Analytical and Bioanalytical Chemistry

, Volume 407, Issue 27, pp 8279–8289 | Cite as

Raman spectroscopy for screening and diagnosis of cervical cancer

  • Fiona M. Lyng
  • Damien Traynor
  • Inês R. M. Ramos
  • Franck Bonnier
  • Hugh J. Byrne
Part of the following topical collections:
  1. Raman4Clinics


Cervical cancer is the fourth most common cancer in women worldwide and mainly affects younger women. The mortality associated with cervical cancer can be reduced if the disease is detected at the pre-cancer stage. Current best-practice methods include cytopathology, HPV testing, and histopathology, but these methods are limited in terms of subjectivity, cost, and time. There is an unmet clinical need for new methods to aid clinicians in the early detection of cervical pre-cancer. These methods should be objective and rapid and require minimal sample preparation. Raman spectroscopy is a vibrational spectroscopic technique by which incident radiation is used to induce vibrations in the molecules of a sample and the scattered radiation may be used to characterise the sample in a rapid and non-destructive manner. Raman spectroscopy is sensitive to subtle biochemical changes occurring at the molecular level, enabling spectral variations corresponding to disease onset to be detected. Over the past 15 years, there have been numerous reports revealing the potential of Raman spectroscopy together with multivariate statistical analysis for the detection of a variety of cancers. This paper discusses the recent advances and challenges for cervical-cancer screening and diagnosis and offers some perspectives for the future.


Raman spectroscopy Cervical cancer Cervical intraepithelial neoplasia (CIN) Low-grade squamous intraepithelial lesion (LSIL) High-grade squamous intraepithelial lesion (HSIL) Cytopathology Histopathology Human papilloma virus (HPV) 

Cervical cancer

Cervical cancer is the fourth most common cancer in women worldwide, accounting for an estimated 528,000 new cases and 266,000 deaths in 2012 [1]. The mortality associated with cervical cancer can be reduced if the disease is detected at the early stages of development or at the pre-malignant stage (cervical intraepithelial neoplasia, CIN). Unlike most other types of cancer, cervical cancer affects mainly younger women, with approximately 60 % of cases occurring in women under 50 years of age. Persistent infection with high-risk human papillomavirus (HPV) (e.g. HPV types 16 and 18) is accepted as the major cause of the development of cervical pre-cancer and cancer [2]. Other risk factors include smoking, immunosuppression, long-term use of oral contraceptives, and low socioeconomic status [3].

Cervical cancer begins in the basal layer of cells lining the cervix when the normal cells slowly change into pre-cancer cells that have the potential to turn into cancer. The gradual progression of cervical cancer can allow the detection of dysplastic changes before invasive cancer develops, through cervical-cancer screening programmes. These screening programmes are common in developed countries, greatly reducing the mortality from cervical cancer, but are not yet implemented in developing countries because of lack of infrastructure and funding.

Cervical cancer screening and diagnosis

The Pap test

The Pap test, also called the Pap smear, cervical smear, or smear test, is a screening method invented independently by Georgios Papanicolau and Aurel Babeş, but named after Papanicolau. It was introduced in the mid-1940s and is currently the most common screening method for cervical neoplasia and its precursor lesions [4].

The smear is collected by scraping the internal wall of the cervix with a cervical brush to obtain representative material from the transformation zone where the stratified squamous epithelium of the ectocervix turns into the columnar mucus-secreting epithelium of the endocervix. The cells are then transferred onto a microscope slide, either by the conventional method, in which the cells are spread along the slide immediately after collection and fixed with a spray fixative, or by liquid-based cytology (LBC), in which the cells are transferred immediately after collection into a vial with a fixative solution and subsequently processed to remove debris and transferred to a slide (ThinPrep® (Hologic) or SurePath® (BD)).

Once on a slide, the cells are Pap stained and evaluated under the microscope by a highly trained cytotechnologist or a pathologist according to the Bethesda system [5].

Cervical cytology is normally graded as negative (negative for intraepithelial lesion or malignancy (NILM)), low-grade squamous intraepithelial lesion (LSIL), or high-grade squamous intraepithelial lesion (HSIL). LSIL may regress, but HSIL is unlikely to do so and may progress to invasive disease. Current guidelines are that LSIL cases are re-tested after six months and HSIL cases are referred to colposcopy [6].

A normal Pap-stained cervical smear, for example that shown in Fig. 1, typically shows cells from the surface of the epithelium; intermediate cells, which are large polygonal cells with a round-to-oval nucleus and a blue-stained cytoplasm, and superficial cells, which are large polygonal cells with a small condensed nucleus and a pink-to-orange-stained cytoplasm. Parabasal cells, which are small round or oval cells with the nucleus occupying half of the cell and a dense blue-stained cytoplasm, can also be found, but these are more prominent in smears from postmenopausal women. Metaplastic cells, endocervical cells, and endometrial cells can also all be present in a normal smear. The most common non-epithelial cells present in the normal smear are white blood cells, including polymorphs (neutrophils) or macrophages (histiocytes), which can increase in number as a result of infection and inflammation.
Fig. 1

Pap-stained negative Thinprep slide showing parabasal (black arrowheads), intermediate (solid arrows), and superficial (dashed arrows) cells and white blood cells (red arrowheads)

The advantages of the Pap test are that it is non-invasive, inexpensive, and widely accepted. However, although it can have high specificity of up to 95–98 %, sensitivity has been revealed to vary from 74 to 96 % as a result of sampling, technical, and/or inter-observer errors mainly associated with the subjectivity of the cytological screening [7].

Semi-automated screening systems consist of an automated microscope coupled to a workstation running image-processing algorithms. Slides are scanned initially, and cells of interest are separated from the background of inflammatory cells, cellular debris, or overlapping cell clusters. Image-segmentation algorithms perform a separation of the nuclei from the cytoplasm of the cells, enabling the calculation of nuclear size, nucleus-to-cytoplasm ratio, or even definition of the texture of the observed object. However, neither of the currently available systems, the FocalPoint™ GS Imaging system (BD) or the ThinPrep™ Imaging system (Hologic), provides fully automated screening without human intervention at some stage. The final decision still lies with the cytologist, resulting in the same subjectivity problem as in manual screening. The MAVARIC trial [7] revealed no improvement in sensitivity or specificity of automated screening when compared with manual screening, nor in cost-effectiveness despite a 60–80 % increase in productivity for automated screening.

