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Schiemer, R.; , .; Phang, S.; Atiomo, W.; Gajjar, K. Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening. Encyclopedia. Available online: (accessed on 01 March 2024).
Schiemer R,  , Phang S, Atiomo W, Gajjar K. Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening. Encyclopedia. Available at: Accessed March 01, 2024.
Schiemer, Roberta, , Sendy Phang, William Atiomo, Ketankumar Gajjar. "Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening" Encyclopedia, (accessed March 01, 2024).
Schiemer, R., , ., Phang, S., Atiomo, W., & Gajjar, K. (2022, May 18). Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening. In Encyclopedia.
Schiemer, Roberta, et al. "Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening." Encyclopedia. Web. 18 May, 2022.
Vibrational Biospectroscopy for Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. 

endometrial cancer uterine neoplasm cancer of the endometrium spectroscopy Raman spectroscopy

1. Current Endometrial Cancer Diagnosis and Screening

Timely investigation of women presenting with symptoms, such as post-menopausal bleeding and persistent menstrual irregularities, allows most cases of EC to be identified in early stage.
Ultrasound imaging, hysteroscopy and endometrial biopsy, together with the histopathological tissue analysis, are the current mainstay of EC diagnosis. In addition, magnetic resonance imaging techniques (MRI) are useful in the assessment of depth of myometrium invasion, cervical stromal involvement and lymph node metastasis [1][2][3][4].
However, unnecessary procedure should be avoided, which may expose patients to complications, generate needless anxiety and take up financial resources. Indeed, hormonal imbalance, coagulopathies, benign endometrial lesions and the use of medications including hormone replacement therapy (HRT) are some of the factors associated with irregular and recurrent vaginal bleeding, which may occur in the absence of EC [5]. Consequently, the main challenge in early cancer diagnosis is the appropriate selection of those patients that require investigations and invasive procedures.

1.1. Ultrasound Imaging

Ultrasound imaging is a technique, which uses high frequency sound waves to provide information about tissue and organ characteristics. The procedure can be performed by the transabdominal and transvaginal access routes, does not require bowel preparation, is safe and is, overall, well tolerated by patients [6].
Ultrasonography is, however, highly operator dependent. Furthermore, excess adipose tissue interferes with sound wave signals, affecting image quality [6], thus women with high body habitus are at increased risk of suffering diagnostic delays [7].
Measurements of the endometrial thickness using ultrasound imaging are used as a surrogate marker to check for the presence of intrauterine abnormalities [8]. However, ultrasound imaging alone cannot discriminate whether an increased endometrial thickness is secondary to a benign lesion or to malignant disease [8].
Women with post-menopausal bleeding have a 8–11% risk of EC, which justifies the need for endometrial assessment in these patients [9]. The use of endometrial thickness cut-offs of 4 mm and 5 mm leads to the correct identification of 94.8% and 90.3% of EC cases, respectively [10]. However, the test specificity is poor, leading to a high risk of false positive results and, consequently, many unnecessary invasive investigations and biopsies [10][11].
In pre-menopausal women with abnormal uterine bleeding, the diagnostic role of endometrial thickness is controversial, as there can be overlap between physiological thickening caused by sex hormones and that caused by endometrial disease [12]. While it has been suggested that a thickness of <8 mm should be considered as non-hyperplastic [13], and only 1% of endometrial cancers occurs in women < 40 years of age [11], there is still no consensus on the ideal endometrial thickness cut-off in this group of patients [14], thus an alternative or complementary non-invasive triaging tool would facilitate the clinician’s decision-making on when to refer for further invasive diagnostic procedures.

1.2. Hysteroscopy

The direct endoscopic visualisation of the endometrial cavity by hysteroscopy, using visible light at 4 to 5× magnification [15], is an invasive procedure that can be performed in order to evaluate the endometrial cavity, to remove lesions such as polyps or small fibroids and to obtain endometrial biopsies.
Hysteroscopy can be carried out both in the outpatient setting and in theatre, under regional or general anaesthetic [16]. Although, overall, the procedure has been shown to be well tolerated, safe, accurate and acceptable, regardless of the setting in which is performed [17][18][19], some patients do experience significant discomfort during outpatient hysteroscopy [19]. Unfortunately, it is difficult to identify this group of patients preoperatively, and for these women a routine hysteroscopic procedure may turn into a painful and traumatic experience. Furthermore, although uncommon, complications may arise, including bleeding, infection and uterine damage [20], and the failure rate of hysteroscopy, where the instrument cannot be successfully introduced into the uterine cavity, has been estimated at 4.2% [19][21].
Well-conducted systematic reviews and meta-analyses found that hysteroscopy is highly accurate for the diagnosis of EC in women with abnormal uterine bleeding [19][22][23] and it is useful at excluding endometrial disease [22][23], although the diagnostic accuracy for endometrial hyperplasia appears to be more modest [19].
Indeed, in its updated 2018 guidance, the UK National Institute for Health and Care Excellence (NICE) now recommends that hysteroscopy can be offered as a first-line investigation for heavy menstrual bleeding, in preference to pelvic ultrasound, if the woman’s history suggests sub-mucosal fibroids, polyps or endometrial pathology [24].
The inevitable consequence of such a diagnostic strategy, however, is the need for a structural re-organisation of healthcare services, in order to absorb the estimated 10,000 extra procedures that would be performed in England each year [25] and their added financial costs. The availability of adequately sized and equipped facilities and investments in recruitment and training of skilled staff are some of the challenges to overcome, in order to implement the new guidance into clinical practice.

1.3. Endometrial Biopsy and Histological Analysis

Histological examination of an endometrial biopsy specimen is the current so-called: “Gold Standard” of EC diagnosis. The sample preparation and analyses, required to allow the visualisation of the internal architecture of cells and tissues and identify cancerous features [26] are, however, time-consuming, and can be subject to human error [27].
The condition or quality of an endometrial biopsy must be “adequate” in order to provide the histological diagnosis, but the lack of standard agreement on quality and quantity assessment criteria [28] leaves the decision regarding sample suitability to individual pathologists. This allows for a high inter-observer variability [29] and a risk of diagnostic delay and potentially detrimental consequences for patients. The reported rate of insufficient quality or quantity of endometrial tissue samples for histological diagnosis in post-menopausal women is 31% (range 7–76%) [30], while in pre-menopausal women it is lower, ranging between 2% and 10% [31]. The reasons for insufficient sampling appear unclear: the experience of the operator has not been confirmed to be a determining factor [32][33] and there is wide variance between insufficient sample rates reported in single versus multicentre studies, suggesting that study design may influence the results [19][32][33][34][35].
Importantly, obtaining an endometrial biopsy is not always a straightforward process. Indeed, endometrial sampling fails in approximately 11% of cases (range 1–53%), mostly as a consequence of cervical stenosis [30]. Factors such as the post-menopause, advanced age and nulliparity also appear to be associated with higher failure rates, likely as a consequence of variation in endometrial thickness and anatomical changes that occur in these patient groups [33].
Outpatient endometrial biopsy has mostly replaced traditional dilatation and curettage under general anaesthetic worldwide [36][37][38], as it has shown comparable performance, while being less invasive and more cost effective [39][40]. The diagnostic accuracy of these biopsies were investigated extensively and a number of meta-analyses were published, reporting on sensitivity and specificity in relation to endometrial cancer, endometrial hyperplasia (with and without atypia) and benign endometrial disease [30][31][38][39][41]. Overall pipelle biopsy, with conventional histopathology, appears to be an effective tool to identify endometrial cancer when adequate samples are obtained; however, the test is not as reliable in the case of endometrial hyperplasia, where a negative result only decreases the hyperplasia risk by 2-fold [41].
The potential failure to diagnose or exclude disease after invasive procedures, such as hysteroscopy and endometrial biopsy, is concerning. Coupled with the highly subjective nature of histopathological assessments, it highlights the need for alternative approaches to complement current practice and provide pathologists with additional support in achieving a more objective tissue evaluation. Furthermore, in the context of EC research priorities [42], current diagnostic modalities, despite their established advantages, are insufficient to fully address the need for patient risk stratification and the demand for minimally invasive, individualised, screening, diagnostic and treatment monitoring pathways.

