Division of Child Health, Obstetrics and Gynaecology, University of Nottingham, Nottingham NG5 1PB, UK.
Mid-Infrared Photonics Group, George Green Institute for Electromagnetics Research, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
Int J Mol Sci. 2022 Apr 27;23(9):4859. doi: 10.3390/ijms23094859.
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.
子宫内膜癌(EC)是全球第六大常见癌症,也是女性死亡的第四大主要原因。早期发现和治疗与良好的预后和降低死亡率相关。然而,与其他常见癌症不同,筛查策略缺乏在临床实践中成功实施所需的敏感性、特异性和准确性,并且目前的诊断方法具有侵入性、昂贵且耗时。这些局限性突出表明,需要开发用于 EC 的诊断和筛查替代方法,这些方法应该准确、快速、微创且具有成本效益。振动光谱技术、中红外吸收光谱和拉曼光谱利用光与生物样本相互作用引起的原子振动吸收,生成独特的光谱响应:“生物化学指纹”。这些是非破坏性技术,并且结合多元统计分析,在过去十年中已被证明可在癌症和健康样本之间进行区分,这表明它们在癌症筛查和诊断方面具有广阔的应用前景。本综述的目的是汇集现有证据,以便深入了解振动生物光谱学在子宫内膜癌诊断和筛查中的应用现状,并评估未来前景。