A classification accuracy of 91.3% was achieved in an independent blinded test set (n = 69), demonstrating the potential clinical utility of Raman spectroscopy.There is an unmet clinical need for new methods to aid clinicians in the early detection of cervical cancer and precancer. Spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of cells, tissues and biofluids. This study aims to demonstrate the clinical utility of Raman spectroscopy for the identification of cervical precancer. Raman spectra were recorded from cervical smear samples (n = 662) 1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set.The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples ( Cervical cancer is the fourth most commonly diagnosed cancer, with 570,000 cases, and the fourth leading cause of cancer death, with 311,000 deaths, among women worldwide . CurrentRecently, human papillomavirus (HPV) testing has replaced cytology for primary screening in many countries due to a higher sensitivity than cytology for the detection of high-grade cervical precancer 2+) . HoweverRecently, optical spectroscopic techniques, such as Raman spectroscopy, have shown great promise for cancer diagnosis using tissues, cells and biofluids ,10. RamaOur group developed methods to record Raman spectra from clinical ThinPrep cytology samples on glass slides and to address blood contamination that can obscure spectral features and lead to variability ,15,27. Wn = 662) and by validating this model by means of an independent blinded dataset (n = 69).To date, however, the studies used small sample sizes and the results were validated using leave-one-out cross validation rather than using an independent test set. The aim of the present study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer by developing a classification model based on spectral data from negative, CIN1 and CIN2+ ThinPrep cytology samples (n = 390). Ethical approval for use of pseudo-anonymised samples for this study was granted by the CWIUH Research Ethics Committee (study no. 28–2014).Cervical smear samples collected in PreservCyt solution were obtained with consent from patients attending the Colposcopy clinic at the Coombe Women and Infants University Hospital (CWIUH), Dublin, Ireland (n = 392) was provided through application to the Scottish HPV Archive, a research tissue biobank set up to facilitate HPV-associated research. The archive comes under the auspice of the National Research for Scotland tissue bioresource, 20/ES/0061. Our previous study demonstrated that samples stored at −25 °C could be analysed successfully by Raman spectroscopy and that fresh samples, biobanked (stored) samples and pooled fresh and biobanked samples achieved a similar sensitivity and specificity for detection of CIN 2+ [A further set of cervical smear samples  had insufficient cells remaining in the ThinPrep vial after cytology and HPV tests were performed or had excessive debris covering any remaining cells. These samples could not be used for Raman spectroscopic analysis and had to be excluded. The training set consisted of samples which were either confirmed as normal by histology with no abnormality detected (n = 115) or were negative on cytology and/or colposcopy but had no biopsy or histology (n = 211), samples which were confirmed as CIN1 by histology (n = 200) and samples which were confirmed as CIN2 or CIN3 (CIN2/3) by histology (n = 136)  . Samplesn = 69) was provided by the Scottish HPV Archive as above. These samples were used as an independent test set to validate the classification model. This set of samples was blinded to the researchers until after the spectral acquisition, data preprocessing and analysis steps were carried out. The samples were confirmed by histology as normal (n = 17), CIN1 (n = 32) and CIN2+ (n = 20)  .Cervical smear samples were tested for HPV using a variety of technologies. Cervical smear samples collected by the CERVIVA team at the Coombe Women and Infants University Hospital were tested for HPV DNA using the Cobas HPV DNA test (Roche) and for HPV mRNA using the Aptima HPV Assay (Hologic), as part of a series of CERVIVA research programmes underway at that time.Samples collected as part of the Scottish HPV archive were tested for HPV using a variety of tests, including: Hybrid Capture 2 (HC2) (Qiagen), Cobas HPV Assay (Roche), Cepheid Expert HPV assay, the Abbott Real-Time HPV Assay, the Aptima HPV Assay (Hologic) and Optiplex HPV Genotyping test .The Cobas HPV test (Roche) is a fully automated