar] - Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis; Prentice Hall: Upper Saddle River, NJ, USA, 1998. [Google Scholar]
- Rashid, N.; Nawaz, H.; Poon, K.W.C.; Bonnier, F.; Bakhiet, S.; Martin, C.; O’Leary, J.J.; Byrne, H.J.; Lyng, F.M. Raman microspectroscopy for the early detection of pre-malignant changes in cervical tissue. Exp. Molec. Path.
**2014**, 97, 554–564. [Google Scholar] [CrossRef] [PubMed] - Ramos, I.R.M.; Malkin, A.; Lyng, F.M. Current Advances in the Application of Raman Spectroscopy for Molecular Diagnosis of Cervical Cancer. Biomed. Res. Int.
**2015**, 2015, 9. [Google Scholar] [CrossRef] [PubMed] - Huang, Z.; McWilliams, A.; Lui, H.; Lam, S.; Zeng, H. Near-infrared Raman spectroscopy for optical diagnosis of lung cancer. Int. J. Cancer
**2003**, 107, 1047–1052. [Google Scholar] [CrossRef] [PubMed] - Devpura, S. Raman Spectroscopy and Diffuse Reflectance Spectroscopy for Diagnosis of Human Cancer and Acanthosis Nigricans. Ph.D. Dissertation, Wayne State University, Detroit, MI, USA, 2012. [Google Scholar]
- Tuer, A.; Tokarz, D.; Prent, N.; Cisek, R.; Alami, J.; Dumont, D.J.; Bakueva, L.; Rowlands, J.; Barzda, V. Nonlinear multicontrast microscopy of hematoxylin-and-eosin-stained histological sections. J. Biomed. Opt.
**2010**, 15, 026018. [Google Scholar] [CrossRef] [PubMed] - Bautista, P.A.; Yagi, Y. Digital simulation of staining in histopathology multispectral images: Enhancement and linear transformation of spectral transmittance. J. Biomed. Opt.
**2012**, 17, 056013. [Google Scholar] [CrossRef] [PubMed] - Wang, H.; Boraey, M.A.; Williams, L.; Lechuga-Ballesteros, D.; Vehring, R. Low-frequency shift dispersive Raman spectroscopy for the analysis of respirable dosage forms. Int. J. Pharm.
**2014**, 469, 197–205. [Google Scholar] [CrossRef] [PubMed] - Balandin, A.A.; Fonoberov, V.A. Vibrational Modes of Nano-Template Viruses. J. Biomed. Nanotech.
**2005**, 1, 90–95. [Google Scholar] [CrossRef] - Talati, M.; Jha, P.K. Acoustic phonon quantization and low-frequency Raman spectra of spherical viruses. Phys. Rev. E
**2006**, 73, 011901. [Google Scholar] [CrossRef] [PubMed] - Montagna, M. Brillouin and Raman scattering from the acoustic vibrations of spherical particles with a size comparable to the wavelength of the light. Phys. Rev. B
**2008**, 77, 045418. [Google Scholar] [CrossRef] - Vehring, R.; Ivey, J.; Williams, L.; Joshi, V.; Dwivedi, S.K.; Lechuga-Ballesteros, D. High-sensitivity analysis of crystallinity in respirable powders using low frequency shift-Raman spectroscopy. In Proceedings of Respiratory Drug Delivery 2012; Dalby, R.N., Byron, P.R., Peart, J., Suman, J.D., Farr, S.J., Young, P.M., Eds.; Virginia Commonwealth University: Richmond, VA, USA, 2012; pp. 641–644. [Google Scholar]

**Figure 1.**Schematic of the experimental setup showing the 785 nm laser directed into the Raman probe via the 10× objective lens. The probe illuminates the tissue sample and collects the scattered light. The elastically scattered signal is removed via a long pass filter in the filter/lens assembly before the light is transmitted into the Maya Pro 2000 NIR spectrometer for dispersion and storage.

**Figure 2.**Representative Raman spectra from two different samples, one healthy and one cancerous over the entire ∼4000 ${\mathrm{cm}}^{-1}$ Raman shift signal. The peaks that show differences between the healthy tissue and the cancerous tissue are indicated by the arrows.

**Figure 3.**The difference spectrum obtained by subtracting the cancerous spectrum from the healthy spectrum in Figure 2. This spectrum highlights the peaks indicated above.

