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ORIGINAL ARTICLE
Year : 2012  |  Volume : 8  |  Issue : 6  |  Page : 126-132

Raman spectroscopy in head and neck cancers: Toward oncological applications


1 Chilakapati Laboratory, ACTREC TMC, Kharghar, Navi Mumbai 410210, India
2 Department of Surgical Oncology, Tata Memorial Hospital, Mumbai, 400012, India

Date of Web Publication24-Jan-2012

Correspondence Address:
C Murali Krishna
Chilakapati Laboratory, Cancer Research Institute, ACTREC, TMC, Kharghar, Sector '22', Navi Mumbai - 410210
India
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Source of Support: Project no: BT/PRI11282/MED/32/83/2008, Department of Biotechnology, Government of India, Conflict of Interest: None


DOI: 10.4103/0973-1482.92227

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 > Abstract 

Aims: To test the spectral reproducibility of earlier findings under ex vivo conditions and to record good quality in vivo spectra in clinically implementable time in finger print region.
Materials and Methods: Spectra from 20 ex vivo tissues (10 normal and 10 tumor) were recorded using fiber optic probe coupled Raman spectrometer. In vivo spectra from 10 healthy volunteers were also recorded. Spectral differences were analyzed by PC-LDA method followed by validation by leave-one-out and test samples.
Results: Spectral features of ex vivo normal tissue suggest predominant lipid features while protein is high in tumor conditions. Major spectral features of in vivo healthy volunteers share several commonalities with ex vivo normal tissues except minor differences in amide III region. Classification efficiency of 90, 85 and 96% for ex vivo tumor, normal and in vivo normal standard models, respectively after leave-one-out cross validation, was observed. Test prediction efficiency of standard models of ex vivo normal, ex vivo tumor and in vivo healthy volunteers were 82.5, 92.5 and 100%, respectively.
Conclusions: Our findings corroborate with the reported ex vivo and in vivo normal spectral features. Features of in vivo Raman spectra show strong similarities with ex vivo normal spectra minor but significant differences were also observed. Findings of this study indicate that with our instrument in vivo Raman spectra in finger print region can be recorded in short and clinically implementable time.

Keywords: Fiberoptic probe, in vivo Raman spectroscopy, LDA, oral cancers


How to cite this article:
Singh S P, Deshmukh A, Chaturvedi P, Krishna C M. Raman spectroscopy in head and neck cancers: Toward oncological applications. J Can Res Ther 2012;8, Suppl S2:126-32

How to cite this URL:
Singh S P, Deshmukh A, Chaturvedi P, Krishna C M. Raman spectroscopy in head and neck cancers: Toward oncological applications. J Can Res Ther [serial online] 2012 [cited 2020 May 26];8:126-32. Available from: http://www.cancerjournal.net/text.asp?2012/8/6/126/92227


 > Introduction Top


Oral squamous cell carcinomas (OSCC) are sixth among most common malignancies, which are known to arise from the oral mucosal lining and account for more than 90% of the tumors in the oral cavity and oropharynx. It is considered as the most common cancer among males in Indian and other south Asian countries. Despite significant advances in surgical procedures and treatment modalities, the long-term prognosis of this disease remains to be poor; five-year survival rate for oral cancers is shown to be around 50%, which is among the lowest. [1] Use of tobacco is known to be the major causative factor for more than 90% of tumors of the oral cavity among men and women. All forms of tobacco-cigarettes, pipes, cigars, and smokeless tobacco have been implicated in induction and development of oral cancers. [2] High mortality rate of many OSCCs is often attributed to the late detection of disease. Conventional oral examination (COE), using normal (incandescent) light, followed by histopathological examination of biopsied specimen is the gold standard diagnosis. However, numbers of studies have shown the limitation in COE as diagnostic protocol especially for pre-cancerous or early cancerous lesions. Some of the known limitations are time consumption and inter observer disagreements. [3],[4],[5] It has also been shown that, early diagnosis leads to better prognosis and increased five-year survival rates up to 90%. Hence, there is a need for new diagnostic techniques, which could enable early detection of oral cancer. In view of inherent accessibility of the oral cavity and most often oral pre cancerous conditions precedes in OSCC, there is a feasibility for development of new, effective and non invasive diagnostic methodologies, such as optical spectroscopic based diagnostic tools. Optical spectroscopic methods are being pursued as alternative or adjunct to existing diagnostic methods. A variety of optical-based techniques like fluorescence, Raman and Fourier-transform infrared spectroscopy have been explored for the development of newer diagnostic tools. [6],[7],[8],[9],[10],[11],[12],[13],[14],[15],[16],[17],[18] These methods are capable of providing biochemical and morphological information at short acquisition times, which can be used for online diagnosis.

