Literature DB >> 30409817

Variable radiological lung nodule evaluation leads to divergent management recommendations.

Arjun Nair1,2, Emily C Bartlett3,2, Simon L F Walsh3, Athol U Wells4, Neal Navani5, Georgia Hardavella6, Sanjeev Bhalla7, Lucio Calandriello8, Anand Devaraj9, Jin Mo Goo10, Jeffrey S Klein11, Heber MacMahon12, C M Schaefer-Prokop13, Joon-Beom Seo14, Nicola Sverzellati15, Sujal R Desai3,9,16.   

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.
Copyright ©ERS 2018.

Mesh:

Year:  2018        PMID: 30409817     DOI: 10.1183/13993003.01359-2018

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  2 in total

1.  DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules.

Authors:  Chengdi Wang; Jun Shao; Xiuyuan Xu; Le Yi; Gang Wang; Congchen Bai; Jixiang Guo; Yanqi He; Lei Zhang; Zhang Yi; Weimin Li
Journal:  Front Oncol       Date:  2022-05-11       Impact factor: 5.738

2.  Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Authors:  Pierre P Massion; Sanja Antic; Sarim Ather; Carlos Arteta; Jan Brabec; Heidi Chen; Jerome Declerck; David Dufek; William Hickes; Timor Kadir; Jonas Kunst; Bennett A Landman; Reginald F Munden; Petr Novotny; Heiko Peschl; Lyndsey C Pickup; Catarina Santos; Gary T Smith; Ambika Talwar; Fergus Gleeson
Journal:  Am J Respir Crit Care Med       Date:  2020-07-15       Impact factor: 21.405

  2 in total

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