Carapito, Â.Ferreira, V. S. FernandesFerreira, A. C. SilvaTeixeira-Marques, A.Henrique, R.Jerónimo, C.Roque, A. C. A.Carvalho, F.Pinto, J.Pinho, P. Guedes de2025-09-172025-09-172026-01-01Carapito, Â., Ferreira, V. S. F., Ferreira, A. C. S., & Teixeira-Marques, A. et al. (2026). Non-invasive bladder cancer detection: identification of a urinary volatile biomarker panel using GC-MS metabolomics and machine learning. Talanta, 297, Article 128749. https://doi.org/10.1016/j.talanta.2025.1287490039-9140304def1c-2351-4e01-8a5d-397cddd9fe4dhttp://hdl.handle.net/10400.14/54992Early detection of bladder cancer (BC) remains a major clinical challenge due to the limitations of current diagnostic methods, which are often invasive, expensive, or insufficiently sensitive, particularly for early-stage disease. Metabolomics approaches, when integrated with machine learning (ML) techniques, offer a powerful platform for identifying novel, non-invasive biomarkers. In this study, urinary volatile organic compounds (VOCs) were analysed from 87 BC patients and 90 age- and sex-matched cancer-free controls using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). Of the 90 VOCs identified, 27 were selected and used to train five ML algorithms—random forest (RF), support vector machine (SVM), partial least squares-discriminant analysis (PLS-DA), extreme gradient boosting (XGBoost), and k-nearest neighbors (k-NN). Model performance was evaluated using cross-validation and an independent validation set, with metrics including area under the curve (AUC), sensitivity, specificity, and accuracy. RF achieved the highest performance using all 27 features (AUC = 0.913; sensitivity, specificity, and accuracy = 85 %). After feature selection, an eight-VOC panel improved performance on the validation set (AUC = 0.872; sensitivity = 89 %, specificity = 92 %, accuracy = 91 %). The panel included ketones, aldehydes, a short fatty alcohol, and a phenol compound—seven elevated in BC, and one (acetone) decreased. This panel outperformed FDA-approved urinary assays and closely matched the specificity of urine cytology. These findings underscore the promise of VOC-based urinary biomarkers, in combination with ML, for the non-invasive detection of BC. Further large-scale validation studies are essential to confirm diagnostic utility and enable clinical translation.engBladder cancerEarly detectionGas chromatography-mass spectrometryMachine learningMetabolomicsUrinary biomarkersVolatile organic compoundsresearch article10.1016/j.talanta.2025.12874910501475065440907368001567306300001