PCR-based HPV DNA test that detects in three separate channels: HPV16 individually, HPV18 individually and a pool of 12 other HPV genotypes (11 definite high-risk genotypes plus 1 possibly high-risk genotype) as follows: HPV 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66 and 68.The Aptima HPV Assay (Hologic) detects mRNA coding for the E6 and E7 viral proteins of HPV types 16, 18, 31, 33, 35, 38, 39, 45, 51, 52, 56, 58, 59, 66 and 68. The Aptima HPV Assay involves three main steps, which take place in a single tube: target capture, target amplification by transcription-mediated amplification (TMA) and detection of the amplification products (amplicon) by the hybridization protection assay (HPA). A 1ml aliquot of PreservCyt specimen was used.®2 technology which is a nucleic acid hybridization assay with signal amplification that utilizes microplate chemiluminescent detection. Specimens containing the target DNA hybridize with a specific HPV RNA probe cocktail. The HC2 High-Risk HPV DNA Test detects HPV DNA from 13 high-risk HPV types, 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59 and 68.The HC2 High-Risk HPV DNA test (Qiagen) uses Hybrid CaptureThe XPert HPV Assay (Cepheid) is a real-time polymerase chain reaction (PCR) assay for the detection of 14 high-risk types of HPV DNA through five separate channels . The assay is formulated in a single-use cartridge, provides a result within one hour, can be performed by non-laboratory-trained health-care workers and requires minimal hands-on time.The Abbott Real-Time HPV Assay (Abbott) detects 14 high-risk HPV genotypes with simultaneous identification of HPV 16 and HPV 18.The Optiplex HPV Genotyping test  is a multiplex in vitro test kit based on Luminex Technology for the qualitative determination of human papillomavirus (HPV) genotypes 6, 11, 16, 18, 26, 31, 33, 35, 39, 42, 43, 44, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73 and 82 in polymerase chain reaction (PCR)-amplified samples of genomic DNA isolated from cervical smears.The Roche Linear Array HPV Genotyping Test is a qualitative test that detects 37 high- and low-risk human papillomavirus genotypes.Samples were prepared for Raman spectroscopy using the ThinPrep 2000 processor . The ThinPrep processor homogenized the sample by spinning the filter, creating shear forces that break up any clumped material . The cells were then transferred onto the TransCyt filter and transferred onto a glass slide to produce a monolayer of cells approx. 20 mm in diameter. The slide was then ejected into a fixative bath of 95% ethanol.2O2 at room temperature for 3 min, followed by a 70% solution of industrial methylated spirits (IMS) for 3 min followed by multiple dips into 100% IMS to remove any remaining cellular debris and H2O2 and were air dried.The slides then underwent a pretreatment step to remove any molecular contamination by haemoglobin, which obscured several features of the cellular spectrum as described in our published protocol . Briefly−1, with an integration time of 30 s averaged over two accumulations. Spectra were recorded only from the cell nucleus as these were found to be more reproducible and consistent than spectra from the cell cytoplasm [Raman spectra were acquired as described in our published protocol  using a ytoplasm . Where pn = 662) and a blinded test set (n = 69) was employed to validate the model.Data were normalised and analysed using MATLAB software (MathWorks) and specific scripts developed and adapted for uploading of the spectra and their preprocessing, including smoothing , baseline correction (rubberband) and vector normalization. The spectra were corrected for the glass background using a linear least-squares method with non-negative constraints (NNLS) as described in our published protocol . The datn = 114), borderline (BNA)  or low-grade (LSIL)  on cytology, with low numbers of samples with atypical (ASCUS/ASC-H/ASC-BNA) , high-grade (HSIL)  or no cytology results . Almost half of the CIN1 samples were LSIL  and almost a third were BNA  on cytology. The remainder of the CIN1 samples were HSIL , negative , ASCUS/ASC-H/ASC-BNA  or had no cytology results . The CIN2+ samples were either LSIL , HSIL  or BNA  on cytology, with low numbers of samples with negative , ASCUS/ASC-H/ASC-BNA , high-grade/invasive  or no cytology results .Cytology results were available for over 95% of the training set samples and are shown in n = 301) and are shown in n = 125), and of these, 45.6% had an HPV DNA-negative