Evidence map›Paper›PMID 24854083›Full record

ReviewNature reviews. Cancer2014

Imaging hallmarks of cancer in living mice.

Saskia I J Ellenbroek, Jacco van Rheenen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.

0numbers the graph read from it
0cells of the map it votes in
94citing 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

94 citing papers in PubMed.

  1. Review
  2. OptimizediScience · 2024
    Article
  3. Review
  4. Article
  5. Cancer Imaging by Intravital Microscopy: The Dorsal Window Chamber Model.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Zebrafish Models of Paediatric Brain Tumours.International journal of molecular sciences · 2022
    Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. The Cellular Organization of the Mammary Gland: Insights From Microscopy.Journal of mammary gland biology and neoplasia · 2021
    Review
  19. Label-Free Multimodal Multiphoton Intravital Imaging.Advances in experimental medicine and biology · 2021
    Article
  20. Benefits of Zebrafish Xenograft Models in Cancer Research.Frontiers in cell and developmental biology · 2021
    Review

34 more citing papers are in PubMed but not listed here.

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

2 authors.

Saskia I J EllenbroekCancer Genomics Netherlands-Hubrecht Institute-KNAW & University Medical Centre Utrecht, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.
Jacco van RheenenCancer Genomics Netherlands-Hubrecht Institute-KNAW & University Medical Centre Utrecht, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To comprehend the complexity of cancer, the biological characteristics acquired during the initiation and progression of tumours were classified as the 'hallmarks of cancer'. Intravital microscopy techniques have been developed to study individual cells that acquire these crucial traits, by visualizing tissues with cellular or subcellular resolution in living animals. In this Review, we highlight the latest intravital microscopy techniques that have been used in living animals (predominantly mice) to unravel fundamental and dynamic aspects of various hallmarks of cancer. In addition, we discuss the application of intravital microscopy techniques to cancer therapy, as well as limitations and future perspectives for these techniques.

Indexed as

Diagnostic ImagingAnimalsDisease ProgressionMiceNeoplasms

Identifiers

PMID24854083

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.