1.4. Screening for Endometrial Cancer

Screening is defined by the World Health Organisation (WHO) as “the presumptive identification of unrecognized disease in an apparently healthy, asymptomatic population by means of tests, examinations or other procedures that can be applied rapidly and easily to the target population” [43]. The researchers suggest that an ideal screening test should be accurate, well-tolerated, associated with minimal morbidity and cost-effective.
Women with known Lynch Syndrome already undergo a multimodal surveillance of the endometrium until hysterectomy is performed, due to their high lifetime risk of developing EC [44].
Unfortunately, there is no EC screening test which is accurate and reliable enough to be implemented for the general asymptomatic population [10][11][12][45][46]. Ultrasound imaging, despite its accessibility, safety and low cost, unfortunately lacks the required sensitivity and specificity, as demonstrated by the nested case-control study [45] within the 2016 United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) [47]. The study showed that if the general UK population were screened using an endometrial thickness cut-off of 5 mm, in order to diagnose 80.5% of cancers, for each endometrial cancer or atypical endometrial hyperplasia (AEH) case detected, 58 healthy women would have to undergo additional unnecessary investigation [45]. It is apparent that the modest test accuracy, potential patient risks and added costs, do not justify the implementation of such a screening strategy. There is, therefore, an unmet need to innovate current diagnostic and screening methods, to tackle the increasing endometrial cancer disease burden and allow early disease detection and timely treatment.

2. Biospectroscopy

The interaction between electromagnetic radiation and any particular matter results in the measurable linear and nonlinear physical phenomena of absorption, emission, reflection and scattering of the radiation by the matter; measurement of the radiation after its interaction with matter yields information on the makeup and arrangement of the matter and is known as spectroscopy. The measurement is displayed as a spectrum, which is a graphical representation of energy absorption, emission, reflection or scattering by the material as a function of the incoming radiation photon energy (plotted usually as frequency or wavelength). The application of spectroscopic techniques to biological materials is called biospectroscopy and the name was only coined in the 1960s [48].
Mid-infrared Absorption and Raman scattering spectroscopy, are sister-vibrational absorption techniques, being complementary as they are based on different quantum mechanical rules. They are label-free, non-destructive optical methods with the ability to investigate the vibration and rotation of atoms and molecules in biological materials, induced by irradiation by light.
The vibrational spectra that are generated depend on the specific biochemical structure of the sample tested; they provide information on the whole range of molecules within the sample simultaneously, which can, therefore, be interpreted as a unique “signature” or a “fingerprint” of that sample [49].
The alteration of molecular signatures in a cell or tissue, which has undergone disease transformation, can be objectively detected, gaining vibrational spectroscopic techniques a potential role in cancer diagnosis and screening [50][51][52].

2.1. Mid-Infrared Absorption Spectroscopy

Mid-infrared (MIR) light is a radiation region of the electromagnetic spectrum of 3–50 microns wavelength, as defined by ISO 20473:2007 [53]. When biological tissues are exposed to MIR light, part of the photon energy can be resonantly absorbed, inducing vibrations; the quantum mechanical selection rules include that there must be a change in dipole moment during the vibration, hence heteropolar chemical bonds are vibrationally stimulated. The intensity and wavelength of each vibration depend on the nature of the chemical bonds and their specific molecular environment, that is its molecular structure [54]. The fraction of energy absorbed by the sample at different frequencies can be quantitatively measured by means of dispersive infrared (IR) spectroscopes [51].
The technology has been further refined and made faster since the 1970s by the introduction of Fourier transform (Ft) IR spectrometers, in which all broadband spectral information is collected simultaneously, and then many times, in order to average and then maximise the signal-to-noise ratio. The raw data obtained, called an interferogram, is then converted using the Fourier transform mathematical algorithm into wavelength intensity, from which the energy absorbed by the sample can be derived [54].
The majority of work reported to date used the technique of Fourier transform Infrared (FtIR) with Attenuated Total Reflectance (ATR) on excised tissue, or extracted body fluids, which overcomes the need for complex sample preparation [54]. Other image acquisition modes include transmission and transflection; these require the use of suitable substrates (e.g., calcium or barium fluoride slides) and longer machine and sample preparation compared with ATR [55]. Transflection was shown to introduce spectral artefacts and so has lost favour [56][57].

2.2. Raman Spectroscopy

Raman spectroscopy relies on the principle of inelastic scattering of photons, also known as Raman scattering, and was first discovered by Raman in 1928 [58]. When a monochromatic light source, such as a visible or near-infrared laser, interacts with a sample, most of the light which scatters off is unchanged in energy. However, a very small number of photons will exchange part of their energy with the molecules of the sample: the chemical bonds of the sample become temporarily excited to a virtual state, then relax to a different vibrational state, while the emitted photons shift to a lower (Stokes) or higher (Anti-Stokes) frequency [59]. The shift in frequency, measured by the Raman spectrometer, is indicative of specific vibrational modes of the sample molecules and, therefore, a unique “fingerprint” spectrum can be inferred [52]. The quantum mechanical selection rules of Raman include that the molecular bond should not undergo a change in dipole during vibration, thus favouring homopolar chemical bonds. Hence, Raman spectroscopy is unaffected by water, and is non-destructive and label-free [60]. These characteristics offer technology a high degree of flexibility, with potential applications to the study of fresh, fixed and live tissues and cells [61]. Spontaneous Raman scattering is a rare phenomenon, with a very low probability of occurrence (~1 in 108) [62]. In order to enhance the Raman-scattering signal level, several variations of Raman spectroscopy have been developed, including resonant Raman (RR), coherent anti-Stokes Raman scattering (CARS) and surface-enhanced Raman scattering (SERS) [50]; these are, however, expensive technologies, with a large footprint. Ultimately, the choice of instrument, desired wavelength and spatial resolution will vary depending on the required application.