result (n = 57), 24% had an HPV DNA-positive result (n = 30) according to an hc2 or Linear Array test and 30.4% had an HPV genotype result from a Cobas, Optiplex, Luminex, Abbott or Cepheid test (n = 38). The majority were either HPV16 and other high-risk HPV types (n = 10) or other high-risk HPV types (n = 21) (n = 93), and of these, 8.6% had a negative result (n = 8), 32.25% had a positive result (n = 30) according to an hc2 or Linear Array test and 59.14% had an HPV genotype test result , (n = 55). The majority were HPV16 and other high-risk HPV types (n = 11) or other high-risk HPV types (n = 28) (n = 83), and of these, no samples had a negative result, 21.68% had a positive result (n = 18) according to an hc2 or Linear Array test and 78.31% had an HPV genotype result from a Cobas, Optiplex, Luminex, Abbott or Cepheid test (n = 65). Again, the majority were HPV16 and other high-risk HPV types (n = 23) or other high-risk HPV types (n = 22)  were only available for less than half of the training set samples  . Less th(n = 28) . More th(n = 22) .n = 236) and are shown in n = 107), and of these, 55.14% had a negative result (n = 59) and 44.86% had a positive result (n = 48). Less than half of the CIN1 samples had an HPV mRNA test result , and of these, 27.96% had a negative result (n = 26) and 73.12% had a positive result (n = 68). Only 25.74% of the CIN2+ samples had an HPV mRNA test result (n = 35), and of these, 20% had a negative result (n = 7) and 80% had a positive result (n = 28).HPV mRNA test results (Aptima) were available for 35.65% of the training set samples (−1 (glycogen), 621 and 644 cm−1 (proteins), 728 and 784 cm−1 (DNA), 828 cm−1 (DNA/RNA), 853 and 936 cm−1 (glycogen and proteins), 957 cm−1 (DNA), 1004 cm−1 , 1035 cm−1 (proteins), 1092 cm−1 (DNA phosphate backbone), 1127 cm−1 (proteins), 1176 cm−1 (cytosine/guanine), 1210 cm−1 , 1245 cm−1 (amide III), 1320 cm−1 , 1338 cm−1 , 1422 cm−1 (DNA/RNA), 1450 cm−1 (proteins and lipids), 1578 cm−1 (nucleic acids), 1610 cm−1 , 1656 and 1669 cm−1 (amide I) (−1 (proteins) and negative peaks at 482, 853, 936 (glycogen), 1238 (proteins), 1400, 1422 and 1578 cm−1 (nucleic acids).The mean Raman spectra recorded from the negative, CIN1 and CIN2+ samples are shown in amide I) . Differe−1.A PLSDA classification model was developed from the spectral data of the training set. The PLSDA latent variables (LVs) showed similar features to those observed in the difference spectra at 482, 728, 784, 828, 852, 1092, 1238, 1334, 1380, 1450, 1485, 1578, 1656 and 1669 cmn = 17), CIN1 (n = 32) and CIN2+ (n = 20)  , Table 3n = 12) or borderline  on cytology. Similarly, the CIN1 samples were mainly low-grade  or borderline , with low numbers of high-grade  or no cytology result . The CIN2+ samples were a mixture of negative , borderline , low-grade  and high-grade  cytology.Cytology results were available for over 98% of the validation set samples and are shown in n = 68 samples and additional Abbott and BD tests for n = 8 samples) were available for over 98% of the validation set samples and are shown in n = 12), 17.65% HPV16 and other high-risk HPV types (n = 3), 5.88% low-risk HPV types (n = 1) and 5.88% no HPV types (n = 1). Similarly, the CIN1 samples were 68.75% other high-risk HPV types (n = 22), 18.75% HPV16 and other high-risk HPV types (n = 6), 3.13% low-risk HPV types (n = 1) and 3.13% no HPV types (n = 1) (n = 9), HPV16 and other high-risk HPV types , HPV16 , HPV16, 18 and other high-risk HPV types  and no HPV types .HPV DNA test results (Luminex test for  (n = 1) . The HPVn = 39) and are shown in n = 5). Of the CIN1 samples tested, 33.33% had a negative result (n = 5) and 66.66% had a positive result (n = 10). Of the CIN2+ samples tested, 31.57% had a negative result (n = 6) and 68.42% had a positive result (n = 13).HPV mRNA test results (Aptima) were available for 56.52% of the validation set samples (n = 69) are shown in the confusion matrix . The different spectra and latent variables (LV1 and LV2) showed that the discrimination was mostly based around increased nucleic acids , decreased glycogen  and changes in protein features , indicating an increased proliferation and altered protein expression as a result of HPV infection. These discriminating features were consistent with our previous studies [Initially, Raman spectra were recorded from a training set consisting of cervical ThinPrep samples . Raman spectra were recorded from this test set and used to test the classification model. In total, 