HPV testing

HPV-testing trials have recently been conducted to evaluate their effectiveness for primary and secondary screening and for proof-of-cure for safe and cost-effective lengthening of the cervical-screening interval as a result of high negative predictive values [8]. Many studies have revealed that HPV testing has higher sensitivity (>95 %) but lower specificity (~84 %) than cytology [9, 10]. HPV DNA tests, including Hybrid Capture 2 (Qiagen), Cervista HPV HR and Cervista HPV 16/18 (Hologic), and cobas HPV (Roche) assays, identify high-risk HPV oncogene expression, whereas HPV E6/E7 mRNA-based assays, for example APTIMA HPV assay (Gen Probe), identify the messenger RNA of two HPV viral oncogenes, E6 and E7. However, these tests are expensive, time-consuming, and provide no information on cervical cytopathology. Over the last decade prophylactic HPV vaccines have been developed, but, despite the introduction of these vaccines, there is still a need for regular cervical screening, because the vaccines do not protect against all oncogenic HPV types. Additionally, some women may not benefit from the vaccines if there is a pre-existing HPV infection or they do not receive the complete number of doses. After vaccination, women must still have routine Pap tests which can detect abnormal cervical growth regardless of what HPV type causes it to develop [11].


An abnormal Pap smear is followed by colposcopy, biopsy, and histopathology. Microscopic evaluation by a pathologist of a stained tissue section obtained from a biopsy is currently regarded as the best practice in cancer diagnostics, and is called pathological histology or histopathology.

After collection during colposcopy, the tissue is fixed and processed into a paraffin-wax block. Tissue sections, usually in the range 2–7 μm, are cut on a microtome and transferred to a slide for staining with haematoxylin and eosin (H&E). The histological slide is then ready for examination under a light microscope by a trained pathologist. Figure 2 shows an H&E-stained normal cervical-tissue section showing stromal, basal and/or parabasal, and intermediate and/or superficial layers. Based on histopathological characteristics of the tissue, grading assesses the degree of malignancy or aggressiveness of the abnormal cells by comparing variables including cellular anaplasia, differentiation, and mitotic activity with counterparts in normal cells and tissue. However, the grading characteristics can be quite subjective and pre-malignancy may not be visually perceptible at all. The sensitivity and specificity of colposcopy-guided biopsy have been revealed to be 75–90 % and 97–99 %, respectively [12], but the subjective interpretation of histological classification is the main aspect leading to poor inter-observer agreement [13, 14, 15].
Fig. 2

H&E-stained normal cervical-tissue section showing the stromal (blue arrow), basal and/or parabasal (red arrow), and intermediate and/or superficial (green arrow) layers

Current best-practice methods for detection of cervical cancer and pre-cancer are therefore limited, and there is an unmet clinical need for new objective screening or diagnostic tests.

Raman spectroscopy

Vibrational spectroscopy analyses vibrations within a molecule; the vibrations are characteristic of the molecular structure, resulting in a spectroscopic “fingerprint” [16]. The exact energy required to excite a molecular vibration depends on the masses of the atoms involved in the vibration and the strength of the chemical bonds between these atoms, which may be affected by molecular structure, molecular interactions, and the chemical environment of the molecule.

Raman spectroscopy is based on inelastic light scattering, in which the sample is illuminated by monochromatic laser light and interactions between the incident photons and molecules in the sample result in scattering of the light. The coupling of the light generates vibrations within the sample which are characteristic of the chemical structure. The energy of the scattered light is reduced by an amount equal to the vibrational energy. As a result, the positions, relative intensities, and shapes of Raman bands carry in-depth information about the molecular composition of the sample.

Cells and tissues contain many biochemical components, including DNA, RNA, proteins, lipids, and carbohydrates, and the Raman spectra of these samples are a superposition of the contributions from each individual biochemical component. It follows that Raman spectroscopy can provide a “biochemical fingerprint” of the complete genome, proteome, and metabolome of the cell or tissue [17]. Additional analyses can be performed subsequently on the cell or tissue samples, including staining, immunocytochemistry, etc., because Raman spectroscopy can be performed in a label-free, non-destructive manner. Over the past 15 years, Raman spectroscopy has been used for the diagnosis of a wide range of cancers, including breast, prostate, oesophageal, colon, lung, oral, and cervical cancer, with excellent sensitivity and specificity being reported [18, 19, 20].

Figure 3 shows a typical Raman spectrum of cervical cells, with a list of tentative spectral assignments shown in Table 1 [21]. The Raman spectrum was acquired using a HORIBA Jobin Yvon XploRATM system (Villeneuve d’Ascq, France), which incorporates an Olympus microscope BX41 equipped with a × 100 objective (MPlanN, Olympus, NA = 0.9) and a 532 nm diode laser source. The power of the laser was set at 50 %, resulting in ~8 mW at the objective. The confocal hole, coupled to a slit of aperture 100 μm, was set at 100 μm, and a 1200 lines mm−1 grating was used. The system was pre-calibrated to the 520.7 cm−1 spectral line of silicon. The backscattered light was measured using an air-cooled CCD detector (Andor, 1024 × 256 pixels). Three spectra were recorded from the cervical-epithelial-cell nucleus, each corresponding to the average of three accumulations of 10 s. Spectral pre-processing was performed using Matlab software (Mathworks) and included smoothing (Savitzky–Golay, k = 5; w = 13), baseline correction (rubber band), and vector normalisation. The spectrum can be divided into two regions: the fingerprint region, from 400–1800 cm−1, and the high-wavenumber region, from 2500–3500 cm−1. The high-wavenumber region is dominated by C–H, N–H, and O–H vibrations and can provide significant chemically specific information, particularly on lipidic and protein structures. The fingerprint region similarly contains features which can be associated with specific chemical moieties (e.g. C=C, C=O), but also contains features associated with combinations of modes and more extensive macromolecular vibrations including skeletal vibrations. Because it is substantially richer in information, it is often favoured for (multivariate) chemical analysis.
Fig. 3

Raman spectrum of cervical cells showing fingerprint and high-wavenumber regions

Table 1

Tentative peak assignments [15] for Raman spectrum shown in Fig. 3

Wavenumber (cm−1)

