Evidence map›Paper›PMID 29564749›Full record

ArticleJournal of neuro-oncology2018

Characteristics of time-activity curves obtained from dynamic

Yuichi Nomura, Yoshitaka Asano, Jun Shinoda, Hirohito Yano, Yuka Ikegame, Tomohiro Kawasaki, Noriyuki Nakayama, Takashi Maruyama, Yoshihiro Muragaki, Toru Iwama

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In one paragraph

Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 12% 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Guideline
  3. Review
  4. Article
  5. Review
  6. Article
  7. EJNMMI reports · 2024
    Article
  8. Article
  9. Review
  10. Review
  11. Dynamic amino-acid PET in neuro-oncology: a prognostic tool becomes essential.European journal of nuclear medicine and molecular imaging · 2021
    Article
  12. DynamicCancers · 2021
    Article
  13. Article
  14. DynamicFrontiers in oncology · 2021
    Article
  15. Article
  16. Integration of dynamic parameters in the analysis ofEuropean journal of nuclear medicine and molecular imaging · 2020
    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

10 authors at 3 institutions in 1 country.

Yuichi NomuraChubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, Gifu, 505-0034, Japan. roka970201@yahoo.co.jp.ORCID http://orcid.org/0000-0003-2301-2680
Yoshitaka AsanoChubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, Gifu, 505-0034, Japan.
Jun ShinodaChubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, Gifu, 505-0034, Japan.
Hirohito YanoDepartment of Neurosurgery, Gifu University Graduate School of Medicine, Gifu, Japan.
Yuka IkegameChubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, Gifu, 505-0034, Japan.
Tomohiro KawasakiChubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, 630 Shimo-kobi, Kobi-cho, Minokamo, Gifu, 505-0034, Japan.
Noriyuki NakayamaDepartment of Neurosurgery, Gifu University Graduate School of Medicine, Gifu, Japan.
Takashi MaruyamaDepartment of Neurosurgery, Tokyo Women's Medical University, Tokyo, Japan.
Yoshihiro MuragakiDepartment of Neurosurgery, Tokyo Women's Medical University, Tokyo, Japan.
Toru IwamaDepartment of Neurosurgery, Gifu University Graduate School of Medicine, Gifu, Japan.
Gifu University · JPTokyo Women's Medical University · JPObayashi (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study was to assess whether dynamic PET with

methodsOne hundred sixty patients with brain tumors (139 gliomas, 9 meningiomas, 4 hemangioblastomas and 8 primary central nervous system lymphomas [PCNSL]) underwent dynamic MET-PET with a 3-dimensional acquisition mode, and the maximum tumor MET-standardized uptake value (MET-SUV) was measured consecutively to construct a time-activity curve (TAC). Furthermore, receiver operating characteristic (ROC) curves were generated from the time-to-peak (TTP) and the slope of the curve in the late phase (SLOPE).

resultsThe TAC patterns of MET-SUVs (MET-TACs) could be divided into four characteristic types when MET dynamics were analyzed by dividing the MET-TAC into three phases. MET-SUVs were significantly higher in early and late phases in glioblastoma compared to anaplastic astrocytoma, diffuse astrocytoma and the normal frontal cortex (P < 0.05). The SLOPE in the late phase was significantly lower in tumors that included an oligodendroglial component compared to astrocytic tumors (P < 0.001). When we set the cutoff of the SLOPE in the late phase to - 0.04 h

conclusionsThe results of this study show that quantification of the MET-TAC for each brain tumor identified by a dynamic MET-PET study could be helpful in the non-invasive discrimination of brain tumor subtypes, in particular gliomas.

Indexed as

Carbon RadioisotopesMethioninePositron-Emission TomographyRadiopharmaceuticalsAdultAgedBrainBrain NeoplasmsFemaleGliomaHemangioblastomaHumansImaging, Three-DimensionalLymphomaMaleMeningeal NeoplasmsCarbon-11Carbon RadioisotopesMethionineRadiopharmaceuticals3D-acquisition modeBrain tumorDynamic PETMethionine-PETTime activity curve

Identifiers

PMID29564749
OpenAlexW2794165115

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.