2.3. Biospectroscopy for Endometrial Tissue Interrogation

The development of effective diagnostic, screening and treatment strategies for endometrial cancer finds its basis in a deep understanding of tissue physiological and pathological processes. In particular, to be clinically useful, a new diagnostic or screening tool should be able to accurately distinguish healthy patients from those with disease.
ATR-FtIR and Raman spectroscopy were used to categorise disease and identify cancer or intra-epithelial neoplasia in a number of excised tissues, such as prostate [63][64][65][66][67], gastrointestinal tract [68][69][70][71][72], brain [60][73][74], breast [75][76][77][78][79][80][81][82], lung [83][84] and skin [85][86][87]. Gynaecological applications include studies of cervical cytology and histopathology [88][89][90][91][92][93], ovarian cancer [94][95] and vulvar disease [96].
With regards to endometrial tissue, vibrational biospectroscopy was successfully applied in preliminary research to the study of its structural architecture [59][97], as well as the classification of cancerous lesions [98][99][100], specific cancer subtypes [99][100] and the identification of cell phenotypes with different drug sensitivity [101] . There is, however, a paucity of literature, and specifically of large studies, compared with other types of diseases.

2.4. Biospectroscopy of Biofluids: Screening and Cancer Diagnosis

With the endometrial cancer global disease burden expected to rise, disease screening, early detection and treatment monitoring will benefit from the development of more cost-effective, rapid, non-invasive and label-free techniques. Biological fluids, being readily accessible with low-cost procedures, represent the ideal sample target. Indeed, the study of biofluids with spectroscopy is becoming a rapidly emerging field and a number of pilot studies have now been published, focusing on oncological applications, as well as on a broad range of acute and chronic medical conditions [52][95][102][103][104][105][106][107][108][109].
With regards to endometrial cancer, biospectroscopy was proposed as a novel approach to test blood, urine and saliva. The development of such techniques is particularly relevant as currently available methods, such as radiological imaging and blood biomarkers, lack the required sensitivity, specificity and accuracy to be used as effective screening tools. Interestingly, while Raman spectroscopy of blood serum was recently investigated for the first time as a non-invasive technique to diagnose endometriosis [110], no studies were found on the application of Raman spectroscopy to biofluids for EC diagnosis (Table 1).
Table 1. Spectroscopy of biofluids: endometrial cancer studies.
Similar to experiments performed with endometrial tissue, the sample manipulations and chemometric analyses used to test biofluids vary between studies. The first pilot research, by Gajjar et al. [105], investigated the potential role of ATR-FtIR for cancer diagnosis using blood samples and, with the development of “machine classifiers”, reported classification rates of endometrial cancer versus controls up to 77.08% and 81.67% for serum and plasma, respectively. The same spectral data were more recently re-analysed by the research group [114], to evaluate the performance of alternative data-processing methods and classifier tools. Furthermore, the authors focused on the water-free sub-section of the spectrum (1430 cm−1 to 900 cm−1), in contrast with the more extended bio-fingerprint region (1800 cm−1 to 900 cm−1) used in the original paper. The researchers assessed four types of classifiers and reported high discrimination rates for both plasma (sensitivity of 0.865 ± 0.043 and specificity of 0.895 ± 0.023 with k-Nearest Neighbours algorithm) and serum (sensitivity 0.899 ± 0.023, specificity 0.763 ± 0.048 for LDA). This approach demonstrated for the first time the possibility of overcoming the dominant effect of water seen in the analysis of hydrated samples with MIR spectroscopy, which would support future applications of MIR spectroscopy in vivo to cancer diagnosis and screening [114].
Paraskevaidi et al. [111][112] used ATR-FtIR spectroscopy with PCA followed by support vector machine (PCA-SVM) to analyse blood plasma and serum from women with endometrial cancer and age-matched healthy controls. Test performance was described as high as 100% sensitivity and 85% specificity (98% accuracy) and changes in the bands associated with proteins and lipids were consistently responsible for the discrimination between blood plasma and serum from endometrial cancer and healthy samples. Traditionally, low-emissivity (low-E) slides have been used as ATR-FtIR substrates to support samples; however, their high cost may limit their use in large-scale studies and implementation in routine analysis. Aluminium foil substrate may represent an equivalent, cheaper, sample-support alternative for the detection of endometrial cancer with blood plasma and serum [112]. Indeed, Paraskevaidi et al. [112] showed that aluminium foil substrate was able to differentiate blood plasma and serum from patients with endometrial cancer and controls, with sensitivities and specificities comparable with the traditional low-E slides. The cost-effectiveness and ease of use, if validated in larger datasets, would greatly facilitate clinical application.
More recently, the same group, Paraskevaidi et al. [113], examined plasma from women with endometrial cancer, atypical hyperplasia and controls with ATR-FtIR in the largest diagnostic cross-sectional study to date (total n = 652). The study identified the six most discriminatory peaks for each subgroup analysis, suggesting these features could be developed in a panel of diagnostic spectral markers. Endometrial cancers and controls were differentiated with 87% sensitivity and 78% specificity (overall accuracy of 83%); further analysis of cancer subtypes achieved disease discrimination with sensitivities of 71–100% and specificities of 81–88%. In addition, the authors accounted for potential confounding factors, such as age, body mass index (BMI), diabetes, fasting status and blood pressure and found no impact on spectral classification after applying the MANOVA test (multivariate analysis of variance) to the spectral wavenumbers.
Discrimination between endometrial cancer and controls appears consistently superior for plasma over serum samples. It has been speculated that this might be due to the more complex and heterogeneous composition of the plasma; however, the cause of superior diagnostic results, being a panel of biomolecules or the presence of higher concentration of cell-free DNA, still remains to be determined [105][112].
In addition to plasma and serum, pilot research has also applied biospectroscopy techniques for endometrial cancer diagnosis to urine and saliva analysis.
Urine spectra obtained by Paraskevaidi et al. [108] yielded high levels of diagnostic accuracy after the application of multivariate analysis and classification algorithms (95% sensitivity and 100% specificity, 95% accuracy). The classification methods used included: partial least squares discriminant analysis (PLS-DA), PCA-SVM and genetic algorithm with LDA (GA-LDA.) The majority of discriminating wavelengths were once more located in the lipid, protein and acid nucleic infrared regions.
Saliva spectral analysis by Bel’skaya et al. [109] showed interesting alterations in the lipid regions of patients with endometrial and ovarian cancer; in particular, the ratio of the intensity of the absorption bands 2923/2957 cm−1 appeared to consistently decrease in cancer samples compared with controls, leading the authors to propose its use as a new diagnostic criterion.
Spectral differences in lipid absorption bands of healthy and diseased samples, also documented in prostate [115] and breast cancer [116], may be explained by tumour-mediated changes of the lipid metabolism and may warrant further investigations in the context of non-invasive endometrial cancer biomarker development.