Raman peak assignments


C–C twisting mode of phenylalanine (proteins)


C–C twisting mode of tyrosine and phenylalanine


Thymine, guanine (DNA and/or RNA)


C–N stretching in adenine and lipids


Symmetric breathing of tryptophan (protein)


Uracil, thymine, cytosine (ring-breathing modes in the DNA and/or RNA)


PO2 stretching in DNA, tyrosine


Ring breathing in tyrosine and proline (proteins)


C–C stretching mode of proline and valine


C–C aromatic-ring stretching in phenylalanine


C–H bending mode in phenylalanine, C–N stretching in proteins


PO2 symmetric stretching (DNA and/or RNA)


C–O stretching


C–N stretching in proteins; C–O stretching in carbohydrates


C–C and C–N stretching of proteins and/or lipids


C–H in plane-bending mode of tyrosine and phenylalanine; cytosine, guanine


C–C6H5 stretching mode in tryptophan, phenylalanine;


Amide III (C–N stretching, N–H bending, proteins), PO2 asymmetric stretching (DNA and/or RNA)


CH3 and/or CH2 twisting mode of collagen and lipid


Guanine (DNA and/or RNA), CH def. in proteins and carbohydrates


CH (CH2) bending mode in proteins and lipids


Amide II (N–H bending, C–N stretching, proteins); adenine, guanine (DNA and/or RNA)


Adenine, guanine (DNA and/or RNA); C=C bending mode of phenylalanine


C=C phenylalanine, tyrosine, and tryptophan


Amide I (C=O stretching, C–N stretching, and N–H bending, proteins)


CH2 symmetric stretching (lipids)


CH2 and CH3 symmetric stretching (lipids)


CH3 symmetric stretching (lipids)

Raman spectroscopy for cytopathology

As shown in Fig. 1, a typical normal cervical smear contains epithelial cells, including superficial cells, intermediate cells, and parabasal cells as well as metaplastic cells, endocervical cells, and endometrial cells, and non-epithelial cells, for example white blood cells. Figure 4 shows a Pap stained cervical smear sample, with the cells of interest indicated by arrows. Mean Raman spectra recorded from the unstained sample from superficial cells, intermediate cells, parabasal cells, and white blood cells are also shown. Raman spectra were acquired as previously described for Fig. 3. The spectra have substantial similarity because they are all measured from the cell nucleus, but some qualitative differences can also be observed. The white blood cells have increased contributions from nucleic acid bases at approximately 800 cm−1 and 1580 cm−1. Parabasal cells have increased contributions from amide III and phosphate stretching at 1240 cm−1, whereas superficial cells differ from intermediate cells mainly as a result of increased contributions from guanine and C–H deformation in proteins and carbohydrates at 1340 cm−1.
Fig. 4

(a) Pap-stained negative Thinprep slide showing parabasal (black arrowheads), intermediate (solid arrows), and superficial (dashed arrows) cells and white blood cells (red arrowheads); and (b) mean Raman spectra from parabasal (light blue), intermediate (blue), and superficial (red) cells and white blood cells (bottom)

Interestingly, the Raman spectra in Fig. 4 were recorded directly from Thinprep slides prepared according to current cytopathology laboratory standard procedures, apart from the Pap stain. The x,y co-ordinates of each recorded cell were saved and, after Raman recording at 532 nm, the slides were Pap stained and each cell was re-visited to verify whether it was a superficial, intermediate, parabasal, or white blood cell. Spectroscopic substrates, for example calcium fluoride, are commonly used for research purposes and, although they reduce the presence of confounding contributions of the substrate [22, 23, 24], they are substantially more expensive and thus not really a viable choice for population-screening applications including cervical-cancer screening. Although 785 nm is commonly used for biological applications of Raman spectroscopy, glass has a strong background at this wavelength; so to use glass as a substrate for Raman spectroscopy, spectra must be recorded using shorter wavelengths, for example 532 nm.

In the early 1990s several infrared-spectroscopy studies on cervical cytopathology samples were reported [25, 26, 27]. However, spectra were recorded from cell pellets rather than from individual cells and several confounding factors, including the presence of metaplastic cells, endocervical columnar cells, polymorphs, blood, cervical mucus, and debris, were identified [28, 29, 30, 31, 32, 33]. Since then, most probably as a result of the problems with confounding factors, there have been relatively few studies using vibrational spectroscopy for cervical cytopathology.

Raman spectroscopy was used by Rubina et al. [34] to distinguish between normal and cervical-cancer cytology samples. Cytology samples were treated with red blood cell lysis buffer before Raman spectral acquisition because the spectra from the cervical-cancer samples were dominated by blood features. A relatively low classification accuracy of 80 % was reported, most probably caused by sample heterogeneity because of the use of cell pellets rather than recording Raman spectra from individual cells.

A recent study from our group [35] reported on discrimination between negative cytology and CIN cytology samples using Raman spectroscopy. Importantly, all data was recorded from cells on glass ThinPrep slides, commonly used in cytopathology laboratories. The study addressed many of the problems involved in recording Raman spectra from ThinPrep cervical cytology samples, and described a new method to clear blood-residue contamination before Raman recording based on pre-treatment of the slides with hydrogen peroxide. This was revealed to minimise variability and to result in the collection of highly reproducible data with excellent discrimination between negative cytology and CIN cytology.

Raman spectroscopy for detection of HPV infection

HPV infection is accepted as one of the major risk factors associated with cervical cancer, and detection of HPV infection is being introduced as a routine screening method. Incorporation of the virus into the cell induces significant changes in the biochemistry which should also be identifiable using Raman spectroscopy.

Raman microspectroscopy has been used to distinguish between primary human keratinocytes (PHK), PHK cells expressing the E7 gene of HPV16 (PHK E7), and cervical-cancer cells expressing HPV16 (CaSki). The mean Raman spectra revealed variations in DNA and protein consistent with HPV gene expression and neoplasia. Using principal component analysis (PCA), Raman spectroscopy was revealed to discriminate between PHK and CaSki cells with a sensitivity of 93 % and a specificity of 93 %, and between PHK and PHK E7 cells with a sensitivity of 93 % and a specificity of 80 % [36].

