Cerebrospinal fluid metabolomics and machine learning identify novel biomarkers for lung cancer leptomeningeal metastasis
CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.
Authors
- Mengyi Tang
- Hong Cai (ORCID: https://orcid.org/0000-0002-2711-9461)
- Qi Wang (ORCID: https://orcid.org/0000-0002-4031-7307)
- Wénwén Liú (ORCID: https://orcid.org/0000-0001-7239-0851)
- Jian Wang (ORCID: https://orcid.org/0000-0002-9482-5227)
- Yinuo Jin
- Dan Liu (ORCID: https://orcid.org/0000-0003-1280-309X)
- Chunhui Yang (ORCID: https://orcid.org/0000-0003-1597-8469)
- Peng Gao (ORCID: https://orcid.org/0000-0002-5085-7112)
- Xin You (ORCID: https://orcid.org/0000-0001-7704-8676)
- Zeming Wu (ORCID: https://orcid.org/0000-0003-4265-4638)
Institutions
- Dalian Institute of Chemical Physics (CN)
- Dalian Medical University (CN)
- Chinese Academy of Sciences (CN)
- Tarim University (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
- Liaoning Ocean and Fisheries Research Institute (CN)
Publication Details
- Journal
- Neuro-Oncology
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1093/neuonc/noaf270
- Citations
- 2
- Primary Topic
- Brain Metastases and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.04