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. 2018 Dec 20;52(6):1801359.
doi: 10.1183/13993003.01359-2018. Print 2018 Dec.

Variable radiological lung nodule evaluation leads to divergent management recommendations

Collaborators, Affiliations
Free article

Variable radiological lung nodule evaluation leads to divergent management recommendations

Arjun Nair et al. Eur Respir J. .
Free article

Abstract

Radiological evaluation of incidentally detected lung nodules on computed tomography (CT) influences management. We assessed international radiological variation in 1) pulmonary nodule characterisation; 2) hypothetical guideline-derived management; and 3) radiologists' management recommendations.107 radiologists from 25 countries evaluated 69 CT-detected nodules, recording: 1) first-choice composition (solid, part-solid or ground-glass, with percentage confidence); 2) morphological features; 3) dimensions; 4) recommended management; and 5) decision-influencing factors. We modelled hypothetical management decisions on the 2005 and updated 2017 Fleischner Society, and both liberal and parsimonious interpretations of the British Thoracic Society 2015 guidelines.Overall agreement for first-choice nodule composition was good (Fleiss' κ=0.65), but poorest for part-solid nodules (weighted κ 0.62, interquartile range 0.50-0.71). Morphological variables, including spiculation (κ=0.35), showed poor-to-moderate agreement (κ=0.23-0.53). Variation in diameter was greatest at key thresholds (5 mm and 6 mm). Agreement for radiologists' recommendations was poor (κ=0.30); 21% disagreed with the majority. Although agreement within the four guideline-modelled management strategies was good (κ=0.63-0.73), 5-10% of radiologists would disagree with majority decisions if they applied guidelines strictly.Agreement was lowest for part-solid nodules, while significant measurement variation exists at important size thresholds. These variations resulted in generally good agreement for guideline-modelled management, but poor agreement for radiologists' actual recommendations.

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Conflict of interest statement

Conflict of interest: A. Nair: this work was partly undertaken at University College London Hospitals (UCLH)/University College London (UCL), by A. Nair, who received a proportion of funding from the Department of Health National Institute for Health Research (NIHR) Biomedical Research Centres funding scheme. A. Nair is a member of the advisory board for Aidence Artificial Intelligence, but has not received any fees from this entity to date, and the work with this entity is not in any way related to the current submission. Conflict of interest: E.C. Bartlett has nothing to disclose. Conflict of interest: S.L.F. Walsh has nothing to disclose. Conflict of interest: A.U. Wells has nothing to disclose. Conflict of interest: N. Navani has nothing to disclose. Conflict of interest: G. Hardavella has nothing to disclose. Conflict of interest: S. Bhalla has nothing to disclose. Conflict of interest: L. Calandriello has nothing to disclose. Conflict of interest: A. Devaraj has nothing to disclose. Conflict of interest: J.M. Goo reports grants from Lunit Inc., outside the submitted work. Conflict of interest: J.S. Klein has nothing to disclose. Conflict of interest: H. MacMahon reports personal fees for advisory board work from Riverain Technologies, personal fees for consultancy from GE Healthcare, grants from Philips Healthcare, honoraria from Konica-Minolta, stock options for research collaborations from Hologic, and payments for patents and licences from University of Chicago, outside the submitted work. Conflict of interest: C.M. Schaefer-Prokop has nothing to disclose. Conflict of interest: J-B. Seo has nothing to disclose. Conflict of interest: N. Sverzellati reports personal fees from Roche and Boehringer Ingelheim, outside the submitted work. Conflict of interest: S.R. Desai has nothing to disclose.

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