  1. Luomaranta, A.; Leminen, A.; Loukovaara, M. Magnetic Resonance Imaging in the Assessment of High-Risk Features of Endometrial Carcinoma: A Meta-Analysis. Int. J. Gynecol. Cancer 2015, 25, 837–842.
  2. Andreano, A.; Rechichi, G.; Rebora, P.; Sironi, S.; Valsecchi, M.G.; Galimberti, S. MR diffusion imaging for preoperative staging of myometrial invasion in patients with endometrial cancer: A systematic review and meta-analysis. Eur. Radiol. 2014, 24, 1327–1338.
  3. Cignini, P.; Vitale, S.G.; Laganà, A.S.; Biondi, A.; La Rosa, V.L.; Cutillo, G. Preoperative work-up for definition of lymph node risk involvement in early stage endometrial cancer: 5-year follow-up. Updat. Surg. 2017, 69, 75–82.
  4. Koplay, M.; Dogan, N.U.; Erdoğan, H.; Sivri, M.; Erol, C.; Nayman, A.; Karabagli, P.; Paksoy, Y.; Çelik, C. Diagnostic efficacy of diffusion-weighted MRI for pre-operative assessment of myometrial and cervical invasion and pelvic lymph node metastasis in endometrial carcinoma. J. Med. Imaging Radiat. Oncol. 2014, 58, 538–546.
  5. Wouk, N.; Helton, M. Abnormal Uterine Bleeding in Premenopausal Women. Am. Fam. Physician 2019, 99, 435–443.
  6. Gill, K.A. Chapter 1: Introduction to Diagnostic Ultrasound. In Ultrasound in Obstetrics and Gynaecology; Davies Publishing, Inc.: Pasadena, CA, USA, 2014; pp. 1–2.
  7. Cusimano, M.C.; Simpson, A.N.; Han, A.; Hayeems, R.; Bernardini, M.Q.; Robertson, D.; Kives, S.L.; Satkunaratnam, A.; Baxter, N.N.; Ferguson, S.E. Barriers to care for women with low-grade endometrial cancer and morbid obesity: A qualitative study. BMJ Open 2019, 9, e026872.
  8. Bosch, T.V.D. Ultrasound in the diagnosis of endometrial and intracavitary pathology: An update. Australas. J. Ultrasound Med. 2012, 15, 7–12.
  9. Clarke, M.A.; Long, B.J.; Del Mar Morillo, A.; Arbyn, M.; Bakkum-Gamez, J.N.; Wentzensen, N. Association of Endometrial Cancer Risk With Postmenopausal Bleeding in Women: A Systematic Review and Meta-analysis. JAMA Intern. Med. 2018, 178, 1210–1222.
  10. Timmermans, A.; Opmeer, B.C.; Khan, K.S.; Bachmann, L.M.; Epstein, E.; Clark, T.J.; Gupta, J.K.; Bakour, S.H.; Bosch, T.V.D.; van Doorn, H.C.; et al. Endometrial Thickness Measurement for Detecting Endometrial Cancer in Women With Postmenopausal Bleeding. Obstet. Gynecol. 2010, 116, 160–167.
  11. Smith-Bindman, R.; Kerlikowske, K.; Feldstein, V.A.; Subak, L.L.; Scheidler, J.; Segal, M.R.; Brand, R.; Grady, D. Endovaginal Ultrasound to Exclude Endometrial Cancer and Other Endometrial Abnormalities. JAMA 1998, 280, 1510–1517.
  12. Getpook, C.; Wattanakumtornkul, S. Endometrial thickness screening in premenopausal women with abnormal uterine bleeding. J. Obstet. Gynaecol. Res. 2006, 32, 588–592.
  13. Nicula, R.; Costin, N. Management of endometrial modifications in perimenopausal women. Clujul Med. 2015, 88, 101–110.
  14. Bignardi, T.; Van den Bosch, T.; Condous, G. Abnormal uterine and post-menopausal bleeding in the acute gynaecology unit. Best Pract. Res. Clin. Obstet. Gynaecol. 2009, 23, 595–607.
  15. Valle, R.F. Operative Hysteroscopy. Glob. Libr. Women’s Med. 2008.
  16. British Gynaecological Cancer Society. BGCS Uterine Cancer Guidelines: Recommendations for Practice; British Gynaecological Cancer Society: London, UK, 2017.
  17. Bakour, S.H.; Jones, S.E.; O’Donovan, P. Ambulatory hysteroscopy: Evidence-based guide to diagnosis and therapy. Best Pract. Res. Clin. Obstet. Gynaecol. 2006, 20, 953–975.
  18. Kremer, C.; Barik, S.; Duffy, S. Flexible outpatient hysteroscopy without anaesthesia: A safe, successful and well tolerated procedure. Br. J. Obstet. Gynaecol. 1998, 105, 672–676.
  19. Clark, T.J.; Voit, D.; Gupta, J.K.; Hyde, C.; Song, F.; Khan, K.S. Accuracy of Hysteroscopy in the Diagnosis of Endometrial Cancer and Hyperplasia. JAMA 2002, 288, 1610–1621.
  20. Royal College of Obstetricians and Gynaecologists. Diagnostic Hysteroscopy under General Anaesthesia. Consent Advice 1. Available online: (accessed on 28 April 2021).
  21. Relph, S.; Lawton, T.; Broadbent, M.; Karoshi, M. Failed hysteroscopy and further management strategies. Obstet. Gynaecol. 2016, 18, 65–68.
  22. Farquhar, C.; Ekeroma, A.; Furness, S.; Arroll, B. A systematic review of transvaginal ultrasonography, sonohysterography and hysteroscopy for the investigation of abnormal uterine bleeding in premenopausal women. Acta Obstet. Gynecol. Scand. 2003, 82, 493–504.
  23. Van Dongen, H.; De Kroon, C.D.; Jacobi, C.E.; Trimbos, J.B.; Jansen, F.W. Diagnostic hysteroscopy in abnormal uterine bleeding: A systematic review and meta-analysis. BJOG Int. J. Obstet. Gynaecol. 2007, 114, 664–675.
  24. National Institute for Health and Care Excellence. Heavy Menstrual Bleeding: Assessment and Management. NICE Guideline . Available online: (accessed on 1 October 2021).
  25. National Institute for Health and Care Excellence. Heavy Menstrual Bleeding (Update). A: Evidence Reviews for Diagnostic Test Accuracy in Investigation for Women Presenting with Heavy Menstrual Bleeding. Available online: (accessed on 1 October 2021).
  26. Slaoui, M.; Fiette, L. Histopathology procedures: From tissue sampling to histopathological evaluation. Methods Mol. Biol. 2011, 691, 69–82.
  27. Morelli, P.; Porazzi, E.; Ruspini, M.; Restelli, U.; Banfi, G. Analysis of errors in histology by root cause analysis: A pilot study. J. Prev. Med. Hyg. 2013, 54, 90.
