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Raman Spectroscopy for Early Detection of Cervical Cancer: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Rubina Shaikh.

Cervical cancer is the fourth most common women’s cancer in the world, and unfortunately mainly affects younger women. Current methods for screening and diagnosis of cervical cancer and precancer are therefore limited, and there has been much interest in the use of optical spectroscopic approaches, such as Raman spectroscopy, to provide an objective test based on the biochemical fingerprint of the cervical cells or tissues. Raman spectroscopy is based on inelastic scattering, which has been used to study the biomolecular fingerprint of cells or tissues. It involves shining a laser on a sample and measuring the scattered photons. When a photon collides on a molecule, it either retains its energy (known as Rayleigh scattering) or exchanges energy with the molecule (known as Raman scattering).

  • cancer screening
  • human papillomavirus
  • cervical cancer
  • Raman spectroscopy

1. Introduction

1.1. Cervical

According to GLOBOCAN [1], cervical cancer is the fourth most common cancer in women worldwide in terms of incidence and mortality, with 604,127 new cases and 341,831 deaths in 2020 [1]. Persistent infection with high-risk human papillomavirus (HPV) is accepted as the major cause of the development of cervical cancer and precancer [2]. Over 100 different types of HPV have been identified, and 14 are considered to be high-risk HPV types (hrHPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68) [3]. Smoking, immunosuppression, long-term use of oral contraceptives, and socioeconomic status are also known risk factors for cervical cancer [4].
In 2018, the World Health Organisation launched a global initiative to eliminate cervical cancer through three main actions: HPV vaccination, cervical cancer screening, and effective treatment [5]. At present, three HPV vaccines are available: (1) a bivalent vaccine targeting high-risk HPV16 and HPV18 (accounting for ~70% of cervical cancer cases), (2) a quadrivalent vaccine targeting HPV16, HPV18, and low-risk HPV6 and HPV11, and (3) a nonavalent vaccine targeting HPV16, HPV18, HPV6, HPV11, and five other high-risk types, HPV31, HPV33, HPV45, HPV52, and HPV58 (accounting for another 20% of cervical cancer cases) [6]. Despite these vaccines having excellent efficacy against cervical precancer lesions [6[6][7],7], vaccination levels are low in low- and middle-income countries, which carry the burden of 80% of global cancer cases [8]. Moreover, high-quality cervical screening programmes are still essential, as HPV vaccination does not protect against all high-risk HPV types [9].

1.2. Cervical Cancer Screening and Diagnosis

For decades, cytology was used for primary cervical screening, but recently, HPV testing has replaced cytology in many countries because HPV testing has a higher sensitivity than cytology for the detection of high-grade cervical precancer (cervical intraepithelial neoplasia (CIN)2+) [10]. Additional triage tests are required, however, for HPV-positive women, because HPV testing has a lower specificity than cytology [11]. Cytology triage is currently utilised to reduce over-referral to colposcopy and overtreatment of HPV-positive women.
Following an abnormal Pap test (HPV test with cytology triage), a woman will be sent for colposcopy to visualise the cervix followed by a tissue biopsy and histopathology. Histopathology is currently regarded as the ‘gold standard’ test for the diagnosis of cancer but the grading characteristics can be subjective and precancer may not be visually perceptible. Current methods for screening and diagnosis of cervical cancer and precancer are therefore limited, and there has been much interest in the use of optical spectroscopic approaches, such as Raman spectroscopy, to provide an objective test based on the biochemical fingerprint of the cervical cells or tissues.

1.3. Raman Spectroscopy

Raman spectroscopy is based on inelastic scattering, which has been used to study the biomolecular fingerprint of cells or tissues [12]. It involves shining a laser on a sample and measuring the scattered photons. When a photon collides on a molecule, it either retains its energy (known as Rayleigh scattering) or exchanges energy with the molecule (known as Raman scattering). The resulting Raman spectra are plots of scattered intensity versus the energy difference between incident and scattered photons, and exhibit changes in the vibrational modes of the molecules. Raman spectra are characterized by shifts in wavenumbers (inverse of wavelength in cm−1) from the incident frequency. The frequency difference between incident and scattered Raman photons is termed the Raman shift, which is unique to any given molecule.

