Evidence map›Paper›PMID 32997175›Full record

ArticleEuropean radiology2021

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?

Justin Solomon, Lukas Ebner, Andreas Christe, Alan Peters, Jaro Munz, Laura Löbelenz, Jeremias Klaus, Taylor Richards, Ehsan Samei, Justus E Roos

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In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

6 citing papers in PubMed, 9 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

10 authors at 4 institutions in 2 countries.

Justin SolomonCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA. justin.solomon@duke.edu.ORCID http://orcid.org/0000-0002-5884-2476
Lukas EbnerDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Andreas ChristeDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Alan PetersDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Jaro MunzDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Laura LöbelenzDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Jeremias KlausDepartment of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Taylor RichardsCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
Ehsan SameiCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
Justus E RoosInstitute of Radiology and Nuclear Medicine, Luzerner Kantonsspital, Lucerne, Canton of Lucerne, Switzerland.
University of Bern · CHDuke Medical Center · USDuke University · USLuzerner Kantonsspital · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe purpose of this study was to determine how well radiologists could visually detect a change in lung nodule size on the basis of visual image perception alone. SUBJECTS AND

methodsUnder IRB approval, 109 standard chest CT image series were anonymized and exported from PACS. Nine hundred forty virtual lung nodule pairs (six baseline diameters, six relative volume differences, two nodule types-solid and ground glass-and 14 repeats) were digitally inserted into the chest CT image series (same location, different sizes between the pair). These digitally altered CT image pairs were shown to nine radiologists who were tasked to visually determine which image contained the larger nodule using a two-alternative forced-choice perception experimental design. These data were statistically analyzed using a generalized linear mixed effects model to determine how accurately the radiologists were able to correctly identify the larger nodule.

resultsNominal baseline nodule diameter, relative volume difference, and nodule type were found to be statistically significant factors (p < 0.001) in influencing the radiologists' accuracy. For solid (ground-glass) nodules, the baseline diameter needed to be at least 6.3 mm (13.2 mm) to be able to visually detect a 25% change in volume with 95 ± 1.4% accuracy. Accuracy was lowest for the nodules with the smallest baseline diameters and smallest relative volume differences. Additionally, accuracy was lower for ground-glass nodules compared to solid nodules.

conclusionsFactors that impacted visual size assessment were baseline nodule diameter, relative volume difference, and solid versus non-solid nodule type, with larger and more solid lesions offering a more precise assessment of change. KEY POINTS: • For solid nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 6.3-mm baseline diameter. • For ground-glass nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 13.2-mm baseline diameter. • Accuracy in detecting a change in nodule size began to stabilize around 90-100% for nodules with larger baseline diameters (> 8 mm for solid nodules, > 12 mm for ground-glass nodules) and larger relative volume differences (>15% for solid nodules, > 25% for ground-glass nodules).

Indexed as

Lung NeoplasmsSolitary Pulmonary NoduleHumansLungRadiographic Image Interpretation, Computer-AssistedRadiologistsTomography, X-Ray ComputedLung neoplasmsMultidetector computed tomographyRadiologistsTask performance and analysis, Visual perception

Identifiers

PMID32997175
OpenAlexW3090204728

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.