  28. Phillips, V. Results of a questionnaire regarding criteria for adequacy of endometrial biopsies. J. Clin. Pathol. 2005, 58, 417–419.
  29. Breijer, M.C.; Visser, N.C.M.; van Hanegem, N.; van der Wurff, A.A.; Opmeer, B.C.; van Doorn, H.C.; Mol, B.W.J.; Pijnenborg, J.M.A.; Timmermans, A. A Structured Assessment to Decrease the Amount of Inconclusive Endometrial Biopsies in Women with Postmenopausal Bleeding. Int. J. Surg. Oncol. 2016, 2016, 3039261.
  30. van Hanegem, N.; Prins, M.M.; Bongers, M.Y.; Opmeer, B.C.; Sahota, D.; Mol, B.W.J.; Timmermans, A. The accuracy of endometrial sampling in women with postmenopausal bleeding: A systematic review and meta-analysis. Eur. J. Obstet. Gynecol. Reprod. Biol. 2016, 197, 147–155.
  31. Narice, B.F.; Delaney, B.; Dickson, J.M. Endometrial sampling in low-risk patients with abnormal uterine bleeding: A systematic review and meta-synthesis. BMC Fam. Pract. 2018, 19, 135.
  32. Gordon, S.; Westgate, J. The incidence and management of failed Pipelle sampling in a general outpatient clinic. Aust. N. Z. J. Obstet. Gynaecol. 1999, 39, 115–118.
  33. Visser, N.C.; Breijer, M.C.; Herman, M.C.; Bekkers, R.L.; Veersema, S.; Opmeer, B.C.; Mol, B.W.; Timmermans, A.; Pijnenborg, J.M. Factors attributing to the failure of endometrial sampling in women with postmenopausal bleeding. Acta Obstet. Gynecol. Scand. 2013, 92, 1216–1222.
  34. Williams, A.R.W.; Brechin, S.; Porter, A.J.L.; Warner, P.; Critchley, H.O.D. Factors affecting adequacy of Pipelle and Tao Brush endometrial sampling. BJOG Int. J. Obstet. Gynaecol. 2008, 115, 1028–1036.
  35. Van Doorn, H.C.; Opmeer, B.C.; Burger, C.W.; Duk, M.J.; Kooi, G.S.; Mol, B.W.J. Dutch Study in Postmenopausal Bleeding (DUPOMEB) Inadequate office endometrial sample requires further evaluation in women with postmenopausal bleeding and abnormal ultrasound results. Int. J. Gynecol. Obstet. 2007, 99, 100–104.
  36. Mohanlal, R.D. Endometrial sampling at an academic hospital in South Africa: Histological findings, lessons learnt and interesting surprises. Afr. J. Lab. Med. 2020, 9, 7.
  37. Du, J.; Li, Y.; Lv, S.; Wang, Q.; Sun, C.; Dong, X.; He, M.; Ulain, Q.; Yuan, Y.; Tuo, X.; et al. Endometrial sampling devices for early diagnosis of endometrial lesions. J. Cancer Res. Clin. Oncol. 2016, 142, 2515–2522.
  38. Clark, T.J.; Mann, C.H.; Shah, N.; Khan, K.S.; Song, F.; Gupta, J.K. Accuracy of outpatient endometrial biopsy in the diagnosis of endometrial cancer: A systematic quantitative review. BJOG: Int. J. Obstet. Gynaecol. 2002, 109, 313–321.
  39. Dijkhuizen, F.P.; Mol, B.W.; Brölmann, H.A.; Heintz, A.P. The accuracy of endometrial sampling in the diagnosis of patients with endometrial carcinoma and hyperplasia: A meta-analysis. Cancer 2000, 89, 1765–1772.
  40. Feldman, S.; Berkowitz, R.S.; Tosteson, A.N. Cost-effectiveness of strategies to evaluate postmenopausal bleeding. Obstet. Gynecol. 1993, 81, 968–975.
  41. Clark, T.J.; Mann, C.H.; Shah, N.; Khan, K.S.; Song, F.; Gupta, J.K. Accuracy of outpatient endometrial biopsy in the diagnosis of endometrial hyperplasia. Acta Obstet. Gynecol. Scand. 2001, 80, 784–793.
  42. Wan, Y.L.; Beverley-Stevenson, R.; Carlisle, D.; Clarke, S.; Edmondson, R.J.; Glover, S.; Holland, J.; Hughes, C.; Kitchener, H.C.; Kitson, S.; et al. Working together to shape the endometrial cancer research agenda: The top ten unanswered research questions. Gynecol. Oncol. 2016, 143, 287–293.
  43. World Health Organisation. Screening. Available online: (accessed on 15 May 2021).
  44. Møller, P.; Seppälä, T.; Bernstein, I.; Holinski-Feder, E.; Sala, P.; Evans, D.G.; Lindblom, A.; Macrae, F.; Blanco, I.; Sijmons, R.; et al. Cancer incidence and survival in Lynch syndrome patients receiving colonoscopic and gynaecological surveillance: First report from the prospective Lynch syndrome database. Gut 2017, 66, 464–472.
  45. Jacobs, I.; Gentry-Maharaj, A.; Burnell, M.; Manchanda, R.; Singh, N.; Sharma, A.; Ryan, A.; Seif, M.W.; Amso, N.; Turner, G.; et al. Sensitivity of transvaginal ultrasound screening for endometrial cancer in postmenopausal women: A case-control study within the UKCTOCS cohort. Lancet Oncol. 2010, 12, 38–48.
  46. Clark, T.; Barton, P.; Coomarasamy, A.; Gupta, J.; Khan, K. Gynaecological oncology: Investigating postmenopausal bleeding for endometrial cancer: Cost-effectiveness of initial diagnostic strategies. BJOG Int. J. Obstet. Gynaecol. 2006, 113, 502–510.
  47. Jacobs, I.J.; Menon, U.; Ryan, A.; Gentry-Maharaj, A.; Burnell, M.; Kalsi, J.K.; Amso, N.; Apostolidou, S.; Benjamin, E.; Cruickshank, D.; et al. Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): A randomised controlled trial. Lancet 2016, 387, 945–956.
  48. Mantsch, H.H. Foreword. In Biomedical Applications of Synchrotron Infrared Microspectroscopy; Analytical Spectroscopy Series; Moss, D., Ed.; The Royal Society of Chemistry: London, UK, 2011.
  49. Baker, M.J.; Byrne, H.J.; Chalmers, J.; Gardner, P.; Goodacre, R.; Henderson, A.; Kazarian, S.G.; Martin, F.L.; Moger, J.; Stone, N.; et al. Clinical applications of infrared and Raman spectroscopy: State of play and future challenges. Analyst 2018, 143, 1735–1757.
  50. Cui, S.; Zhang, S.; Yue, S. Raman Spectroscopy and Imaging for Cancer Diagnosis. J. Health Eng. 2018, 2018, 8619342.