Fluorescence-based diagnosis in oral cancer started with use of exogenous fluorophores. [19] However, limitations like long time lag and impracticality for use in regular screenings of high-risk patient groups rendered this approach less applicable. Another widely used approach is diagnosis based on autofluorescence (also called natural, endogenous or laser induced fluorescence) of naturally occurring fluorophores like collagen, elastin, keratin and NADH. A recent review by D.C.G. De Veld et al. has summarized major developments in these technologies. [20]Simpler instrumentation and shorter acquisition time are the major attractions of these techniques, especially from routine usage point of view. But limited spectral information and use of multiple excitation wavelengths in order to explore major intrinsic fluorophores are some of the known hindrances.

Fourier transform infrared spectroscopy (FT-IR) is an absorption-based vibrational spectroscopy method. A recent report of fiber-optic FTIR-based study in oral cancer has shown loss of triglycerides and alterations in protein content, suggested by changes in amide I band as common features in malignant tissues. [21] Differences based on aberrant distribution of keratin in oral tissues are also reported. [22] However, these methodologies are less suitable for in vivo and in situ studies as water, the major component of biological tissues, is highly absorptive in the mid-IR range. ATR-based methodologies could be useful in circumventing this difficulty.

Raman effect is based on inelastic scattering of photons. Small fraction of photons (1 in 10 8 ) are inelastically scattered and exchanges energy with the molecular vibration. [23] Unlike FT-IR, Raman spectroscopy does not suffer from water interference. Inherent weak scattering is the major disadvantage with Raman spectroscopy. Hence, Raman spectra of biological tissues are often swamped by parasitic fluorescence. However, latest developments in light sources (lasers) and detectors (charge coupled devices) have made Raman spectroscopy of even weakly scattering samples like tissues and cells easy. Use of near infrared photons e.g. 785, 830 or 850 nm which is less harmful and also minimizes the associated fluorescence is the other major development. Most important attribute of Raman spectroscopy lies in its adaptation to in vivo conditions. The use of optical fibers for guiding laser light to desired site and also to collect Raman photons facilitates in vivo measurements. In view of above attributes, Raman spectroscopy is projected as an ideal tool in pursuing biomedical applications. Raman spectroscopic differentiation of normal and cancerous tissues of oral, breast, cervix, colon, stomach ovarian and other forms of cancers have been reported in the literature. [15],[16],[24],[25],[26],[27],[28],[29] In vivo Raman measurements from bladder and prostate, [30] esophagus, [31],[32],[33] skin, [34],[35],[36] cervix [37],[38] and arteries [39] are already been reported. Because of strong Raman signal of fused silica fiber probes in fingerprint region, it is easy and practical to explore high wave number (HWVN) region (2500-4000 cm -1 ). But HWVN is limited in content of biochemical information with respect to fingerprint region. Major spectral features in the high wave number region are due to S-H, C-H, O-H, N-H stretching vibrations. Nevertheless, several studies have explored of HWVN in classifying tissues. [40],[41],[42],[43] As far as oral cavity is concerned Guze et al. has reported in vivo Raman study in HWVN region. In this study, authors have compared spectra from different sites of oral cavity and reported differences among them. [44]

In our earlier studies, we have shown classification of normal, pre-malignant and malignant conditions in ex vivo oral tissues. [15],[16] In the present study, we have assessed reproducibility of spectral features under ex vivo conditions with a fiber-based system before taking up the in vivo applications. We have also carried out in vivo Raman measurements of healthy volunteers. Findings of the study are discussed in this article.


 > Materials and Methods Top


Sample details and Raman spectroscopy

Histopathologically confirmed 20 ex vivo tissues (10 normal and 10 tumor) from buccal mucosa were collected in PBS and stored in liquid nitrogen. Spectra were collected on HE-785 commercial Raman spectrometer (LabRam, Jobin-Vyon-Horiba, France) using InPhotonics (Inc, Downy St, USA) fiberoptic probe. Photographic representation of the instrument is shown in [Figure 1]. Samples were placed on a CaF 2 window and spectra were recorded at different points with spacing of 1-2 mm using XYZ precision stage. On an average, 10 spectra from each tissue were collected. Spectral acquisition parameters for ex vivo Raman studies were: λex -785nm, laser power-52 mW and spectra were integrated for 10 s and averaged over 5 accumulations. A total of 100 spectra each from normal and tumor tissues were recorded. 60 spectra from 6 tissues each of normal and tumor were used for developing standard model while remaining 40 spectra from 4 tissues and each were as test data set.
Figure 1: Photographic representation of the instrumental set up

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We have also collected in vivo spectra from 10 healthy volunteers after obtaining their informed written consent using above described instrument. The experimental conditions were laser power-80 mW, integration time 4 s and averaged over 3 accumulations. Spectra were recorded at different points on buccal mucosa as per teeth positions. Buccal surfaces opposing teeth were considered as reference point. The spectra were recorded from buccal surfaces of canine, first premolar, second premolar, first molar and second molar. Spectra were numbered from 1 to 5 respectively and procedure was followed for both right and left buccal mucosa in all subjects. A total of 100 spectra from 10 healthy volunteers were used in this study. As in the case of ex vivo study, standard model was developed using 60 spectra from 6 subjects and remaining 40 spectra of 4 subjects were used as test data.