Ostrowska et al. [37] used both infrared absorption and Raman spectroscopy to study a range of cervical-cancer cell lines. HPV-negative (C33a) and low-HPV-copy-number (SiHa with one or two copies) cell lines were revealed to be biochemically similar, but significantly different from mid (HeLa) and high (CaSki)-HPV-copy-number cervical-cancer cell lines. The main variations were observed for protein, nucleic acid, and lipid, and were confirmed by both mean spectra and PCA analysis. Notably, the application of multivariate partial-least-squares regression analysis, with HPV copy number as target, revealed that the dataset can be used to evaluate the degree of HPV infection on the basis of the spectral profile of the cells.

A study by Vargis et al. [38] used both cell lines and cytology samples to investigate the potential of Raman microspectroscopy to detect the presence of HPV. Classification accuracies of 89–93 % were achieved for discrimination between a HPV-negative normal-human-keratinocyte cell line (NHEK), a HPV-negative cervical-cancer cell line (C33a), and HPV-positive cervical-cancer cell lines (HeLa and SiHa). A classification accuracy of 98.5 % was achieved for discrimination between HPV-positive and HPV-negative cytology samples.

Raman spectroscopy for histopathology

As shown in Fig. 2, a typical normal cervical-tissue section contains different cell layers, for example stromal, basal and/or parabasal, and intermediate and/or superficial layers. Figure 5 shows parallel H&E-stained and unstained cervical-tissue sections and a four-cluster Raman spectral map showing three distinct layers. The Raman map was recorded using a HORIBA Jobin Yvon HR 800 (Villeneuve d’Ascq, France) Raman microscope equipped with a × 100 objective (MPlanN, Olympus, NA = 0.9) and a 785 nm laser source. Raman scattering was collected through a 100 μm confocal hole onto a Synapse air-cooled CCD detector for the range of 400–1800 cm−1 using a 300 lines mm−1 diffraction grating, yielding a dispersion of ~1.5 cm−1 per CCD pixel. The instrument was calibrated using the 520.7 cm−1 peak of silicon. Raman spectral mapping was performed using 2 × 15 s acquisitions with a step size of 18 μm. Spectral pre-processing was performed using Matlab software (Mathworks), including smoothing (Savitzky–Golay, k = 5; w = 13), baseline correction (rubber band), and vector normalisation. K-means cluster analysis was used to analyse the spectral data set from the Raman map. The blue cluster represents the stroma, the red cluster the basal and/or parabasal layer, the green cluster the intermediate and/or superficial layer, and the black cluster the substrate. Mean Raman spectra representing the different clusters are also shown in Fig. 5. The most distinctive Raman bands in the blue spectrum can be assigned to collagen (853 cm−1, 921 cm−1, 938 cm−1, and 1245 cm−1), a major component of the connective-tissue layer or stroma. The red spectrum shows distinctive bands from DNA bases, adenine (722 cm−1), thymine (755 cm−1), and cytosine (782 cm−1), and the green spectrum shows bands at 480 cm−1, 849 cm−1, and 938 cm−1 indicating the accumulation of glycogen in the intermediate and/or superficial layer.
Fig. 5

(a) H&E stained and (b) unstained normal cervical tissue showing stromal, basal, and intermediate and/or superficial layers; (c) Raman spectral map showing stromal (blue), basal (red), and intermediate and/or superficial (green) layers; and (d) mean Raman spectra from each representative layer

Krishna et al. studied formalin-fixed cervical tissues using both Raman and FTIR spectroscopy. Normal and malignant tissues could be distinguished by differences in protein, lipids, and nucleic-acid peaks and stronger amide III assignments, suggesting disordered, helical secondary-protein structure, in malignant conditions [39]. Formalin-fixed paraffin-preserved (FFPP) cervical-tissue sections were also investigated by Lyng et al. [40]. The underlying biochemical changes associated with cervical precancer and cancer were revealed to be caused by a reduction in glycogen and an increase in nucleic acids. The loss of differentiation, together with increased proliferation, in pre-cancer results in reduced levels of glycogen, because normal cervical cells accumulate glycogen as they mature. A Raman mapping study using frozen tissue sections revealed that Raman spectroscopy could distinguish normal cervical tissue from invasive cervical-cancer tissue, mainly on the basis of collagen bands and CH stretching bands [41]. Tan et al. [42] used Raman spectral mapping and hierarchical cluster analysis (HCA) to differentiate between normal squamous epithelium and CIN2 in FFPP tissue sections, and it was revealed that the Raman spectra associated with the CIN2 lesion clustered predominantly with those of the basal epithelial cells of the normal squamous epithelium, suggesting that the cells of these regions share common biochemical profiles. Spectral features responsible for their differentiation were associated with the amide I and amide III bands.

More recently, FFPP tissue sections from cervical biopsies classified as NILM, LSIL, or HSIL were analysed by Raman spectral mapping [43]. Together with K-means cluster analysis (KMCA), Raman mapping was able to differentiate the NILM cervical tissue into three layers including stroma, basal and/or parabasal, and superficial layers, characterised by spectral features of collagen, DNA bases, and glycogen, respectively. In the LSIL and HSIL samples, KMCA clustered regions of the superficial layer with the basal layer. Using PCA, biochemical changes associated with disease were also observed in normal areas of the abnormal samples, where morphological changes were not apparent, providing a clear indication of the potential of Raman spectroscopy to identify biochemical changes associated with the initial stages of the disease, rather than just the morphological changes associated with later-stage disease and current clinical diagnosis.