  51. Baker, M.J.; Trevisan, J.; Bassan, P.; Bhargava, R.; Butler, H.J.; Dorling, K.M.; Fielden, P.R.; Fogarty, S.W.; Fullwood, N.J.; Heys, K.A.; et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 2014, 9, 1771–1791.
  52. Mitchell, A.L.; Gajjar, K.B.; Theophilou, G.; Martin, F.L.; Martin-Hirsch, P.L. Vibrational spectroscopy of biofluids for disease screening or diagnosis: Translation from the laboratory to a clinical setting. J. Biophotonics 2014, 7, 153–165.
  53. The International Organisation for Standardisation. ISO 20473: 2007 Optics and Photonics. Spectral Bands 2007, 10. Available online: (accessed on 1 May 2021).
  54. Stuart, B. Infrared Spectroscopy: Fundamentals and Applications; John Wiley & Sons Ltd.: Chichester, UK, 2004.
  55. Kazarian, S.G.; Chan, K.L.A. ATR-FTIR spectroscopic imaging: Recent advances and applications to biological systems. Analyst 2013, 138, 1940–1951.
  56. Filik, J.; Frogley, M.D.; Pijanka, J.K.; Wehbe, K.; Cinque, G. Electric field standing wave artefacts in FTIR micro-spectroscopy of biological materials. Analyst 2012, 137, 853–861.
  57. Bassan, P.; Lee, J.; Sachdeva, A.; Pissardini, J.; Dorling, K.M.; Fletcher, J.S.; Henderson, A.; Gardner, P. The inherent problem of transflection-mode infrared spectroscopic microscopy and the ramifications for biomedical single point and imaging applications. Analyst 2013, 138, 144–157.
  58. Raman, C.V.; Krishnan, K.S. A New Type of Secondary Radiation. Nature 1928, 121, 501–502.
  59. Patel, I.I.; Trevisan, J.; Evans, G.; Llabjani, V.; Martin-Hirsch, P.L.; Stringfellow, H.F.; Martin, F.L. High contrast images of uterine tissue derived using Raman microspectroscopy with the empty modelling approach of multivariate curve resolution-alternating least squares. Analyst 2011, 136, 4950–4959.
  60. Kirsch, M.; Schackert, G.; Salzer, R.; Krafft, C. Raman spectroscopic imaging for in vivo detection of cerebral brain metastases. Anal. Bioanal. Chem. 2010, 398, 1707–1713.
  61. Cui, L.; Butler, H.J.; Martin-Hirsch, P.L.; Martin, F.L. Aluminium foil as a potential substrate for ATR-FTIR, transflection FTIR or Raman spectrochemical analysis of biological specimens. Anal. Methods 2016, 8, 481–487.
  62. Butler, H.J.; Ashton, L.; Bird, B.; Cinque, G.; Curtis, K.; Dorney, J.; Esmonde-White, K.; Fullwood, N.J.; Gardner, B.; Martin-Hirsch, P.L.; et al. Using Raman spectroscopy to characterize biological materials. Nat. Protoc. 2016, 11, 664–687.
  63. Crow, P.; Stone, N.; Kendall, C.A.; Uff, J.S.; Farmer, J.A.M.; Barr, H.; Wright, M.P.J. The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro. Br. J. Cancer 2003, 89, 106–108.
  64. Theophilou, G.; Lima, K.M.G.; Briggs, M.; Martin-Hirsch, P.L.; Stringfellow, H.F.; Martin, F.L. A biospectroscopic analysis of human prostate tissue obtained from different time periods points to a trans-generational alteration in spectral phenotype. Sci. Rep. 2015, 5, 13465.
  65. Patel, I.I.; Trevisan, J.; Singh, P.B.; Nicholson, C.M.; Krishnan, R.K.G.; Matanhelia, S.S.; Martin, F.L. Segregation of human prostate tissues classified high-risk (UK) versus low-risk (India) for adenocarcinoma using Fourier-transform infrared or Raman microspectroscopy coupled with discriminant analysis. Anal. Bioanal. Chem. 2011, 401, 969–982.
  66. Güler, G.; Guven, U.; Oktem, G. Characterization of CD133+/CD44+ human prostate cancer stem cells with ATR-FTIR spectroscopy. Analyst 2019, 144, 2138–2149.
  67. German, M.; Hammiche, A.; Ragavan, N.; Tobin, M.; Cooper, L.J.; Matanhelia, S.S.; Hindley, A.C.; Nicholson, C.M.; Fullwood, N.J.; Pollock, H.M.; et al. Infrared Spectroscopy with Multivariate Analysis Potentially Facilitates the Segregation of Different Types of Prostate Cell. Biophys. J. 2006, 90, 3783–3795.
  68. Bergholt, M.S.; Zheng, W.; Lin, K.; Ho, K.Y.; Teh, M.; Yeoh, K.G.; So, J.; Huang, Z. In Vivo Diagnosis of Esophageal Cancer Using Image-Guided Raman Endoscopy and Biomolecular Modeling. Technol. Cancer Res. Treat. 2011, 10, 103–112.
  69. Yao, H.-W.; Liu, Y.-Q.; Fu, W.; Shi, X.-Y.; Zhang, Y.-F.; Xu, Y.-Z. Initial research on Fourier transform infrared spectroscopy for the diagnosis of colon neoplasms. Guang Pu Xue Yu Guang Pu Fen Xi Guang Pu 2011, 31, 297–301.
  70. Sahu, R.K.; Argov, S.; Walfisch, S.; Bogomolny, E.; Moreh, R.; Mordechai, S. Prediction potential of IR-micro spectroscopy for colon cancer relapse. Analyst 2010, 135, 538–544.
  71. Nallala, J.; Piot, O.; Diebold, M.-D.; Gobinet, C.; Bouché, O.; Manfait, M.; Sockalingum, G.D. Infrared and Raman Imaging for Characterizing Complex Biological Materials: A Comparative Morpho-Spectroscopic Study of Colon Tissue. Appl. Spectrosc. 2014, 68, 57–68.
  72. Maziak, D.E.; Do, M.T.; Shamji, F.M.; Sundaresan, S.R.; Perkins, D.G.; Wong, P.T. Fourier-transform infrared spectroscopic study of characteristic molecular structure in cancer cells of esophagus: An exploratory study. Cancer Detect. Prev. 2007, 31, 244–253.
  73. Cameron, J.M.; Butler, H.J.; Smith, B.R.; Hegarty, M.G.; Jenkinson, M.D.; Syed, K.; Brennan, P.M.; Ashton, K.; Dawson, T.; Palmer, D.S.; et al. Developing infrared spectroscopic detection for stratifying brain tumour patients: Glioblastoma multiforme vs. lymphoma. Analyst 2019, 144, 6736–6750.
  74. Bury, D.; Morais, C.L.M.; Martin, F.L.; Lima, K.M.G.; Ashton, K.M.; Baker, M.J.; Dawson, T.P. Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model. Br. J. Neurosurg. 2020, 34, 40–45.
