Evidence map›Paper›PMID 35919083›Full record

ArticleIranian journal of medical sciences2022

Evaluation of Ultra-Low-Dose Chest Computed Tomography Images in Detecting Lung Lesions Related to COVID-19: A Prospective Study.

Fariba Zarei, Reza Jalli, Sabyasachi Chatterjee, Rezvan Ravanfar Haghighi, Pooya Iranpour, Vani Vardhan Chatterjee, Sedigheh Emadi

Open access · greenAbstract read
In one paragraph

Article in Iranian journal of medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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 3 institutions in 2 countries.

Fariba ZareiMedical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Reza JalliMedical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Sabyasachi ChatterjeeOngil, 79 D3, Sivaya Nagar, Reddiyur Alagapuram, Salem, India.
Rezvan Ravanfar HaghighiMedical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Pooya IranpourMedical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Vani Vardhan ChatterjeeDepartment of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India.
Sedigheh EmadiMedical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Shiraz University of Medical Sciences · IRAcharya Nagarjuna University · INIndian Institute of Science Bangalore · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The present study aimed to evaluate the effectiveness of ultra-low-dose (ULD) chest computed tomography (CT) in comparison with the routine dose (RD) CT images in detecting lung lesions related to COVID-19. Methods: A prospective study was conducted during April-September 2020 at Shahid Faghihi Hospital affiliated with Shiraz University of Medical Sciences, Shiraz, Iran. In total, 273 volunteers with suspected COVID-19 participated in the study and successively underwent RD-CT and ULD-CT chest scans. Two expert radiologists qualitatively evaluated the images. Dose assessment was performed by determining volume CT dose index, dose length product, and size-specific dose estimate. Data analysis was performed using a ranking test and kappa coefficient (κ). P<0.05 was considered statistically significant. Results: Lung lesions could be detected with both RD-CT and ULD-CT images in patients with suspected or confirmed COVID-19 (κ=1.0, P=0.016). The estimated effective dose for the RD-CT protocol was 22-fold higher than in the ULD-CT protocol. In the case of the ULD-CT protocol, sensitivity, specificity, accuracy, and positive predictive value for the detection of consolidation were 60%, 83%, 80%, and 20%, respectively. Comparably, in the case of RD-CT, these percentages for the detection of ground-glass opacity (GGO) were 62%, 66%, 66%, and 18%, respectively. Assuming the result of real-time polymerase chain reaction as true-positive, analysis of the receiver-operating characteristic curve for GGO detected using the ULD-CT protocol showed a maximum area under the curve of 0.78. Conclusion: ULD-CT, with 94% dose reduction, can be an alternative to RD-CT to detect lung lesions for COVID-19 diagnosis and follow-up.An earlier preliminary report of a similar work with a lower sample size was submitted to the arXive as a preprint. The preprint is cited as: https://arxiv.org/abs/2005.03347.

Indexed as

COVID-19COVID-19 TestingHumansLungProspective StudiesRadiation DosageTomography, X-Ray ComputedComputed tomographyCOVID-19Radiation protection

Identifiers

PMID35919083
PMCPMC9339117
OpenAlexW4289785788

What OpenQuestion holds

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LicenceCC BY-ND
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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.