Raman spectroscopy for in-vivo applications

Mahadevan-Jansen et al. [44, 45] first revealed the potential of Raman spectroscopy for in-vivo detection of cervical cancer and pre-cancer and developed a fibre-optic probe for in-vivo measurements. Increases in phospholipids and DNA, ~1330, 1454, and 1650 cm−1, were associated with progression to high-grade dysplasia [44, 45]. Improvements in the overall classification accuracy of Raman spectroscopy from 88 % to 94 % were achieved by including information on menopausal status and menstrual cycle [46]. Similarly, consideration of disease history and proximity to dysplastic lesions was found to result in disease classification accuracy of 97 % [47]. Further studies investigated the effect of race, ethnicity, body mass index, parity, and socioeconomic status on Raman spectra from patients with a normal cervix, and concluded that only body mass index and parity resulted in significant variations of spectral profiles [48]. Their effect on dysplasia and cancer has not been assessed, however. The potential of high-wavenumber (2800–3700 cm−1) Raman spectroscopy for in-vivo detection of cervical pre-cancer has been investigated by Mo et al. and Duraipandian et al. [49, 50]. Significant differences in Raman bands of lipids at 2850 and 2885 cm−1, proteins at 2940 cm−1, and the broad Raman band of water at 3400 cm−1 were observed in normal and dysplastic cervical tissue, with a sensitivity of 93.5 % and specificity of 97.8 % achieved for identification of dysplasia [49]. Simultaneous fingerprint and high-wavenumber Raman spectroscopy has been revealed to outperform fingerprint or high-wavenumber Raman spectroscopy alone, resulting in a sensitivity of 85.0 %, specificity of 81.7 %, and overall diagnostic accuracy of 82.6 %. Raman spectral differences between normal and dysplastic cervical tissue were observed at 854, 937, 1001, 1095, 1253, 1313, 1445, 1654, 2946, and 3400 cm−1, mainly related to proteins, lipids, glycogen, nucleic acids, and water content in tissue [50].

Future perspectives

The potential of Raman spectroscopy as a truly label-free, objective, automatable diagnostic technique has been well established through numerous research studies of numerous pathologies, both in vivo and ex vivo. Disease diagnostics have long relied on visual differences in tissue appearance, aided in modern histopathology and cytology by optical stains and microscope technology. However, such approaches, based on changes in tissue and cell morphology, often reveal the later stages of disease development, rather than the underlying biochemical changes associated with disease onset or aetiology. Raman spectroscopy provides a signature or fingerprint of the biological sample, based on the biochemical constituents, and subtle changes to the composition associated with disease or external insult can be identified with high sensitivity with the aid of multivariate statistical analysis. As an optical technology, it can be applied microscopically or endoscopically, for ex-vivo or in-vivo applications, the former including cytopathology or histopathology. Signatures of viral infection can also be clearly identified, indicating that the technique could ultimately compete with costly viral screening programmes, notably with the same instrumentation and an integrated screening procedure.

Table 2 lists the advantages and disadvantages of current screening and/or diagnostic methods and Raman spectroscopy for cervical cancer. In cytology, the Pap smear test, and in histology, histochemical staining are well established and regarded as methods of choice. Any new disruptive technology, for example spectropathology, has an obvious relative disadvantage in that translation will require not only acceptance by the clinical community, but large-scale and costly clinical trials. Ultimately, although current cytological and histological methods require ex-vivo samples and are thus to some degree invasive, Raman-based spectropathology could be performed in-vivo in a completely non-invasive fashion. The principle advantage of spectroscopy-based techniques is that, once established, the technique is completely objective, based on disease and/or disease-aetiology-related changes in biochemical signatures, rather than relying on expert interpretation of morphological changes. This should minimise the risk of misdiagnosis resulting from variations in subjective human interpretation. Rapid analysis should also result in rapid turnaround to minimise patient anxiety. To date, much of the proof-of-concept research has been performed on high-specification, laboratory apparatus, requiring expert users. However, given the increasing availability of lower specification and cost, user-friendly Raman instruments, this technology could readily translate to clinical laboratories. Although the readout is a Raman spectrum, using pre-defined classification algorithms a non-specialist operator could receive an output such as a yes, no, or maybe (or red, orange, green) indicator for the presence of cancer or pre-cancer.
Table 2

Advantages and disadvantages of current screening and/or diagnostic methods and Raman spectroscopy


Pap test and/or cytology

HPV testing


Raman spectroscopy


Well accepted screening method

High specificity

High sensitivity

High negative predictive values

Well-accepted best-practice method

Definitive diagnosis of tumour stage

Stromal invasion can be determined

Tumour margins can be determined


Can be used by non-specialists with suitable diagnostic algorithms

Low operating costs because no reagents required; based on a fingerprint of the biochemical composition

Can be used ex vivo or in vivo

Can be used for cells and tissues

Can provide information on HPV status

High spatial resolution enabling subcelluar imaging



Low sensitivity

Requires clinical expertise and experience

Reagents and instrumentation required

Must be confirmed with colposcopy and histopathology


Low specificity

Reagents and instrumentation required

Time consuming

Different tests for HPV DNA and mRNA

Does not provide information about cellular morphology and/or abnormality



Requires clinical expertise and experience

Reagents and instrumentation required

Pre-malignant lesions difficult to distinguish from benign conditions

Inter-observer variations

Sampling errors


Instrumentation required

Long spectral-acquisition times

Multivariate data analysis needed to extract information from the spectral data

However, despite the obvious potential, vibrational spectroscopic techniques have had limited if any translation into the clinical environment. A critical assessment of the challenges facing the translation of spectroscopic methods to the clinic has recently been presented [51]. “Spectropathology for the Next Generation: Quo Vadis?” summarises the discussion sessions of the SPEC 2014 conference, the 8th in the series of biennial flagship conferences in the field, which were led by members of the International Advisory Board. Although the potential of the techniques has been well demonstrated in the research environment, and many of the technical challenges associated with sample presentation, data acquisition, processing, and analysis have been addressed, it is clear that the lack of studies of the scale of clinical relevance is an obstacle to their credibility and uptake. Widespread engagement of the medical community and commitment from instrument manufacturers, and the resources required for large-scale clinical trials, may depend on the availability of such large-scale studies. In this context, the identification of strategic, achievable target applications is recommended.

Because of the high throughput of established screening programmes, cervical cytopathology is potentially such a strategic target application. A crucial consideration is how the spectroscopic technique could fit into the current workflow. Notably, the Pap smear procedure introduces cytological stains which can interfere with the Raman acquisition, introducing substantial fluorescence background and/or photodegradation depending on the wavelength used. With automated spectral acquisition, however, the implementation could be similar to the Thinprep Imager (Hologic) or the Focal Point GS Imaging system (BD), which use image-processing algorithms to automatically review liquid-based cytology Thinprep and SurePath slides. Raman spectroscopy is reagent free, so costs should be favourable when compared with the imaging systems or with manual scoring where personnel costs could be reduced. A recent study by our group [35] has revealed that glass ThinPrep slides can be used for Raman spectral recording in place of spectroscopic substrates, for example calcium fluoride substrates. These substrates reduce the presence of confounding contributions of the substrate, but they are substantially more expensive than the glass slides used in the cytopathology laboratory.