1.4. Evolution of Raman Spectroscopy and Its Application in Cervical Cancer

During a sea voyage from India to England in 1921, the Indian Physicist C.V. Raman conducted experiments, later submitted to Nature as a letter called ‘The Colour of the Sea’ [13] (Figure 1). A later series of experiments on the scattering of light in different liquids led to the discovery of the inelastic scattering effect named after Raman in 1928 [14], and this discovery was awarded the Nobel Prize in Physics in 1930. One of the known limitations of Raman spectroscopy was, for example, a weak scattering [12]: typically 1 in 108 of the incident light undergoes spontaneous Raman scattering. Additional limitations were long spectral acquisition time, background fluorescence, and interference from silica in fibre optics [12]. Many of these limitations were overcome by the invention of lasers in the 1960s, and fibre-optic probes and CCD in the 1980s [12].
Molecules 28 02502 g001
Figure 1. Major events in cervical cancer screening and Raman spectroscopy (blue flags are the event in Cervical cancer screening; orange flags are the events in technological advancement in Raman spectroscopy).
In parallel with the technological advances in the field of Raman spectroscopy, there was development in the field of cervical cancer and its screening methods. For example, Dr Papanicolaou and Dr Herbert collaborated on the diagnosis of uterine cancer using the vaginal smear in August 1943 [15], and the current cervical screening has evolved from this cervical screening test, popularly known as the Papanicolaou (Pap) test [16] (Figure 1). Later, the human papillomavirus virus (HPV) was linked to cervical cancer, leading to the Nobel Prize [17]. Nationwide screening using the Pap test was implemented in 1990 in developed nations, resulting in a >70% decline in both the incidence and mortality of cervical cancer [18].
In the 1990s, technological advancements in cytology led to the development of liquid-based cytology (LBC), which was shown to be better quality than the conventional Pap test [16]. Multiple studies have exhibited a significant impact of LBC in decreasing the incidence of cervical cancer [19]. The 2000s saw the emergence of a HPV vaccine [20] and other revolutions, such as co-testing to reduce the cervical cancer burden [21]. In recent years, HPV-based screening with cytology has been implemented in the Netherlands, Germany, Italy, the UK, and Ireland [22,23][22][23]. Contrary to this, developing nations struggle to have national-level cervical screening programmes, either due to a lack of infrastructure or trained histopathologists [22]. To date, cervical cancer remains a major global challenge.

2. Cytology

Table 1 summarises the studies on applying Raman spectroscopy in cervical cytology.

2.1. Cell Pellets

Vargis et al. [31][24] demonstrated that Raman spectroscopy could detect the presence of high-risk HPV in cytology samples. Spectral differences were observed in regions corresponding to lipid, amino acid, and deoxyribonucleic acid (DNA) content and CH stretching and bending regions assigned to proteins. HPV-positive and HPV-negative cytology samples were discriminated against with an accuracy of 98.5%. Rubina et al. [32][25] used Raman spectroscopy to discriminate between exfoliated cell pellets from patients with negative cytology and cervical cancer cytology. Classification efficiency of ~90% was achieved using principal component analysis-linear discriminant analysis (PCA-LDA) [33][26], but heme and fibrin bands from blood appeared to be major discriminating features. A subset of cell pellets was treated with red blood cell lysis buffer before Raman spectroscopy. Successful blood removal was confirmed by the absence of heme and fibrin bands. The main discriminating feature was increased protein content in the cervical cancer samples compared to the negative samples. A classification efficiency of ~80% was achieved using PCA-LDA. Misclassifications were attributed to sample heterogeneity and the predominance of normal cells in the cervical cancer samples. This work was extended by Hole et al. [34][27] to Raman analysis of cervical and oral exfoliated cell pellets. The main discriminating features were DNA and protein, and improved classification was achieved when mean spectra from each sample were used to overcome intra-sample heterogeneity (84% cervical and 86% oral cancer) compared to all spectra from each sample (77% cervical and 82% oral cancer).
Table 1.
Application of Raman spectroscopy in cervical cytology.
Year Authors (Research Group) Sample/Patient Numbers Sample Type/Sample Prep/Substrate Raman Parameters Data Analysis Methodology Main Findings Reference
2012 Vargis et al. (Mahadevan-Jansen group) 50 patient samples—25 HPV negative and 25 HPV positive Centrifugation and washing with sterile water