63/69 samples (91.30%) were classified correctly. All 17 negative samples were classified correctly, but 2 CIN1 samples and 4 CIN2+ samples were incorrectly classified as negative. Five of the misclassified samples were low-grade or high-grade on cytology and were high-risk HPV DNA-positive and HPV mRNA-positive. Interestingly, one of the misclassified samples, which was CIN3 on histology, was negative on cytology, had no HPV types and was HPV mRNA-negative. Although misclassified according to histology results, interestingly, the negative classification was consistent with the cytology and HPV test results. Our study showed similar performance to a recently reported SERS approach using gold nanoparticle substrates to enhance the Raman signal of cervical exfoliated cells [A PLSDA classification model was developed from the training set for discrimination between negative, CIN1 and CIN2+ ThinPrep samples, and validated using an independent blinded dataset . For the samples with normal histology, these were comprised of samples with negative, borderline and low-grade cytology for the training set, but only borderline and low-grade cytology for the test set. As not all samples were tested for HPV DNA and mRNA, it was difficult to compare the training and test sets in terms of HPV test results. Another limitation of the study was that not all of the negative samples in the training set were confirmed by histology as normal, but this was difficult to achieve, as a biopsy was not performed for all patients undergoing a colposcopy. In addition, the sample set represented a disease-enriched population to increase the number of disease cases. This is standard practice when evaluating a new technology, particularly in the case of cervical cancer, where CIN2 prevalence is only around 1% in routinely screened high-income countries. Future work should involve a prospective study in a screening population. Although a technical validation exercise was performed demonstrating that stored samples are credible biospecimens for Raman analysis [In our study, for the samples with CIN1 histology, the test set corresponded reasonably well to the training set, as both sets of samples were mainly comprised of samples with borderline or low-grade cytology. For the samples with CIN2+ histology, the training set mostly consisted of samples with borderline, low-grade and high-grade cytology, while the test set also contained samples with negative cytology (analysis , the useRecently, HPV testing has been introduced for primary cervical screening, but the HPV DNA test cannot distinguish between a transient and a transforming HPV infection, and cytology-based triage is also needed. Our study aimed to evaluate Raman spectroscopy as a tool to support current cervical screening approaches, HPV testing and cytology. As HPV primary screening has been implemented widely, there is a need to improve the specificity through the use of a good triage test. Raman spectroscopy may be a good alternative to cytology triage, as it is less subjective with a better sensitivity. Our recent study has shown that Raman spectroscopy has potential as a triage test for HPV-positive women to identified transforming HPV infections , althougIn addition, in the future, with more widespread HPV vaccination, the performance of techniques relying on a subjective assessment, such as cytology, should decrease as a consequence of the reduction in the prevalence of the disease. Thus, new objective tools, such as Raman spectroscopy, would be needed for screening future vaccinated populations. Raman spectroscopy is a low-cost solution that has the potential to be integrated into existing services, as well being a potential solution for developing countries, where “see-and-treat” options are preferred to increase the effectiveness of screening hard-to-reach populations.In this study, a classification model for negative, CIN1 and CIN2+ ThinPrep samples was developed and validated. The model was developed from a training set of 662 samples and an accuracy of 91.3% was achieved in an independent test set of 69 samples.Raman spectroscopy is a label-free objective tool that may be a good alternative to cytology for the triage of HPV-positive cases. It may also be important in the future as HPV vaccination increases, and the prevalence of the disease decreases, because the reduced performance of current screening and triage tests should result in a clinical need for new objective tools for screening vaccinated populations.