Notably, the same data acquisition and screening procedure can be used for identification of HPV infection, because the technique is based on biochemical signatures, and thus, with appropriately trained spectral databases and data-mining procedures, the spectral profiling could provide an integrated analysis of health status and risk of developing HSIL or cervical cancer, combining the advantages of the currently used cytological and HPV screening standards with higher sensitivity, specificity, and throughput.

Similar potential advantages of Raman-spectroscopic approaches for histopathology can be identified, although it is recognised that current mapping and/or imaging times of large areas of tissue followed by current pre and post-data-processing procedures need to be improved [52]. Although substantial progress has been made, there is much to be done in terms of standardising procedures and protocols. The demands on the ability to rapidly scan large areas of tissue probably currently favour the use of FTIR rather than Raman spectroscopy for such applications. In terms of tissue-processing procedures, there remains much debate, although consistency with current clinical practice probably favours the use of FFPP tissue samples. Notably, analyses of archived tissue libraries may add much to understanding of disease progression and patient prognosis. It has been revealed that it is not necessary to remove the paraffin to obtain usable spectral information [53]. Leaving the paraffin in place reduces scattering artefacts and effects of further variable removal of aromatic solvent soluble components. However, it may be argued that greater consistency of spectral information is achieved when sections are deparaffinised. Deparaffinising also enables post-staining of the sections, although it has been revealed that the efficiency of the deparaffinisation process can depend on the tissue pathology [23].

In-vivo applications are reliant on the further development of spectroscopic probes, which have already been used for cervical applications [44, 45, 46, 47, 48, 49, 50]. Raman spectroscopy could be a potential candidate for an adjunct tool for “screen and treat” approaches for low-resource countries. Patients are screened by visual inspection during colposcopy and treated immediately by cryotherapy if required. Raman spectroscopy could be used to improve the poor sensitivity of colposcopy in identifying cervical cancer and pre-cancerous lesions.

However, much work still remains before these techniques could be translated into standard clinical practice. Many studies have used relatively small sample sizes, and as such may be biased. Validation in large multi-centre studies is needed, using real-world cytopathology and histopathology samples from screening programmes and colposcopy clinics. Large-scale clinical trials are needed to obtain the large volumes of data necessary for the development of robust classification algorithms.

If spectroscopy can be shown to be as good as, or better than, the methods of choice in current use, then there is great potential for these techniques to be used as an alternative or an adjunct to the current methods. The advantages would be higher accuracy, higher throughput, and reduced workload for the cytologist and/or pathologist, and higher accuracy and chance of earlier detection for the patient. It is important, however, to establish standard operating procedures, through such networks as the UK EPSRC Network CLIRSPEC ( and the EU COST Action Raman4Clinics (, to engage spectroscopic-instrument and medical-device industries to optimise data-collection efficiencies, and to actively engage the medical and clinical communities to encourage uptake and translation of the technology.



The authors acknowledge funding from Enterprise Ireland co-funded by the European Regional Development Fund (ERDF) and Ireland’s EU Structural Funds Programme 2007–2013, CF2011 1045, the Health Research Board Collaborative Applied Research Grant, CARG2012/29, and Dublin Institute of Technology Fiosraigh Research Excellence Award.


