Evidence map›Paper›PMID 29097345›Full record

ReviewDiagnostic and interventional radiology (Ankara, Turkey)

Tailoring protocols for chest CT applications: when and how?

Roberto Iezzi, Anna Rita Larici, Paola Franchi, Riccardo Marano, Nicola Magarelli, Alessandro Posa, Biagio Merlino, Riccardo Manfredi, Cesare Colosimo

Open access · greenAbstract readReview
In one paragraph

Review in Diagnostic and interventional radiology (Ankara, Turkey). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

9 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. High throughput image labeling on chest computed tomography by deep learning.Journal of medical imaging (Bellingham, Wash.) · 2020
    Article
  8. Article
  9. Imaging protocols for CT chest: A recommendation.The Indian journal of radiology & imaging
    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

9 authors at 1 institution in 1 country.

Roberto IezziInstitute of Radiology, Department of Radiological Sciences, Università Cattolica del Sacro Cuore, Rome, Italy. roberto.iezzi.md@gmail.com.
Anna Rita Larici
Paola Franchi
Riccardo Marano
Nicola Magarelli
Alessandro Posa
Biagio Merlino
Riccardo Manfredi
Cesare Colosimo
Università Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the medical era of early detection of diseases and tailored therapies, an accurate characterization and staging of the disease is pivotal for treatment planning. The widespread use of computed tomography (CT)-often with the use of contrast material (CM)-probably represents the most important advance in diagnostic radiology. The result is a marked increase in radiation exposure of the population for medical purposes, with its intrinsic carcinogenic potential, and CM affecting kidney function. The radiologists should aim to minimize patient's risk by reducing radiation exposure and CM amount, while maintaining the highest image quality. To achieve this goal, it is necessary to perform "patient-centric imaging". The purpose of this review is to provide radiologists with "tips and tricks" to control radiation dose at CT, summarizing technical artifices in order to reduce image noise and increase image contrast. Also chest CT tailored protocols are supplied, with particular attention to three most common thoracic CT protocols: aortic/cardiac CT angiography (CTA), pulmonary CTA, and routine chest CT.

Indexed as

Radiation DosageHumansRadiography, ThoracicThoracic DiseasesTomography, X-Ray Computed

Identifiers

PMID29097345
PMCPMC5669541
OpenAlexW2766102429

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.