Infrared Spectroscopy of Urine for the Non-Invasive Detection of Endometrial Cancer

Abstract

Current triage for women with post-menopausal bleeding (PMB) to diagnose endometrial cancer rely on specialist referral for intimate tests to sequentially image, visualise and sample the endometrium. A point-of-care non-invasive triage tool with an instant readout could provide immediate reassurance for low-risk symptomatic women, whilst fast-tracking high-risk women for urgent intrauterine investigations. This study assessed the potential for infrared (IR) spectroscopy and attenuated total reflection (ATR) technology coupled with chemometric analysis of the resulting spectra for endometrial cancer detection in urine samples. Standardised urine collection and processing protocols were developed to ensure spectroscopic differences between cases and controls reflected cancer status. Urine spectroscopy distinguished endometrial cancer (n = 109) from benign gynaecological conditions (n = 110) with a sensitivity of 98% and specificity of 97%. If confirmed in subsequent low prevalence studies embedded in PMB clinics, this novel endometrial cancer detection tool could transform clinical practice by accurately selecting women with malignant pathology for urgent diagnostic work up whilst safely reassuring those without.

Keywords

• machine learning
• endometrial cancer detection
• PLS-DA
• cancer
• vibrational spectroscopy
• ATR