ReviewEuropean radiology2014
Current knowledge on tumour induction by computed tomography should be carefully used.
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.
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.
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.
Who cites it
5 citing papers in PubMed.
- Evaluation of Effective Dose from CT Scans for Overweight and Obese Adult Patients Using the VirtualDose Software.Radiation protection dosimetry · 2017Article
- Validity of the size-specific dose estimate in adults undergoing coronary CT angiography: comparison with the volume CT dose index.The international journal of cardiovascular imaging · 2015Article
- Imaging the Parasinus Region with a Third-Generation Dual-Source CT and the Effect of Tin Filtration on Image Quality and Radiation Dose.AJNR. American journal of neuroradiology · 2015Article
- Evaluation of dose reduction and image quality in CT colonography: comparison of low-dose CT with iterative reconstruction and routine-dose CT with filtered back projection.European radiology · 2015Article
- The investigation of dose and image quality of chest computed tomography using different combinations of noise index and adaptive statistic iterative reconstruction level.The Indian journal of radiology & imagingArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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.