Pellets of exfoliated cells on

calcium fluoride slides		 Renishaw Invia Raman microscope

785 nm laser, ∼30 mW at the sample

50X/0.75 NA objective lens

Spectral resolution ∼6 cm−1

45 to 60 s acquisition, 3

accumulations		 Sparse multinomial logistic regression (SMLR) Discrimination of HPV-positive and HPV-negative cytology samples

Spectral differences: lipid, amino acid, protein, and DNA content

Accuracy: 98.5%		 [31][24]
2013 Rubina et al. (Krishna group) 94 patient samples—45 negative cytology and 49 cervical cancer cytology Centrifugation and washing with saline

Pellets of exfoliated cells on

calcium fluoride slides

Red blood cell (RBC) lysis buffer treatment		 Horiba-

Jobin-Yvon fibre-optic Raman microprobe system

785 nm laser

∼40 mW at the sample

40X/0.65 NA objective lens

Spectral resolution ∼4 cm−1

6 s acquisition, 3

accumulations		 Principal Component Analysis Linear Discriminant Analysis (PCA-LDA) Treatment of cell pellet with RBC lysis buffer to remove blood contamination

Discrimination of negative and cancer cytology

Spectral differences: protein

Accuracy: 80%		 [32][25]
2014 Bonnier et al. (Lyng group) 63 patient samples—50 negative cytology and 13 high-grade cytology ThinPrep method

Single exfoliated cells on

ThinPrep glass slides

Hydrogen peroxide pre-treatment		 Horiba-

Jobin-Yvon XploRA Raman microscope

532 nm laser

∼8 mW at the sample

100X/0.9 NA objective lens

Spectral resolution ∼3 cm−1

10 s acquisition, 3 accumulations		 PCA Pre-treatment of ThinPrep slides with hydrogen peroxide to eliminate variability due to blood contamination

Discrimination of negative and high-grade cytology

Spectral differences: DNA, RNA		 [35][28]
2016 Ramos et al. (Lyng group) 166 patient samples—88 negative cytology, 35 low-grade cytology, and 43 high-grade cytology Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al., 30 s acquisition, 2 accumulations PCA-LDA Discrimination of negative, low-grade, and high-grade cytology

Spectral differences: lipids, nucleic acids, and proteins

Sensitivity: 90.91–100% Specificity: 97.24–100%		 [36][30]
2017 Kearney et al. (Lyng group) 80 patient samples—30 negative cytology, 50 high-grade cytology Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al., 30 s acquisition, 2 accumulations PCA-Factorial Discriminant Analysis (FDA) Raman spectral signatures of superficial, intermediate, and parabasal cells

High variability in spectra from cytoplasm due to glycogen

Discrimination of negative and high-grade cytology

Spectral differences: lipids, nucleic acids and proteins

Sensitivity: 92%, Specificity: 97%		 [37][31]
2018 Traynor et al. (Lyng group) 60 patient samples—45 negative cytology and 15 high-grade cytology Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al., 30 s acquisition, 2 accumulations PLSDA Biochemical changes due to high-grade cytology more pronounced than hormone related changes

Discrimination of negative and high-grade cytology

Spectral differences: glycogen, nucleic acids, and proteins

Sensitivity: 96–98%, Specificity: 97–98%		 [38][32]
2018 Duraipandian et al. (Lyng group) 35 patient samples—18 negative cytology and 17 high-grade cytology Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al., 30 s acquisition, 2 accumulations PCA-LDA and PLSDA Discrimination of negative and high-grade cytology (morphologically normal superficial and intermediate cells)

Spectral differences: glycogen, nucleic acids, and proteins

Sensitivity: 75.6% (PCA-LDA), 96.1% (PLSDA)

Specificity: 84.5% (PCA-LDA), 93.5% (PLSDA)		 [39][33]
2018 Traynor et al. (Lyng group) 30 patient samples—15 negative cytology and 15 high-grade cytology Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al.

