Evidence map›Paper›PMID 24281269›Full record

ReviewEuropean radiology2014

Current knowledge on tumour induction by computed tomography should be carefully used.

Cristian Candela-Juan, Alegría Montoro, Enrique Ruiz-Martínez, Juan Ignacio Villaescusa, Luis Martí-Bonmatí

Abstract readReview
PubMed Publisher
In one paragraph

Review in European radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Cristian Candela-JuanRadioprotection Department, La Fe University and Polytechnic Hospital, Valencia, 46026, Spain, ccanjuan@gmail.com.
Alegría Montoro
Enrique Ruiz-Martínez
Juan Ignacio Villaescusa
Luis Martí-Bonmatí

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risks associated to ionising radiation from medical imaging techniques have focused the attention of the medical society and general population. This risk is aimed to determine the probability that a tumour is induced as a result of a computed tomography (CT) examination since it makes nowadays the biggest contribution to the collective dose. Several models of cancer induction have been reported in the literature, with diametrically different implications. This article reviews those models, focusing on the ones used by the scientific community to estimate CT detriments. Current estimates of the probability that a CT examination induces cancer are reported, highlighting its low magnitude (near the background level) and large sources of uncertainty. From this objective review, it is concluded that epidemiological data with more accurate dosimetric estimates are needed. Prediction of the number of tumours that will be induced in population exposed to ionising radiation should be avoided or, if given, it should be accompanied by a realistic evaluation of its uncertainty and of the advantages of CTs. Otherwise they may have a negative impact in both the medical community and the patients. Reducing doses even more is not justified if that compromises clinical image quality in a necessary investigation. Key Points • Predictions of radiation-induced cancer should be discussed alongside benefits of imaging. • Estimates of induced cancers have noticeable uncertainties that should always be highlighted. • There is controversy about the acceptance of the linear no-threshold model. • Estimated extra risks of cancer are close to the background level. • Patients should not be alarmed by potential cancer induction by CT examinations.

Indexed as

Models, StatisticalDose-Response Relationship, RadiationHumansNeoplasms, Radiation-InducedRadiation DosageRisk FactorsTomography, X-Ray Computed

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.