Dataset Information

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.

SUBMITTER: Ramirez CAM

PROVIDER: S-EPMC9600188 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Ramirez Carlos A Meza CAM Stringfellow Helen H Naik Raj R Crosbie Emma J EJ Paraskevaidi Maria M Rehman Ihtesham U IU Martin-Hirsch Pierre P

Cancers 20221013 20

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 refl ...[more]

PMID: 36291799

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Project description:Near-infrared spectroscopy combined with chemometrics was applied to construct a hybrid model for the non-invasive detection of protein content in different types of plant feed materials. In total, 829 samples of plant feed materials, which included corn distillers' dried grains with solubles (DDGS), corn germ meal, corn gluten meal, distillers' dried grains (DDG) and rapeseed meal, were collected from markets in China. Based on the different preprocessed spectral data, specific models for each type of plant feed material and a hybrid model for all the materials were built. Performances of specific model and hybrid model constructed with full spectrum (full spectrum model) and selected wavenumbers with VIP (variable importance in the projection) scores value bigger than 1.00 (VIP scores model) were also compared. The best spectral preprocessing method for this study was found to be the standard normal variate transformation combined with the first derivative. For both full spectrum and VIP scores model, the prediction performance of the hybrid model was slightly worse than those of the specific models but was nevertheless satisfactory. Moreover, the VIP scores model obtained generally better performances than corresponding full spectrum model. Wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were found to be the key wavenumbers in modeling protein content in these plant feed materials. The values for the root mean square error of prediction (RMSEP) and the relative prediction deviation (RPD) obtained with the VIP scores hybrid model were 1.05% and 2.53 for corn DDGS, 0.98% and 4.17 for corn germ meal, 0.75% and 6.99 for corn gluten meal, 1.54% and 4.59 for DDG, and 0.90% and 3.33 for rapeseed meal, respectively. The results of this study demonstrate that the protein content in several types of plant feed materials can be determined using a hybrid near-infrared spectroscopy model. And VIP scores method can be used to improve the general predictability of hybrid model.

Dataset Information

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

Publications

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

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