Vibrational Biospectroscopy: An Alternative Approach to 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. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects

Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed of four discriminant methods (LDA{,} QDA{,} kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM). Of particular interest{,} is the impact of discrimination when presented with spectral data from a section of the bio-fingerprint region (1430 cm-1 to 900 cm-1) in contrast to the more extended bio-fingerprint region used here (1800 cm-1 to 900 cm-1). Quality metrics used were the misclassification rate{,} sensitivity{,} specificity{,} and Matthew’s correlation coefficient (MCC). For plasma (with spectral data ranging from 1430 cm-1 to 900 cm-1){,} the best performing classifier was kNN{,} which achieved a sensitivity{,} specificity and MCC of 0.865 ± 0.043{,} 0.865 ± 0.023 and 0.762 ± 0.034{,} respectively. For serum (in the same wavenumber range){,} the best performing classifier was LDA{,} achieving a sensitivity{,} specificity and MCC of 0.899 ± 0.023{,} 0.763 ± 0.048 and 0.664 ± 0.067{,} respectively. For plasma (with spectral data ranging from 1800 cm-1 to 900 cm-1){,} the best performing classifier was SVM{,} with a sensitivity{,} specificity and MCC of 0.993 ± 0.010{,} 0.815 ± 0.000 and 0.815 ± 0.010{,} respectively. For serum (in the same wavenumber range){,} QDA performed best achieving a sensitivity{,} specificity and MCC of 0.852 ± 0.023{,} 0.700 ± 0.162 and 0.557 ± 0.012{,} respectively. Our findings demonstrate that even when a section of the bio-fingerprint region has been removed{,} good classification of endometrial cancer versus non-cancerous controls is still maintained. These findings suggest the potential of a MIR screening tool for endometrial cancer screening