30 s acquisition, 2 accumulations		 PLSDA Pre-treatment of ThinPrep vial with hydrogen peroxide to remove excessive blood contamination (blood scale index 2–3)

Discrimination of negative and high-grade cytology (morphologically normal superficial and intermediate cells)

Spectral differences: glycogen, nucleic acids, and proteins

Sensitivity: 82–92%, Specificity: 87–93%		 [40][34]
2018 Hole et al. (Krishna group) 66 patient samples—28 negative cytology and 38 cervical cancer cytology Centrifugation and washing with saline

Pellets of exfoliated cells on

calcium fluoride slides

Red blood cell (RBC) lysis buffer treatment		 Horiba-Jobin-Yvon fibre-optic Raman microprobe system

785 nm laser

∼40 mW at the sample

40X/0.65 NA objective lens

Spectral resolution ∼4 cm−1

15 s acquisition time, 3

accumulations		 PCA-LDA Discrimination of negative and cancer cytology

Spectral differences: DNA, protein

Accuracy: 84%		 [34][27]
2019 Aljouch et al. (El-Mashtoly/Gerwert group) 30 patient samples—10 negative, 10 low-grade and 10 high-grade cytology Single exfoliated cells prepared using a cytospin centrifuge onto quartz slides WITec Raman microscope

532 nm laser

60X/1.0 NA objective lens, water immersion		 Deep convolutional neural networks (DCNN) Raman imaging of single exfoliated cells

Discrimination of negative, low-grade, and high-grade cytology

Spectral differences: lipids, proteins, polysaccharides, and nucleic acids

Accuracy: 94–100%		 [41][35]
2020 X. Zheng et al.

(Wu group)		 63 patient samples

33-normal

30- HR-HPV		 5 µL of preserved cell samples on aluminum foil, dried at room temperature LabRam HR evolution with 532 nm laser source was focused using 50X objective PCA-LDA The authors observed the diagnostic accuracy of 99.4% [42][36]
2020 Sitarz et al.

(Kaczor group)		 96 patient samples––negative, low-grade, high-grade, and cancer cytology and HPV- and HPV+ Single exfoliated cells fixed with 2.5% glutaraldehyde, washed in PBS, and placed on calcium fluoride slides WITec Raman microscope

532 nm laser ∼28 mW at the sample

63X/1.0 NA objective lens, water immersion

Spectral resolution ∼3 cm−1		 Cluster analysis (CA) Increased glycogen metabolism with HPV infection—for cells with large nuclear diameter, glycogen decreased in HPV positive compared to HPV negative samples [43][37]
2020 Karunakaran et al. (Maiti group) 124 patient samples comprising 47 negative, 41 high-grade, and 36 cancer cytology Single exfoliated cells, cell pellets and extracted DNA incubated with gold nanoparticles (AuNPs, 40–45 nm) on glass slide WITec Raman microscope

633 nm laser

20X objective lens		 Support vector machines (SVM) Discrimination of negative, high-grade, and cancer cytology

Spectral differences—nucleic acids and amino acids

Accuracy: 94.46% (single exfoliated cells), 71.6% (cell pellets) and 97.72% (extracted DNA)		 [44][38]
2021 Karunakaran et al. (Maiti group) 9 patient samples comprising 3 negative, 3 high-grade, and 3 cancer cytology Density gradient centrifugation

Single exfoliated cells on glass slide

Incubation with SERS nanotag for 45 min		 WITec Raman microscope

633 nm laser

10 mW power

20X objective lens

Spectral resolution ∼1 cm−1

10 s acquisition, 3

accumulations		 n/a SERS detection of cervical cancer biomarkers p16 and Ki67 in single exfoliated cells [45][39]
2021 Sitarz et al. (Kaczor group) 63 patient samples comprising negative, low-grade, high-grade, and cancer cytology and HPV- and HPV+ Sample preparation as for Sitarz et al. [36][30] Raman set up as for Sitarz et al. K means cluster analysis (KMCA) Dual switch of lipid metabolism—decreased lipid in low-grade cytology and increased lipid in high-grade and cancer cytology compared to negative cytology [46][40]
2021 Traynor et al. (Lyng group) 60 patient samples for training set—30 HPV DNA positive, mRNA negative and 30 HPV DNA positive, mRNA positive

14 blinded patient samples for test set		 Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al.