  75. Kong, K.; Zaabar, F.; Rakha, E.; Ellis, I.; Koloydenko, A.; Notingher, I. Towards intra-operative diagnosis of tumours during breast conserving surgery by selective-sampling Raman micro-spectroscopy. Phys. Med. Biol. 2014, 59, 6141–6152.
  76. Zúñiga, W.C.; Jones, V.; Anderson, S.M.; Echevarria, A.; Miller, N.L.; Stashko, C.; Schmolze, D.; Cha, P.D.; Kothari, R.; Fong, Y.; et al. Raman Spectroscopy for Rapid Evaluation of Surgical Margins during Breast Cancer Lumpectomy. Sci. Rep. 2019, 9, 14639.
  77. Nargis, H.; Nawaz, H.; Ditta, A.; Mahmood, T.; Majeed, M.; Rashid, N.; Muddassar, M.; Bhatti, H.; Saleem, M.; Jilani, K.; et al. Raman spectroscopy of blood plasma samples from breast cancer patients at different stages. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2019, 222, 117210.
  78. Lyng, F.M.; Traynor, D.; Nguyen, T.N.Q.; Meade, A.; Rakib, F.; Al-Saady, R.; Goormaghtigh, E.; Al-Saad, K.; Ali, M.H. Discrimination of breast cancer from benign tumours using Raman spectroscopy. PLoS ONE 2019, 14, e0212376.
  79. Walsh, M.J.; Holton, S.E.; Kajdacsy-Balla, A.; Bhargava, R. Attenuated total reflectance Fourier-transform infrared spectroscopic imaging for breast histopathology. Vib. Spectrosc. 2012, 60, 23–28.
  80. Tian, P.; Zhang, W.; Zhao, H.; Lei, Y.; Cui, L.; Wang, W.; Li, Q.; Zhu, Q.; Zhang, Y.; Xu, Z. Intraoperative diagnosis of benign and malignant breast tissues by fourier transform infrared spectroscopy and support vector machine classification. Int. J. Clin. Exp. Med. 2015, 8, 972–981.
  81. Depciuch, J.; Kaznowska, E.; Zawlik, I.; Wojnarowska-Nowak, R.; Cholewa, M.; Heraud, P.; Cebulski, J. Application of Raman Spectroscopy and Infrared Spectroscopy in the Identification of Breast Cancer. Appl. Spectrosc. 2016, 70, 251–263.
  82. Depciuch, J.; Kaznowska, E.; Golowski, S.; Koziorowska, A.; Zawlik, I.; Cholewa, M.; Szmuc, K.; Cebulski, J. Monitoring breast cancer treatment using a Fourier transform infrared spectroscopy-based computational model. J. Pharm. Biomed. Anal. 2017, 143, 261–268.
  83. Huang, Z.; McWilliams, A.; Lui, H.; McLean, D.I.; Lam, S.; Zeng, H. Near-infrared Raman spectroscopy for optical diagnosis of lung cancer. Int. J. Cancer 2003, 107, 1047–1052.
  84. Sun, X.; Xu, Y.; Wu, J.; Zhang, Y.; Sun, K. Detection of lung cancer tissue by attenuated total reflection–Fourier transform infrared spectroscopy—A pilot study of 60 samples. J. Surg. Res. 2013, 179, 33–38.
  85. Lui, H.; Zhao, J.; McLean, D.; Zeng, H. Real-time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis. Cancer Res. 2012, 72, 2491–2500.
  86. Lima, C.A.; Goulart, V.P.; Côrrea, L.; Pereira, T.M.; Zezell, D.M. ATR-FTIR Spectroscopy for the Assessment of Biochemical Changes in Skin Due to Cutaneous Squamous Cell Carcinoma. Int. J. Mol. Sci. 2015, 16, 6621–6630.
  87. Lima, C.A.; Goulart, V.P.; Correa, L.; Zezell, D.M. Using Fourier transform infrared spectroscopy to evaluate biological effects induced by photodynamic therapy. Lasers Surg. Med. 2016, 48, 538–545.
  88. Walsh, M.J.; Singh, M.N.; Pollock, H.M.; Cooper, L.J.; German, M.J.; Stringfellow, H.F.; Fullwood, N.J.; Paraskevaidis, E.; Martin-Hirsch, P.L.; Martin, F.L. ATR microspectroscopy with multivariate analysis segregates grades of exfoliative cervical cytology. Biochem. Biophys. Res. Commun. 2007, 352, 213–219.
  89. Kelly, J.G.; Angelov, P.P.; Trevisan, J.; Vlachopoulou, A.; Paraskevaidis, E.; Martin-Hirsch, P.L.; Martin, F.L. Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass. Anal. Bioanal. Chem. 2010, 398, 2191–2201.
  90. Kelly, J.G.; Cheung, K.T.; Martin, C.; O’Leary, J.J.; Prendiville, W.; Martin-Hirsch, P.L.; Martin, F.L. A spectral phenotype of oncogenic human papillomavirus-infected exfoliative cervical cytology distinguishes women based on age. Clin. Chim. Acta 2010, 411, 1027–1033.
  91. Halliwell, D.E.; Morais, C.L.; Lima, K.M.; Trevisan, J.; Siggel-King, M.R.; Craig, T.; Ingham, J.; Martin, D.S.; Heys, K.; Kyrgiou, M.; et al. An imaging dataset of cervical cells using scanning near-field optical microscopy coupled to an infrared free electron laser. Sci. Data 2017, 4, 170084.
  92. Ramos, I.R.; Meade, A.D.; Ibrahim, O.; Byrne, H.J.; McMenamin, M.; McKenna, M.; Malkin, A.; Lyng, F.M. Raman spectroscopy for cytopathology of exfoliated cervical cells. Faraday Discuss. 2016, 187, 187–198.
  93. Krishna, C.M.; Prathima, N.B.; Malini, R.; Vadhiraja, B.M.; Bhatt, R.A.; Fernandes, D.J.; Kushtagi, P.; Vidyasagar, M.S.; Kartha, V.B. Raman spectroscopy studies for diagnosis of cancers in human uterine cervix. Vib. Spectrosc. 2006, 41, 136–141.
  94. Paraskevaidi, M.; Ashton, K.M.; Stringfellow, H.F.; Wood, N.J.; Keating, P.J.; Rowbottom, A.W.; Martin-Hirsch, P.L.; Martin, F.L. Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma. Talanta 2018, 189, 281–288.
  95. Lima, K.M.G.; Gajjar, K.B.; Martin-Hirsch, P.L.; Martin, F.L. Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR-FTIR spectroscopy coupled with variable selection methods. Biotechnol. Prog. 2015, 31, 832–839.
  96. Frost, J.; Ludeman, L.; Hillaby, K.; Gornall, R.; Lloyd, G.; Kendall, C.; Shore, A.C.; Stone, N. Raman spectroscopy and multivariate analysis for the non invasive diagnosis of clinically inconclusive vulval lichen sclerosus. Analyst 2016, 142, 1200–1206.