Data analysis

Spectra from ex vivo and in vivo conditions were corrected for CCD response, spectral contamination from CaF 2 background as wells as for fiber signals. Corrected spectra were then first derivativized followed by vector normalization. Spectra in 1200-1800 cm -1 region were used for PCA-based linear discriminant analysis. PC-LDA was performed using in-house MATLAB based software. [45] While developing spectral models, LDA was carried out using 20 factors accounting for ~95% variance in the spectral data as shown in [Figure 2]. Data analysis was performed in three steps in first step standard classifier models belonging to each class i.e. ex vivo normal, ex vivo tumor and in vivo normal were developed using 60 spectra from each class. In second step, the classifier model was verified by leave-one-out (LOO) cross validation method. These models were then evaluated by separate test data set comprising 40 spectra from each category.
Figure 2: Cumulative percent variance contribution of PCA factors used for LDA

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 > Results Top


The mean Raman spectra of ex vivo normal (dotted line), ex vivo tumor (broken line) and in vivo healthy volunteers (solid line) in 1200-1800 cm -1 region are shown in [Figure 3]. Spectral features of ex vivo normal suggest predominant lipid features as indicated by Raman bands at 1750 cm -1 , strong CH 2 bend at 1450 cm -1 , two sharp features around 1300 cm -1 and sharp peak at 1660 cm -1 . Protein bands indicated by 1320 cm -1 amide III, broad δCH 2 and broad features in the 1660 cm -1 amide I were seen in mean malignant spectrum. Even though, major spectral features of in vivo healthy volunteers are similar (high lipid content) to ex vivo normal tissues, there also exists distinct differences in amide III and 1550-1600 cm -1 region [Figure 3]. These spectral features show strong similarities to that were observed in our earlier studies as well as that are reported by others. [15],[16],[46] Thus repeatability of spectral features was once again established.
Figure 3: Average spectra in 1200– 1800 cm-1 region of in vivo healthy volunteers (solid line), ex vivo normal (dotted line), ex vivo tumor (broken line)

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We have employed multivariate tool, PC-LDA to explore the feasibility of classification between ex vivo and in vivo spectra. This analysis resulted 3 independent clusters corresponding to ex vivo normal, ex vivo tumor tissue and in vivo spectra [Figure 4]a. These results are also summarized in confusion matrix shown in [Table 1]a and classification efficiency of 100% was observed for all three classes of ex vivo normal, ex vivo tumor and in vivo healthy volunteers. Leave-one-out is a cross validation method for evaluation of performance of the classification models without losing the diversity in the data. In case of LOO, classification models are generated iteratively by leaving out one sample in each model such that n samples should contribute to n-1 models development. And the sample is classified according to the model that was developed independently of that sample ("out of bag" prediction). The performance is estimated as a percentage of number of correct predictions over all samples used in dataset. 54 out of 60 ex vivo tumor spectra were correctly classified while 6 were misclassified as ex vivo normal spectra. 51 of 60 spectra from ex vivo normal condition were correctly classified while 8 spectra were misclassified as ex vivo tumor spectra and 1 as in vivo normal spectra. Similarly, 58 of 60 spectra from in vivo normal were correctly classified while rest of them were misclassified as ex vivo normal. Classification efficiency of standard models for ex vivo tumor, normal and in vivo normal 90%, 85 and 96.6%, respectively was observed [Table 1]b. In last step of data analysis, 40 spectra from all three categories were used as test data and test prediction efficiency of standard models was evaluated. Overall prediction efficiencies of standard models of ex vivo normal, ex vivo tumor and in vivo healthy volunteers were 82.5%, 92.5 and 100%, respectively. As shown in [Figure 4]b-d, all 40 spectra from healthy volunteers were predicted correctly. 33 out of 40 ex vivo tumor spectra were correctly predicted and 7 were misclassified as ex vivo normal spectra. Only 3 of 40 spectra in case of ex vivo normal models were misclassified as ex vivo tumor spectra.
Figure 4: Scatter plot of LDA. (a) Standard model. (b) Prediction of test ex vivo normal spectra. (c) Prediction of test
ex vivo tumor spectra. (d) Prediction of test in vivo healthy volunteer spectra


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Table 1: Confusion matrix of LDA. (a) Standard model development. (b) Leave-one-out cross validation (diagonal elements are true positive predictions and false positive predictions are indicated by ex-diagonal elements)

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 > Discussion Top


Oral mucosa is one of the most accessible sites of body to detect alteration of mucosa in its early stage; still the incidence of malignant lesions and potentially malignant pathologies of oral mucosa is increasing. The limitations of current clinical and histopathological diagnostic methods like inter observer subjectivity in diagnosis of early malignant lesions, invasive, and traumatic biopsy procedures stress toward the need of alternate/adjunct method of diagnosis.