  1. 1.
    Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2012) GLOBOCAN, v1.0, Cancer incidence and mortality worldwide: IARC Cancer Base No. 11, 2013. International Agency for Research on CancerGoogle Scholar
  2. 2.
    Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, Snijders PJ, Peto J, Meijer CJ, Munoz N (1999) Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol 189:12–19CrossRefGoogle Scholar
  3. 3.
    Franco EL, Schlecht NF, Saslow D (2003) The epidemiology of cervical cancer. Cancer J 9:348–359CrossRefGoogle Scholar
  4. 4.
    Koss LG, Melamed MR (2006) Koss’ diagnostic cytology and its histopathologic bases, 5th edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  5. 5.
    Kurman RJ, Solomon D (1994) The Bethesda system for reporting cervical/vaginal cytologic diagnoses: definitions, criteria and explanatory notes for terminology and specimen adequacy. Springer Verlag, New YorkCrossRefGoogle Scholar
  6. 6.
    Herbert A, Bergeron C, Wiener H, Schenck U, Klinkhamer P, Bulten J et al (2007) European guidelines for quality assurance in cervical cancer screening: recommendations for cervical cytology terminology. Cytopathology 18:213–219CrossRefGoogle Scholar
  7. 7.
    Kitchener HC, Blanks R, Cubie H, Desai M, Dunn G, Legood R, Gray A, Sadique Z, Moss S, MAVARIC Trial Study Group (2011) MAVARIC—A comparison of automation-assisted and manual cervical screening: a randomised controlled trial. Health Technol Assess 15:1–170CrossRefGoogle Scholar
  8. 8.
    Schiffman M, Wentzensen N, Wacholder S, Kinney W, Gage JC, Castle PE (2011) Human papillomavirus testing in the prevention of cervical cancer. J Natl Cancer Inst 103:368–383CrossRefGoogle Scholar
  9. 9.
    Ronco G, Giorgi-Rossi P, Carozzi F, Confortini M, Dalla Palma P, Del Mistro A et al (2010) Efficacy of human papillomavirus testing for the detection of invasive cervical cancers and cervical intraepithelial neoplasia: a randomised controlled trial. Lancet Oncol 11:249–257CrossRefGoogle Scholar
  10. 10.
    Cuzick J, Cadman L, Mesher D, Austin J, Ashdown-Barr L, Ho L et al (2013) Comparing the performance of six human papillomavirus tests in a screening population. Br J Cancer 108:908–913CrossRefGoogle Scholar
  11. 11.
    Castle PE, de Sanjosé S, Qiao YL, Belinson JL, Lazcano-Ponce E, Kinney W (2012) Introduction of human papillomavirus DNA screening in the world: 15 years of experience. Vaccine 20(30 Suppl 5):F117–F122CrossRefGoogle Scholar
  12. 12.
    Dalla Palma P, Giorgi Rossi P, Collina G, Buccoliero AM, Ghiringhello B, Lestani M et al (2008) The risk of false-positive histology according to the reason for colposcopy referral in cervical cancer screening: a blind revision of all histologic lesions found in the NTCC trial. Am J Clin Pathol 129:75–80CrossRefGoogle Scholar
  13. 13.
    Stoler MH, Schiffman M (2001) Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study. JAMA 285:1500–1505CrossRefGoogle Scholar
  14. 14.
    Parker MF, Zahn CM, Vogel KM, Olsen CH, Miyazawa K, O'Connor DM (2002) Discrepancy in the interpretation of cervical histology by gynecologic pathologists. Obstet Gynecol 100:277–280CrossRefGoogle Scholar
  15. 15.
    Grenko RT, Abendroth CS, Frauenhoffer EE, Ruggiero FM, Zaino RJ (2000) Variance in the interpretation of cervical biopsy specimens obtained for atypical squamous cells of undetermined significance. Am J Clin Pathol 114:735–740CrossRefGoogle Scholar
  16. 16.
    Diem M (1993) Introduction to modern vibrational spectroscopy. Wiley, New YorkGoogle Scholar
  17. 17.
    Diem M, Mazur A, Lenau K, Schubert J, Bird B, Miljkovic M, Krafft C, Popp J (2013) Molecular pathology via IR and Raman spectral imaging. J Biophotonics 6:855–886CrossRefGoogle Scholar
  18. 18.
    Ellis DI, Cowcher DP, Ashton L, O'Hagan S, Goodacre R (2013) Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool. Analyst 138:3871–3884CrossRefGoogle Scholar
  19. 19.
    Kendall C, Isabelle M, Bazant-Hegemark F, Hutchings J, Orr L, Babrah J, Baker R, Stone N (2009) Vibrational spectroscopy: a clinical tool for cancer diagnostics. Analyst 134:1029–1045CrossRefGoogle Scholar
  20. 20.
    Nijssen A, Koljenovic S, Bakker Schut TC, Caspers PJ, Puppels GJ (2009) Towards oncological application of Raman spectroscopy. J Biophotonics 2:29–36CrossRefGoogle Scholar
  21. 21.
    Movasaghi Z, Rehman S, Rehman IU (2007) Raman spectroscopy of biological tissues. Appl Spectrosc Rev 42:493–541CrossRefGoogle Scholar
  22. 22.
    Rashid N (2013) Raman microspectroscopy for the characterization of cervical cancer, PhD thesis, Dublin Institute of TechnologyGoogle Scholar
  23. 23.
    Fullwood LM, Griffiths D, Ashton K, Dawson T, Lea RW, Davis C, Bonnier F, Byrne HJ, Baker MJ (2014) Effect of substrate choice and tissue type on tissue preparation for spectral histopathology by Raman microspectroscopy. Analyst 139:446–454CrossRefGoogle Scholar
  24. 24.
    Kerr LT, Byrne HJ, Hennelly BM (2015) Optimal choice of sample substrate and laser wavelength for Raman spectroscopic analysis of biological specimens. Anal Methods (accepted)Google Scholar
  25. 25.
    Fung Kee Fung M, Senterman M, Eid P, Faught W, Mikhael NZ, Wong PT (1997) Comparison of Fourier-transform infrared spectroscopic screening of exfoliated cervical cells with standard Papanicolaou screening. Gynecol Oncol 66:10–15CrossRefGoogle Scholar
  26. 26.
    Neviliappan S, Fang Kan L, Tiang Lee Walter T, Arulkumaran S, Wong PTT (2002) Infrared spectral features of exfoliated cervical cells, cervical adenocarcinoma tissue, and an adenocarcinoma cell line (SiSo). Gynecol Oncol 85:170–174CrossRefGoogle Scholar
  27. 27.
    Wong PT, Wong RK, Caputo TA, Godwin TA, Rigas B (1991) Infrared spectroscopy of exfoliated human cervical cells: evidence of extensive structural changes during carcinogenesis. Proc Natl Acad Sci U S A 1991(88):10988–10992CrossRefGoogle Scholar
  28. 28.
    Wong PTT, Senterman MK, Jackli P, Wong RK, Salib S, Campbell CE, Feigel R, Faught W, Fung Kee Fung M (2002) Detailed account of confounding factors in interpretation of FTIR spectra of exfoliated cervical cells. Biopolymers 67:376–386CrossRefGoogle Scholar
  29. 29.
