ArticleMetabolites2021
Experimental Assessment of Leptomeningeal Metastasis Diagnosis in Medulloblastoma Using Cerebrospinal Fluid Metabolomic Profiles.
Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Advances in cerebrospinal fluid biomarkers for the diagnosis, treatment and monitoring of leptomeningeal metastases (Review).Oncology letters · 2026Review
- Proteomic profiling of cerebrospinal fluid reveals TKT as a potential biomarker for medulloblastoma.Scientific reports · 2024Article
- Monitoring central nervous system tumour metabolism using cerebrospinal fluid.Frontiers in oncology · 2024Review
- Metabolite profiling of Borneo's Gonystylus bancanus through comprehensive extraction from various polarity of solvents.Scientific reports · 2023Article
- Review
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
Diagnosing leptomeningeal metastasis (LM) in medulloblastoma is currently based on positive cerebrospinal fluid (CSF) cytology or magnetic resonance imaging (MRI) finding. However, the relevance of discordant results has not been established. We evaluated the diagnostic potential of CSF metabolomic profiles in the medulloblastoma LM assessment. A total of 83 CSF samples from medulloblastoma patients with documented MRI and CSF cytology results at the time of sampling for LM underwent low-mass ions (LMIs) analysis using liquid chromatography-mass spectrometry. Discriminating LMIs were selected by a summed sensitivity and specificity (>160%) and LMI discriminant equation (LOME) algorithms, evaluated by measuring diagnostic accuracy for verifying LM groups of different MRI/cytology results. Diagnostic accuracy of LM in medulloblastoma was 0.722 for cytology and 0.889 for MRI. Among 6572 LMIs identified in all sample, we identified 27 discriminative LMIs differentiating MRI (+)/cytology (+) from MRI (-)/cytology (-). Using LMI discriminant equation (LOME) analysis, we selected 9 LMIs with a sensitivity of 100% and a specificity of 93.6% for differentiating MRI (+)/cytology (+) from MRI (-)/cytology (-). Another LOME of 20 LMIs significantly differentiated sampling time relative to treatment (
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Registered trials
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