30 s acquisition, 2

accumulations		 PLSDA Discrimination of transient and transforming HPV infections (morphologically normal superficial and intermediate cells)

Spectral differences: glycogen, nucleic acids and proteins

Accuracy: 93%		 [47][41]
2022 Traynor et al. (Lyng group) 662 patient samples for training set—326 negative cytology, 200 low-grade cytology and 136 high-grade cytology

69 blinded patient samples for test set		 Sample preparation as for Bonnier et al. [28][29] Raman set up as for Bonnier et al.

30 s acquisition, 2

accumulations		 PLSDA Discrimination of negative, CIN1, and CIN2+ samples (morphologically normal superficial and intermediate cells)

Spectral differences: glycogen, nucleic acids, and proteins

Accuracy: 91.3%		 [48][42]
n/a: not applicable; no multivariate analysis was carried out in the study.

2.2. Single Exfoliated Cells

In contrast to earlier work on cellular pellets, Bonnier et al. [35][28] presented a new method for recording Raman spectra from cervical cytology samples prepared as single exfoliated cells using the ThinPrep liquid-based cytology method. Pre-treatment of the slides with a hydrogen peroxide solution to clear blood residue contamination before Raman recording was shown to significantly minimize variability in the spectral data. Features related to the DNA/RNA content of the cells contributed to the discrimination of spectra from negative cytology samples and high-grade cytology samples. This pre-treatment method was later adapted to Thinprep specimens with excessive blood contamination (blood scale index 2–3) by adding hydrogen peroxide directly to the vial before slide preparation [38][32]. In this study on Raman spectra from negative cytology and high-grade cytology samples after pre-treatment, glycogen, nucleic acids, and proteins were found to be the main discriminating features regardless of whether the samples had minimal blood contamination (blood scale index 0) or excessive blood contamination (blood scale index 2–3). Traynor et al. [25][43] published a protocol for Raman spectral cytopathology for use on liquid-based cytology samples prepared onto glass slides. This protocol covered sample preparation, spectral acquisition, pre-processing, and data analysis, and it included methods of correction of the glass spectral contribution and sample pre-treatment methods to remove contaminants, such as blood and mucus. Using the same protocol, Ramos et al. [36][30] investigated if Raman spectroscopy could be applied to routine cervical cytology samples from a cervical screening programme. Raman spectra were recorded from ThinPrep samples with negative, low-grade, and high-grade cytology. Cell nuclei from normal cells (negative cytology samples) and abnormal cells (low-grade and high-grade cytology samples) were targeted. Protein features were the main discriminating feature, although some differences in nucleic acid features were also observed. A change in ratio at 1318/1339 cm−1 was also observed for negative, low-grade, and high-grade cases, suggesting a decrease in the lipid/protein to guanine ratio in low-grade and high-grade cytology samples, either as a result of a reduction of lipids/proteins and/or an increase in nucleic acid (guanine) content. Sensitivity and specificity values > 90% were achieved when the cervical intraepithelial neoplasia (CIN) terminology was used to classify the samples compared to the squamous intraepithelial lesion (SIL) terminology. Kearney et al. [37][31] continued this work on single exfoliated cells and defined the Raman spectral signatures of superficial, intermediate, and parabasal cells, which are the main cell types present in liquid-based cytology Pap test specimens. Raman spectra were recorded from both the nuclei and from the cytoplasm of negative cytology and high-grade cytology samples, and the spectra from the cytoplasm showed significant variability, which was shown to be due to different levels of glycogen in the cells at different phases of the menstrual cycle. Glycogen, nucleic acids, and proteins were the main differentiating features between Raman spectra from the nuclei of cells with normal cytology and those with high-grade cytology. A further investigation of hormone-associated variability related to the menstrual cycle, menopause, and the use of hormone-based contraceptives on the Raman spectra was carried out by Traynor et al. [40][34] on negative cytology and high-grade cytology ThinPrep samples. The findings showed that post-menopausal samples could be problematic for Raman spectral analysis due to a lack of cellular material and the presence of cellular debris and mucus. In addition, although hormone-related spectral changes in glycogen and protein features were observed, it was found that biochemical changes in cells with high-grade cytology were more pronounced than biochemical changes in cells due to the menstrual cycle or the use of hormone-based contraceptives. Up to this point, abnormal cells with high-grade cytology had been investigated using Raman spectroscopy. To overcome the challenge associated with finding the rare abnormal cells on the unstained ThinPrep slide, Duraipandian et al. [39][33] showed that biochemical differences between negative cytology and high-grade cytology samples could be detected in cells that appear normal. Using morphologically normal single exfoliated cells, Raman spectroscopy was investigated as a potential triage test to discriminate between transient and transforming HPV infections [47][41]. HPV, DNA, and mRNA testing were carried out, and Raman spectra were recorded from single-cell nuclei. Discrimination was mostly based on increased nucleic acids (727, 781, 826, 1485, and 1580 cm−1), decreased glycogen (482, 852, 937, 1082, 1123, 1334, and 1380 cm−1), and changes in protein features (1152, 1240, 1450, 1640, and 1670 cm−1), indicating increased proliferation and altered protein expression due to the overexpression of E6/E7 viral proteins. A PLS-DA classification model was trained using 60 ThinPrep cervical samples and then validated using a blinded independent test set of 14 ThinPrep cervical samples, achieving an accuracy of 93%. Again using morphologically normal single exfoliated cells, Traynor et al. [48][42] investigated the clinical utility of Raman spectroscopy for identifying cervical precancers in a large sample set of 662 ThinPrep cervical samples. Raman spectra were recorded from single cell nuclei of negative, CIN1, and CIN2+ samples as a training set. A PLSDA classification model was validated using a blinded independent test set of 69 ThinPrep cervical samples, achieving an accuracy of 91.3%.

