Evidence map›Paper›PMID 26813670›Full record

ArticleEuropean radiology2016

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging.

Adrian Huber, Julia Landau, Lukas Ebner, Yanik Bütikofer, Lars Leidolt, Barbara Brela, Michelle May, Johannes Heverhagen, Andreas Christe

Erratum issuedOpen access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
9.1field-weighted citation impact, top 2% 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

32 citing papers in PubMed, 82 citations in OpenAlex.

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  8. The Journey for Lung Cancer Screening where we Stand Today.The open respiratory medicine journal · 2022
    Review
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  11. Comparison of clinical diagnostic value of spiral CT with different dose in patients with early-stage peripheral lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 3 countries.

Adrian HuberDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland. adrian.huber@insel.ch.
Julia LandauDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Lukas EbnerDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Yanik BütikoferDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Lars LeidoltDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Barbara BrelaDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Michelle MayDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Johannes HeverhagenDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
Andreas ChristeDepartment of Diagnostic, Interventional and Paediatric Radiology, University Hospital Inselspital Bern, CH-3010, Bern, Switzerland.
University Hospital of Bern · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the detection rate of pulmonary nodules in ultralow-dose CT acquisitions. MATERIALS AND

methodsIn this lung phantom study, 232 nodules (115 solid, 117 ground-glass) of different sizes were randomly distributed in a lung phantom in 60 different arrangements. Every arrangement was acquired once with standard radiation dose (100 kVp, 100 references mAs) and once with ultralow radiation dose (80 kVp, 6 mAs). Iterative reconstruction was used with optimized kernels: I30 for ultralow-dose, I70 for standard dose and I50 for CAD. Six radiologists examined the axial 1-mm stack for solid and ground-glass nodules. During a second and third step, three radiologists used maximum intensity projection (MIPs), finally checking with computer-assisted detection (CAD), while the others first used CAD, finally checking with the MIPs.

resultsThe detection rate was 95.5 % with standard dose (DLP 126 mGy*cm) and 93.3 % with ultralow-dose (DLP: 9 mGy*cm). The additional use of either MIP reconstructions or CAD software could compensate for this difference. A combination of both MIP reconstructions and CAD software resulted in a maximum detection rate of 97.5 % with ultralow-dose.

conclusionLung cancer screening with ultralow-dose CT using the same radiation dose as a conventional chest X-ray is feasible. KEY POINTS: • 93.3 % of all lung nodules were detected with ultralow-dose CT. • A sensitivity of 97.5 % is possible with additional image post-processing. • The radiation dose is comparable to a standard radiography in two planes. • Lung cancer screening with ultralow-dose CT is feasible.

Indexed as

Early Detection of CancerFeasibility StudiesHumansLung NeoplasmsMultiple Pulmonary NodulesObserver VariationPhantoms, ImagingRadiation DosageRadiation ExposureRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicRandom AllocationSensitivity and SpecificitySoftwareTomography, X-Ray ComputedComputed tomographyDiagnostic performance with low radiation dosageLung adenocarcinomaPulmonary nodule detectionUltralow-dose acquisition

Identifiers

PMID26813670
OpenAlexW2258116496

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.