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Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines

OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Dani...

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Autores principales: van Riel, Sarah J., Ciompi, Francesco, Jacobs, Colin, Winkler Wille, Mathilde M., Scholten, Ernst Th., Naqibullah, Matiullah, Lam, Stephen, Prokop, Mathias, Schaefer-Prokop, Cornelia, van Ginneken, Bram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579178/
https://www.ncbi.nlm.nih.gov/pubmed/28293773
http://dx.doi.org/10.1007/s00330-017-4767-2
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author van Riel, Sarah J.
Ciompi, Francesco
Jacobs, Colin
Winkler Wille, Mathilde M.
Scholten, Ernst Th.
Naqibullah, Matiullah
Lam, Stephen
Prokop, Mathias
Schaefer-Prokop, Cornelia
van Ginneken, Bram
author_facet van Riel, Sarah J.
Ciompi, Francesco
Jacobs, Colin
Winkler Wille, Mathilde M.
Scholten, Ernst Th.
Naqibullah, Matiullah
Lam, Stephen
Prokop, Mathias
Schaefer-Prokop, Cornelia
van Ginneken, Bram
author_sort van Riel, Sarah J.
collection PubMed
description OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: D(longest-C) (PanCan), D(meanAxial) (NCCN), both obtained from axial sections, and D(mean3D) (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination. RESULTS: PanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying D(longest-C), D(mean3D) and D(meanAxial), PanCan remained superior to Lung-RADS (p < 0.001 – p = 0.001) and NCCN (p < 0.001 – p = 0.016). Diameter definition significantly influenced NCCN’s performance with D(longest-C) being the worst (D(longest-C) vs. D(mean3D), p = 0.005; D(longest-C) vs. D(meanAxial), p = 0.016). CONCLUSIONS: Without follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: • PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. • Nodule size definition had no significant impact on Lung-RADS and PanCan model. • 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. • Longest diameter achieved lowest performance for all models. • Mean diameter performed equivalently when derived from axial sections and from volumetry.
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spelling pubmed-55791782017-09-18 Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines van Riel, Sarah J. Ciompi, Francesco Jacobs, Colin Winkler Wille, Mathilde M. Scholten, Ernst Th. Naqibullah, Matiullah Lam, Stephen Prokop, Mathias Schaefer-Prokop, Cornelia van Ginneken, Bram Eur Radiol Chest OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: D(longest-C) (PanCan), D(meanAxial) (NCCN), both obtained from axial sections, and D(mean3D) (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination. RESULTS: PanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying D(longest-C), D(mean3D) and D(meanAxial), PanCan remained superior to Lung-RADS (p < 0.001 – p = 0.001) and NCCN (p < 0.001 – p = 0.016). Diameter definition significantly influenced NCCN’s performance with D(longest-C) being the worst (D(longest-C) vs. D(mean3D), p = 0.005; D(longest-C) vs. D(meanAxial), p = 0.016). CONCLUSIONS: Without follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: • PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. • Nodule size definition had no significant impact on Lung-RADS and PanCan model. • 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. • Longest diameter achieved lowest performance for all models. • Mean diameter performed equivalently when derived from axial sections and from volumetry. Springer Berlin Heidelberg 2017-03-14 2017 /pmc/articles/PMC5579178/ /pubmed/28293773 http://dx.doi.org/10.1007/s00330-017-4767-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Chest
van Riel, Sarah J.
Ciompi, Francesco
Jacobs, Colin
Winkler Wille, Mathilde M.
Scholten, Ernst Th.
Naqibullah, Matiullah
Lam, Stephen
Prokop, Mathias
Schaefer-Prokop, Cornelia
van Ginneken, Bram
Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title_full Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title_fullStr Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title_full_unstemmed Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title_short Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines
title_sort malignancy risk estimation of screen-detected nodules at baseline ct: comparison of the pancan model, lung-rads and nccn guidelines
topic Chest
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579178/
https://www.ncbi.nlm.nih.gov/pubmed/28293773
http://dx.doi.org/10.1007/s00330-017-4767-2
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