  97. Theophilou, G.; Morais, C.L.M.; Halliwell, D.E.; Lima, K.M.G.; Drury, J.; Martin-Hirsch, P.L.; Stringfellow, H.F.; Hapangama, D.K.; Martin, F.L. Synchrotron- and focal plane array-based Fourier-transform infrared spectroscopy differentiates the basalis and functionalis epithelial endometrial regions and identifies putative stem cell regions of human endometrial glands. Anal. Bioanal. Chem. 2018, 410, 4541–4554.
  98. Barnas, E.; Skret-Magierlo, J.; Skret, A.; Kaznowska, E.; Depciuch, J.; Szmuc, K.; Łach, K.; Krawczyk-Marć, I.; Cebulski, J. Simultaneous FTIR and Raman Spectroscopy in Endometrial Atypical Hyperplasia and Cancer. Int. J. Mol. Sci. 2020, 21, 4828.
  99. Kelly, J.G.; Singh, M.; Stringfellow, H.F.; Walsh, M.J.; Nicholson, J.M.; Bahrami, F.; Ashton, K.M.; Pitt, M.A.; Martin-Hirsch, P.L.; Martin, F.L. Derivation of a subtype-specific biochemical signature of endometrial carcinoma using synchrotron-based Fourier-transform infrared microspectroscopy. Cancer Lett. 2009, 274, 208–217.
  100. Taylor, S.E.; Cheung, K.T.; Patel, I.I.; Trevisan, J.; Stringfellow, H.F.; Ashton, K.M.; Wood, N.J.; Keating, P.J.; Martin-Hirsch, P.L.; Martin, F.L. Infrared spectroscopy with multivariate analysis to interrogate endometrial tissue: A novel and objective diagnostic approach. Br. J. Cancer 2011, 104, 790–797.
  101. Murali Krishna, C.; Kegelaer, G.; Adt, I.; Rubin, S.; Kartha, V.; Manfait, M.; Sockalingum, G. Characterisation of uterine sarcoma cell lines exhibiting MDR phenotype by vibrational spectroscopy. Biochim. Biophys. Acta (BBA)—Gen. Subj. 2005, 1726, 160–167.
  102. Baker, M.J.; Hussain, S.R.; Lovergne, L.; Untereiner, V.; Hughes, C.; Lukaszewski, R.A.; Thiéfin, G.; Sockalingum, G.D. Developing and understanding biofluid vibrational spectroscopy: A critical review. Chem. Soc. Rev. 2016, 45, 1803–1818.
  103. Owens, G.; Gajjar, K.; Trevisan, J.; Fogarty, S.W.; Taylor, S.E.; Da Gama-Rose, B.; Martin-Hirsch, P.L.; Martin, F.L. Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients. J. Biophotonics 2014, 7, 200–209.
  104. Taleb, I.; Thiéfin, G.; Gobinet, C.; Untereiner, V.; Bernard-Chabert, B.; Heurgué, A.; Truntzer, C.; Hillon, P.; Manfait, M.; Ducoroy, P.; et al. Diagnosis of hepatocellular carcinoma in cirrhotic patients: A proof-of-concept study using serum micro-Raman spectroscopy. Analyst 2013, 138, 4006–4014.
  105. Gajjar, K.; Trevisan, J.; Owens, G.; Keating, P.J.; Wood, N.J.; Stringfellow, H.F.; Martin-Hirsch, P.L.; Martin, F.L. Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: A novel diagnostic approach for ovarian cancer. Analyst 2013, 138, 3917–3926.
  106. Hands, J.R.; Clemens, G.; Stables, R.; Ashton, K.; Brodbelt, A.; Davis, C.; Dawson, T.P.; Jenkinson, M.D.; Lea, R.W.; Walker, C.; et al. Brain tumour differentiation: Rapid stratified serum diagnostics via attenuated total reflection Fourier-transform infrared spectroscopy. J. Neuro-Oncol. 2016, 127, 463–472.
  107. Yu, M.-C.; Rich, P.; Foreman, L.; Smith, J.; Tanna, A.; Dibbur, V.; Unwin, R.; Tam, F.W.K. Label Free Detection of Sensitive Mid-Infrared Biomarkers of Glomerulonephritis in Urine Using Fourier Transform Infrared Spectroscopy. Sci. Rep. 2017, 7, 4601.
  108. Paraskevaidi, M.; Morais, C.L.M.; Lima, K.M.G.; Ashton, K.M.; Stringfellow, H.F.; Martin-Hirsch, P.L.; Martin, F.L. Potential of mid-infrared spectroscopy as a non-invasive diagnostic test in urine for endometrial or ovarian cancer. Analyst 2018, 143, 3156–3163.
  109. Bel’Skaya, L.V.; Sarf, E.A.; Solomatin, D.V.; Kosenok, V.K. Analysis of the lipid profile of saliva in ovarian and endometrial cancer by IR fourier spectroscopy. Vib. Spectrosc. 2019, 104, 102944.
  110. Parlatan, U.; Inanc, M.T.; Ozgor, B.Y.; Oral, E.; Bastu, E.; Unlu, M.B.; Basar, G. Raman spectroscopy as a non-invasive diagnostic technique for endometriosis. Sci. Rep. 2019, 9, 19795.
  111. Paraskevaidi, M.; Morais, C.; Raglan, O.; Lima, K.M.G.; Martin-Hirsch, P.L.; Paraskevaidis, E.; Kyrgiou, M.; Martin, F.L. Spectroscopy of blood samples for the diagnosis of endometrial cancer and classification of its different subtypes. J. Clin. Oncol. 2017, 35, 5596.
  112. Paraskevaidi, M.; Morais, C.L.M.; Raglan, O.; Lima, K.M.G.; Paraskevaidis, E.; Martin-Hirsch, P.L.; Kyrgiou, M.; Martin, F.L. Aluminium foil as an alternative substrate for the spectroscopic interrogation of endometrial cancer. J. Biophotonics 2018, 11, e201700372.
  113. Paraskevaidi, M.; Morais, C.L.M.; Ashton, K.M.; Stringfellow, H.F.; McVey, R.J.; Ryan, N.A.J.; O’Flynn, H.; Sivalingam, V.N.; Kitson, S.J.; Mackintosh, M.L.; et al. Detecting Endometrial Cancer by Blood Spectroscopy: A Diagnostic Cross-Sectional Study. Cancers 2020, 12, 1256.
  114. Mabwa, D.; Gajjar, K.; Furniss, D.; Schiemer, R.; Crane, R.; Fallaize, C.; Martin-Hirsch, P.L.; Martin, F.L.; Kypraios, T.; Seddon, A.B.; et al. Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples. Analyst 2021, 146, 5631–5642.
  115. Baker, M.J.; Gazi, E.; Brown, M.D.; Shanks, J.H.; Gardner, P.; Clarke, N.W. FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer. Br. J. Cancer 2008, 99, 1859–1866.
  116. Fabian, H.; Thi, N.A.N.; Eiden, M.; Lasch, P.; Schmitt, J.; Naumann, D. Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy. Biochim. Biophys. Acta (BBA)—Biomembr. 2006, 1758, 874–882.
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