Evidence map›Paper›PMID 11995813›Full record

ArticleJournal of neuro-oncology2002

Ki-67 labeling is correlated with the time to recurrence in primary glioblastomas.

Roland Schröder, Klaus D Feisel, Ralf-Ingo Ernestus

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of neuro-oncology, 2002. 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
–field-weighted citation impact
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.

  1. Article
  2. Article
  3. Article
  4. Evaluation of Akt and RICTOR Expression Levels in Astrocytomas of All Grades.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2017
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Recurrent glioblastoma of childhood treated with bevacizumab: case report and molecular features.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2010
    Article
  10. Article
  11. Article
  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

3 authors.

Roland SchröderDepartment of Neuropathology, University of Cologne, Germany.
Klaus D Feisel
Ralf-Ingo Ernestus

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Ki-67 labeling index (LI) was shown in many cut-off studies to be significantly correlated to the postoperative survival probability in gliomas in univariate and multivariate analyses. However, a direct relationship of the Ki-67 LI and the growth fraction, respectively, to clinical growth parameters was not demonstrated in a single tumor type of this group until now. We compared the Ki-67 LI of 20 primary glioblastomas and their recurrent tumors with the time to reoperation for recurrence. Regression analyses showed (1) a high reproducibility of the LIs at the second versus the first operation corresponding to an inherent growth potential of a given individual tumor and (2) a strong inverse correlation of LI to time to recurrence (TR) (r = -0.92). Additionally to a lower LI (corresponding to a smaller growth fraction), a longer cycle time could be derived in slowly growing as compared to rapidly growing tumors. A direct relationship of the Ki-67 LI to a clinical time parameter (the TR) was obtained, and a predictive significance of individual LIs in glioblastomas could be defined.

Indexed as

Biomarkers, TumorAdultBrain NeoplasmsCell DivisionFemaleGlioblastomaHumansKi-67 AntigenMaleMiddle AgedNeoplasm Recurrence, LocalPredictive Value of TestsStaining and LabelingTime FactorsBiomarkers, TumorKi-67 Antigen

Identifiers

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.