1. Non-Invasive Endometrial Cancer Screening through Urinary Fluorescent Metabolome Profile Monitoring and Machine Learning Algorithms.
- Author
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Švecová, Monika, Dubayová, Katarína, Birková, Anna, Urdzík, Peter, and Mareková, Mária
- Subjects
RANDOM forest algorithms ,RESEARCH funding ,EARLY detection of cancer ,LOGISTIC regression analysis ,TUMOR markers ,URINE ,DESCRIPTIVE statistics ,ENDOMETRIAL tumors ,SUPPORT vector machines ,URINALYSIS ,METABOLOMICS ,MACHINE learning ,RESOURCE-limited settings ,ALGORITHMS ,FLUORESCENCE spectroscopy ,SENSITIVITY & specificity (Statistics) - Abstract
Simple Summary: The incidence of endometrial cancer is increasing, creating a need for fast and efficient diagnostic methods. This study explores a new, non-invasive approach using urinary fluorescence spectroscopy to detect endometrial cancer. By analyzing morning urine samples and utilizing advanced machine learning techniques, we identified prospective spectral markers that differentiate between control, benign, and malignant gynecological patients. Our findings indicate good sensitivity and specificity, with high AUC from machine learning models, suggesting this method could significantly improve early cancer detection. This approach is easier and more affordable, especially in resource-limited settings. It has the potential to change the way endometrial cancer is diagnosed, offering a simpler and more accessible option for patients. Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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