Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer.
- DOI
- 10.1038/s44276-026-00244-8
- Published
- 2026 Aug 7
- Container
- BJC reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s44276-026-00244-8,
title = {Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer.},
author = {Deza I and de Lacy Costello B and Drabińska-Fois N and White P and Ratcliffe N and Lazarowicz H and Probert C},
year = {2026},
journal = {BJC reports},
doi = {10.1038/s44276-026-00244-8},
url = {https://doi.org/10.1038/s44276-026-00244-8}
}RIS
TY - JOUR TI - Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer. AU - Deza I AU - de Lacy Costello B AU - Drabińska-Fois N AU - White P AU - Ratcliffe N AU - Lazarowicz H AU - Probert C PY - 2026 JO - BJC reports DO - 10.1038/s44276-026-00244-8 UR - https://doi.org/10.1038/s44276-026-00244-8 ER -
APA
I, D., B, D. L. C., N, D., P, W., N, R., H, L., & C, P. (2026). Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer.. BJC reports. https://doi.org/10.1038/s44276-026-00244-8
Source records
- pubmed · retrieved 2026-09-25T23:53:36.829Z