2.3. Raman Imaging

Coherent anti-Stokes Raman scattering (CARS)/second harmonic generation (SHG)/two-photon excited autofluorescence (TPF) imaging followed by Raman imaging has been applied to liquid-based Pap smear samples [41][35]. The main discriminating features were lipids, proteins, polysaccharides, and nucleic acids, and deep convolutional neural networks (DCNNs) achieved 100% accuracy for the classification of negative, low-grade, and high-grade cytology based on Raman spectral data and on morphological features obtained from CARS/SHG/TPF images. Sitarz et al. [43][37] used Raman imaging to assess glycogen levels in the cytoplasm of cervical exfoliated cells. For cervical epithelial cells with small diameter nuclei, glycogen content was similar for HPV-negative and HPV-positive samples, whereas, for cells with large diameter nuclei, glycogen content decreased in HPV-positive compared to HPV-negative samples, indicating that glycogen metabolism is accelerated with HPV infection. A follow-on study from the same group investigated the lipid profile of cervical exfoliated cells [46][40]. Lipid content was found to decrease in samples with low-grade cytology and increase in samples with high-grade cytology compared to samples with negative cytology, suggesting a dual switch of lipid metabolism.

2.4. SERS

A SERS approach has been applied to cervical exfoliated cells to discriminate normal, high-grade precancer, and cervical squamous cell carcinoma [44][38]. Gold nanoparticles were used to enhance the Raman signal, and spectra were recorded from single exfoliated cells, cell pellets, and extracted DNA. Nucleic acids and amino acids were the main discriminating features. Classification accuracies of 94.46%, 71.6%, and 97.72% were achieved for single exfoliated cells, cell pellets, and extracted DNA, respectively. For extracted DNA, high accuracy was achieved for normal, high-grade precancer, and cancer samples, whereas for the single exfoliated cells and cell pellets, high accuracies were achieved for normal and cancer samples but not for high-grade samples. This SERS approach was further extended to the simultaneous detection of cervical cancer biomarkers, p16 and Ki67, in single exfoliated cells using a SERS-tag functionalized with the monoclonal antibodies against p16/Ki67 [45][39].

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