    Chiriboga L, Xie P, Vigorita V, Zarou D, Zakim D, Diem M (1998) Infrared spectroscopy of human tissue. II. A comparative study of spectra of biopsies of cervical squamous epithelium and of exfoliated cervical cells. Biospectroscopy 4:55–59CrossRefGoogle Scholar
  30. 30.
    Cohenford MA, Godwin TA, Cahn F, Bhandare P, Caputo TA, Rigas B (1997) Infrared spectroscopy of normal and abnormal cervical smears: evaluation by principal component analysis. Gynecol Oncol 66:59–65CrossRefGoogle Scholar
  31. 31.
    Diem M, Chiriboga L, Lasch P, Pacifico A (2002) IR spectra and IR spectral maps of individual normal and cancerous cells. Biopolymers 67:349–353CrossRefGoogle Scholar
  32. 32.
    Romeo MJ, Quinn MA, Burden FR, McNaughton D (2002) Influence of benign cellular changes in diagnosis of cervical cancer using IR microspectroscopy. Biopolymers 67:362–366CrossRefGoogle Scholar
  33. 33.
    Wood BR, Quinn MA, Tait B, Ashdown M, Hislop T, Romeo M, McNaughton D (1998) FTIR microspectroscopic study of cell types and potential confounding variables in screening for cervical malignancies. Biospectroscopy 4:75–91CrossRefGoogle Scholar
  34. 34.
    Rubina S, Amita M, Kedar KD, Bharat R, Krishna CM (2013) Raman spectroscopic study on classification of cervical cell specimens. Vib Spectrosc 68:115–121CrossRefGoogle Scholar
  35. 35.
    Bonnier F, Traynor D, Kearney P, Clarke C, Knief P, Martin C, O’Leary JJ, Byrne HJ, Lyng F (2014) Processing ThinPrep cervical cytological samples for Raman spectroscopic analysis. Anal Methods 6:7831–7841CrossRefGoogle Scholar
  36. 36.
    Jess PRT, Simth DDW, Mazilu M, Dholakia K, Riches AC, Herrington CS (2007) Early detection of cervical neoplasia by Raman spectroscopy. Int J Cancer 121:2723–2728CrossRefGoogle Scholar
  37. 37.
    Ostroswka KM, Malkin A, Meade A, O’Leary J, Martin C, Spillane C, Byrne HJ, Lyng FM (2010) Investigation of the influence of high-risk human papillomavirus on the biochemical composition of cervical cancer cells using vibrational spectroscopy. Analyst 135:3087–3093CrossRefGoogle Scholar
  38. 38.
    Vargis E, Tang Y-W, Khabele D, Mahadevan-Jansen A (2012) Near-infrared Raman microspectroscopy detects high-risk human papillomaviruses. Transl Oncol 5:172–179CrossRefGoogle Scholar
  39. 39.
    Krishna CM, Sockalingum GD, Vadhiraja BM, Maheedhar K, Rao ACK, Rao L, Venteo L, Plutot M, Fernandes DJ, Vidyasagar MS, Kartha BVB, Manfait M (2006) Vibrational spectroscopy studies of formalin-fixed cervix tissues. Biopolymers 85:214–221CrossRefGoogle Scholar
  40. 40.
    Lyng FM, Faolain EO, Conroy J, Meade AD, Knief P, Duffy B, Hunter MB, Byrne JM, Kelehan P, Byrne HJ (2007) Vibrational spectroscopy for cervical cancer pathology, from biochemical analysis to diagnostic tool. Exp Mol Pathol 82:121–129CrossRefGoogle Scholar
  41. 41.
    Kamemoto LE, MisRa AK, Sharma SK, Goodman MT, Luk H, Dykes AC, Acosta T (2010) Near-infrared micro-Raman spectroscopy for in vitro detection of cervical cancer. Appl Spectrosc 64:255–261CrossRefGoogle Scholar
  42. 42.
    Tan KM, Herrington CS, Brown CT (2011) Discrimination of normal from pre-malignant cervical tissue by Raman mapping of de-paraffinized histological tissue sections. J Biophotonics 4:40–48CrossRefGoogle Scholar
  43. 43.
    Rashid N, Nawaz H, Poon KW, Bonnier F, Bakhiet S, Martin C, O'Leary JJ, Byrne HJ, Lyng FM (2014) Raman microspectroscopy for the early detection of pre-malignant changes in cervical tissue. Exp Mol Pathol 97:554–564CrossRefGoogle Scholar
  44. 44.
    Mahadevan-Jasen A, Mitchel MF, Ramanujam N, Utzinger U, Richards-Kortum R (1998) Development of a fiber optic probe to measure NIR Raman spectra of cervical tissue in vivo. Photochem Photobiol 68:427–431CrossRefGoogle Scholar
  45. 45.
    Utzinger U, Heintzelman DL, Mahadevan-Jasen A, Malpica A, Folen M, Richards-Kortum R (2001) Near-infrared Raman spectroscopy for in vivo detection of cervical precancers. Appl Spectrosc 55:959–995CrossRefGoogle Scholar
  46. 46.
    Kanter EM, Majumder S, Kanter GJ, Woeste E, Mahadevan-Jasen A (2009) Effect of hormonal variation on Raman spectra for cervical disease detection. Am J Obstet Gynecol 200:512, e1–5 CrossRefGoogle Scholar
  47. 47.
    Vargis E, Kanter EM, Majumder S, Keller MD, Beaven RB, Rao GG, Mahadevan-Jasen A (2011) Effect of normal variations on disease classification of Raman spectra from cervical tissue. Analyst 136:2981–2987CrossRefGoogle Scholar
  48. 48.
    Vargis E, Byrd T, Logan Q, Khabele D, Mahadevan-Jansen A (2011) Sensitivity of Raman spectroscopy to normal patient variability. J Biomed Opt 16:117004CrossRefGoogle Scholar
  49. 49.
    Mo J, Zheng W, Low JJH, Ng J, Ilancheran A, Huang Z (2009) High wavenumber Raman spectroscopy for in vivo detection of cervical dysplasia. Anal Chem 81:8908–8915CrossRefGoogle Scholar
  50. 50.
    Duraipandian S, Zheng W, Ng J, Low JJH, Ilancheran A (2012) Simultaneous fingerprint and high-wavenumber confocal Raman spectroscopy enhances early detection of cervical precancer in vivo. Anal Chem 84:5913–5919CrossRefGoogle Scholar
  51. 51.
    Byrne HJ, Baranska M, Puppels GJ, Stone N, Wood B, Gough KM, Lasch P, Heraud P, Sulé-Suso J, Sockalingum GD (2015) Spectropathology for the next generation: Quo Vadis? Analyst 140:2066–2073CrossRefGoogle Scholar
  52. 52.
    Bassan P, Sachdeva A, Shanks J, Brown MD, Clarke NW, Gardner P (2013) Whole organ cross-section chemical imaging using label-free mega-mosaic FTIR microscopy. Analyst 138:7066–7069CrossRefGoogle Scholar
  53. 53.
    Tfayli A, Gobinet C, Vrabie V, Huez R, Manfait M, Piot O (2009) Digital dewaxing of Raman signals: discrimination between nevi and melanoma spectra obtained from paraffin-embedded skin biopsies. Appl Spectrosc 63:564–570CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Fiona M. Lyng
    • 1
    • 2
  • Damien Traynor
    • 1
    • 2
  • Inês R. M. Ramos
    • 1
    • 2
  • Franck Bonnier
    • 3
    • 4
  • Hugh J. Byrne
    • 3
  1. 1.DIT Centre for Radiation and Environmental ScienceFOCAS Research Institute, Dublin Institute of TechnologyDublin 8Ireland
  2. 2.School of PhysicsDublin Institute of TechnologyDublin 8Ireland
  3. 3.FOCAS Research InstituteDublin Institute of TechnologyDublin 8Ireland
  4. 4.Faculty of Pharmacy, EA 6295 Nanomédicaments et NanosondesUniversité François-Rabelais de ToursToursFrance

Personalised recommendations