Evidence map›Paper›PMID 17044918›Full record

ArticleTheoretical biology & medical modelling2006

Cancer initiation and progression: an unsimplifiable complexity.

Fabio Grizzi, Antonio Di Ieva, Carlo Russo, Eldo E Frezza, Everardo Cobos, Pier Carlo Muzzio, Maurizio Chiriva-Internati

Open access · hybridAbstract readEditorial
In one paragraph

Article in Theoretical biology & medical modelling, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 27% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.

  1. Pooled it
  2. Review
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  5. Article
  6. Article
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  17. Thigmotropism of malignant melanoma cells.Dermatology research and practice · 2012
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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

7 authors at 4 institutions in 2 countries.

Fabio GrizziLaboratories of Quantitative Medicine, Istituto Clinico Humanitas IRCCS, Via Manzoni 56, 20089 Rozzano, Milan, Italy. fabio.grizzi@humanitas.it
Antonio Di Ieva
Carlo Russo
Eldo E Frezza
Everardo Cobos
Pier Carlo Muzzio
Maurizio Chiriva-Internati
IRCCS Humanitas Research Hospital · ITTexas Tech University Health Sciences Center · USTexas Tech University · USUniversity of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer remains one of the most complex diseases affecting humans and, despite the impressive advances that have been made in molecular and cell biology, how cancer cells progress through carcinogenesis and acquire their metastatic ability is still widely debated.

conclusionThere is no doubt that human carcinogenesis is a dynamic process that depends on a large number of variables and is regulated at multiple spatial and temporal scales. Viewing cancer as a system that is dynamically complex in time and space will, however, probably reveal more about its underlying behavioural characteristics. It is encouraging that mathematicians, biologists and clinicians continue to contribute together towards a common quantitative understanding of cancer complexity. This way of thinking may further help to clarify concepts, interpret new and old experimental data, indicate alternative experiments and categorize the acquired knowledge on the basis of the similarities and/or shared behaviours of very different tumours.

Indexed as

AlgorithmsCocarcinogenesisDisease ProgressionHumansModels, BiologicalNeoplasm MetastasisNeoplasms

Identifiers

PMID17044918
PMCPMC1621057
OpenAlexW2113605459

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.