**Figure 4.**Plot of the first two principal component scores for (

**A**) for the full spectrum (100 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$) and (

**B**) the conventional fingerprint (400 ${\mathrm{cm}}^{-1}$–1800 ${\mathrm{cm}}^{-1}$). Both plots show good separation between the healthy controls and the malignant tissue samples (both tonsil squamous cell carcinoma and squamous cell carcinoma). The numbers in parentheses represent the information content associated with each principal component.

**Figure 5.**Plot of the second and third principal component scores for (

**A**) for the full spectrum (100 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$) and (

**B**) the conventional fingerprint (400 ${\mathrm{cm}}^{-1}$–1800 ${\mathrm{cm}}^{-1}$). The full spectrum analysis reveals a distinct boundary between the healthy and diseased tissue, however no obvious separation of the data is observed when looking at the conventional fingerprint region. This increased separation is due to the peaks observed outside the conventional fingerprint regime. The numbers in parentheses represent the information content associated with each principal component.

**Figure 6.**Plots of the loadings for the first three principal components. The loadings for the first is dominated by a large Rayleigh peak near the 0 ${\mathrm{cm}}^{-1}$ shift, but small peaks can be seen further out. The loadings for the second and third principal components clearly show the peaks and contribute strongly to the discrimination capability of the spectra.

**Figure 7.**(

**A**) Plot of the first two principal component (PC) scores for only the unstained tissue samples. The analysis was performed on the full spectral data, and show good separation between healthy and diseased tissue. The numbers in parentheses represent the information content associated with each principal component. (

**B**) Corresponding loadings plots for the PCs shown in (

**A**).

**Figure 8.**(

**A**) Plot of the first two PC scores for only the unstained tissue samples. The analysis was performed in the Raman shift region from 400 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$ and show good separation between the healthy and diseased tissue classes. The numbers in parentheses represent the information content associated with each principal component. (

**B**) Corresponding loadings plots for the PCs shown in (

**A**).

**Figure 9.**(

**A**) Plot of the first two PC scores for only the unstained tissue samples. The analysis was performed in the Raman shift region from 400 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$ and show good separation between the healthy and diseased tissue classes. The numbers in parentheses represent the information content associated with each principal component. (

**B**) Corresponding loadings plots for the PCs shown in (

**A**).

**Figure 10.**(

**A**) Plot of the first two PC scores for the unstained tissue samples. The analysis was performed in the Raman shift region from 400 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$ and show good separation between the healthy and diseased tissue classes. The numbers in parentheses represent the information content associated with each principal component. (

**B**) Corresponding loadings plots for the PCs shown in (

**A**).

**Figure 11.**(

**A**) Plot of the first two PC scores for all (stained and unstained) the tissue samples. The analysis was performed in the Raman shift region from 1800 ${\mathrm{cm}}^{-1}$–4300 ${\mathrm{cm}}^{-1}$ and continues to show good separation between the healthy and diseased tissue classes despite the inclusion of the stained tissue samples. The numbers in parentheses represent the information content associated with each principal component. (

**B**) Corresponding loadings plots for the PCs shown in (

**A**).

Patient | Tissue ID | Sex/Age | Diagnosis |
---|---|---|---|

005094 | S01A | F/53 | Control |

005094 | S01B | F/53 | Tonsil SCC |

005112 | S01A | M/49 | Tonsil SCC |

005118 | S01A | M/49 | Tonsil SCC |

005120 | S01A | M/70 | Control |

005120 | S01B | M/70 | Control |

005120 | S01C | M/70 | SCC |

005120 | S01D | M/70 | SCC |

005120 | S01E | M/70 | SCC |

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## Share and Cite

**MDPI and ACS Style**

Holler, S.; Mansley, E.; Mazzeo, C.; Donovan, M.J.; Sobrero, M.; Miles, B.A.
Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the “Fingerprint” Region. *Biosensors* **2017**, *7*, 20.
https://doi.org/10.3390/bios7020020

**AMA Style**

Holler S, Mansley E, Mazzeo C, Donovan MJ, Sobrero M, Miles BA.
Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the “Fingerprint” Region. *Biosensors*. 2017; 7(2):20.
https://doi.org/10.3390/bios7020020

**Chicago/Turabian Style**

Holler, Stephen, Elaina Mansley, Christopher Mazzeo, Michael J. Donovan, Maximiliano Sobrero, and Brett A. Miles.
2017. "Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the “Fingerprint” Region" *Biosensors* 7, no. 2: 20.
https://doi.org/10.3390/bios7020020