Evidence map›Paper›PMID 29254157›Full record

ArticleOncotarget2017

Cerebrospinal fluid metabolomic profiles can discriminate patients with leptomeningeal carcinomatosis from patients at high risk for leptomeningeal metastasis.

Byong Chul Yoo, Jun Hwa Lee, Kyung-Hee Kim, Weiwei Lin, Jong Heon Kim, Jong Bae Park, Hyun Jin Park, Sang Hoon Shin, Heon Yoo, Ji Woong Kwon and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.8field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Mechanisms of Organ-Specific Metastasis of Breast Cancer.Cold Spring Harbor perspectives in medicine · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Brain metastasis.Nature reviews. Cancer · 2020
    Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Byong Chul YooBiomarker Branch, Research Institute, National Cancer Center, Goyang, Republic of Korea.
Jun Hwa LeeBiomarker Branch, Research Institute, National Cancer Center, Goyang, Republic of Korea.
Kyung-Hee KimBiomarker Branch, Research Institute, National Cancer Center, Goyang, Republic of Korea.
Weiwei LinDepartment of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang, Republic of Korea.
Jong Heon KimDepartment of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang, Republic of Korea.
Jong Bae ParkDepartment of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang, Republic of Korea.
Hyun Jin ParkCenter for Pediatric Cancer, National Cancer Center, Goyang, Republic of Korea.
Sang Hoon ShinNeuro-oncology Clinic, National Cancer Center, Goyang, Republic of Korea.
Heon YooNeuro-oncology Clinic, National Cancer Center, Goyang, Republic of Korea.
Ji Woong KwonNeuro-oncology Clinic, National Cancer Center, Goyang, Republic of Korea.
Ho-Shin GwakDepartment of Cancer Biomedical Science, National Cancer Center, Graduate School of Cancer Science and Policy, Goyang, Republic of Korea.
National Cancer Center · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEarly diagnosis of leptomeningeal carcinomatosis (LMC) is necessary to improve outcomes of this formidable disease. However, cerebrospinal fluid (CSF) cytology is frequently false negative. We examined whether CSF metabolome profiles can be used to differentiate patients with LMC from patients having a risk for development of LMC.

resultsA total of 10,905 LMIs were evaluated using PCA-DA. The LMIs defined Group 2 with a sensitivity of 85% and a specificity of 91%. After selecting 33 LMIs, including diacetylspermine and fibrinogen fragments, the CSF metabolomics profile had a sensitivity of 100% and a specificity of 93% for discriminating Group 1b from the other groups. After selecting 21 LMIs, including phosphatidylcholine, the CSF metabolomics profile differentiated LMC (Group 2) patients from the high-risk groups of Group 3 and Group 4 with 100% sensitivity and 100% specificity. MATERIALS AND

methodsWe prospectively collected CSF from five groups of patients: Group 1a, systemic cancer; Group 1b, no tumor; Group 2, LMC; Group 3, brain metastasis; Group 4, brain tumor other than brain metastasis. All metabolites in the CSF samples were detected as low-mass ions (LMIs) using mass spectrometry. Principal component analysis-based discriminant analysis (PCA-DA) and two search algorithms were used to select the LMIs that differentiated the patient groups of interest from controls.

conclusionsAnalysis of CSF metabolite profiles could be used to diagnose LMC and exclude patients at high-risk of LMC with a 100% accuracy. We expect a future validation trial to evaluate CSF metabolic profiles supporting CSF cytology.

Indexed as

cerebrospinal fluiddiagnosisleptomeningeal carcinomatosismetabolomeprofile

Identifiers

PMID29254157
PMCPMC5731867
OpenAlexW2754199979

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.