Optical diagnosis methods based on fluorescence, infra red and Raman have been pursued as potential tools in detection of different cancers. Major advantages of these methods are the following: these require no or minimum sample preparation, objectivity as data is amenable to multivariate tools and most importantly less time consuming. Among these methods, due to interference of water bands, FTIR is less practical for in vivo applications. Fluorescence and Raman spectroscopy methods are widely being explored for in vivo applications. Recent ex vivo and in vivo studies using fluorescence spectroscopy by Kamath et al., Jayanthi et al., Chaturvedi et al., and Subhash et al.[47],[48],[49],[50] have demonstrated the potential of this technology in classification of normal, malignant and premalignant conditions as well as in identification of dental caries. But fluorescence is a wavelength dependent phenomenon. Studies carried out using N 2 laser or He-Cd laser gave better results as they can excite multiple endogenous fluorophores like collagen, NADH, flavins etc. But these wavelengths fall in relatively harmful UVA and UVB regions. Whereas, studies with visible ranges most often ends up in exciting single fluorophores, as shown in a recent study by Rehman et al,[51] and decrease in collagen associated green fluorescence in neoplastic transformation. Thus for a better understanding at molecular level multiple excitations are needed.

Raman spectroscopy is not influenced by water, it is not strictly wavelength dependent and least harmful NIR sources can be used for excitation. Therefore, it is considered as an ideal tool for in vivo applications. Previous studies, mostly our own on ex vivo oral tissues have successfully shown efficacy of this methodology in classifying normal, cancer and premalignant conditions. [15],[16] Presently translational approaches for in vivo applications are being actively pursued. Limitations like weak Raman signals often swamped by high fluorescence background and longer acquisition time because of low throughput spectrographs are some of the well-known hindrances for in vivo applications. Some of the recent developments in fiberoptic probes, CCDs and good quality filters have shown a prospect for in vivo applications. Various groups have shown applicability of Raman spectroscopy in detecting cancers of different sites like skin, gastrointestinal tract, and breast under in vivo conditions. Since, light can be delivered easily through fiberoptic probes to desired site, Raman spectroscopic methods are more adaptable for regular screening of oral cavity. There have also been attempts to record good quality Raman spectra in oral cavity but none on oral pathological conditions. In this study, we have verified the reproducibility of ex vivo spectra and also recorded in vivo spectra from healthy volunteers using fiberoptic probe coupled Raman spectrometer. Main objective was to establish the reproducibility of Raman spectral features of ex vivo tissues and also to test the feasibility of recording good quality in vivo Raman spectra (preferably in finger print region) in short acquisition time before taking up the clinical studies. Our findings corroborate with the reported ex vivo and in vivo normal spectral features. Features of in vivo Raman spectra show strong similarities i.e. abundance of lipids with ex vivo normal spectra. Minor but significant differences between both groups of spectra are also observable; these features can be used for classification. Findings of the study support applicability of our fiber probe coupled system in recording good quality Raman spectra in fingerprint region in short and clinically implementable time. However, translational application for routine clinical usage needs rigorous evaluation by larger data set comprising different conditions. We are, in fact, actively pursuing in vivo Raman studies of oral cancers, precancers and other pathological conditions.


 > Conclusion Top


Oral cancer is one of the most common malignancies in Indian subcontinent. Raman spectroscopic methods, more adaptable for in vivo applications, are being projected as novel non-invasive, objective diagnostic methods. In the present study, we have taken up in vivo Raman spectroscopy of oral cancers and successfully demonstrated reproducibility of spectral features of ex vivo oral tissues as well as feasibility of recording in vivo spectra in short and clinically implementable time in oral cavity using a fiber probe coupled system. In future, validation with large data under different pathological categories is required to use this methodology for routine clinical screening.


 > Acknowledgements Top


This work was carried out under project no: BT/PRI11282/MED/32/83/2008, Department of Biotechnology and Government of India. Authors would like to acknowledge Ms. Arti R. Hole for helping with histopathological analysis of ex vivo tissues and also to all healthy volunteers who have participated in the study.

 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1]


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