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A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma
OBJECTIVE: The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a system...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458920/ https://www.ncbi.nlm.nih.gov/pubmed/36090345 http://dx.doi.org/10.3389/fsurg.2022.973523 |
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author | Yan, Bo Chang, Yuanyuan Jiang, Yifeng Liu, Yuan Yuan, Junyi Li, Rong |
author_facet | Yan, Bo Chang, Yuanyuan Jiang, Yifeng Liu, Yuan Yuan, Junyi Li, Rong |
author_sort | Yan, Bo |
collection | PubMed |
description | OBJECTIVE: The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD. METHODS: A total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients). RESULTS: GGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR = 1.490, 95% CI, 1.326–1.674), mean CT attenuation (OR = 1.007, 95% CI, 1.004–1.011) and heterogeneous uniformity of density (OR = 3.009, 95% CI, 1.485–6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95% CI, 0.889–0.949). CONCLUSIONS: ILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness. |
format | Online Article Text |
id | pubmed-9458920 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94589202022-09-10 A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma Yan, Bo Chang, Yuanyuan Jiang, Yifeng Liu, Yuan Yuan, Junyi Li, Rong Front Surg Surgery OBJECTIVE: The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD. METHODS: A total of 301 surgical LUAD patients with HRCT-confirmed GGN were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 171 invasive and 130 non-invasive LUAD patients). RESULTS: GGN features including shorter distance from pleura, larger diameter, area and mean CT attenuation, more heterogeneous uniformity of density, irregular shape, coarse margin, not defined nodule-lung interface, spiculation, pleural indentation, air bronchogram, vacuole sign, vessel changes, lobulation were observed in invasive LUAD patients compared with non-invasive LUAD patients. After adjustment by multivariate logistic regression model, GGN diameter (OR = 1.490, 95% CI, 1.326–1.674), mean CT attenuation (OR = 1.007, 95% CI, 1.004–1.011) and heterogeneous uniformity of density (OR = 3.009, 95% CI, 1.485–6.094) were independent risk factors for invasive LUAD. In addition, a predictive model integrating these three independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.919, 95% CI, 0.889–0.949). CONCLUSIONS: ILAG predictive model integrating GGN diameter, mean CT attenuation and heterogeneous uniformity of density via HRCT shows great potential for early estimation of LUAD invasiveness. Frontiers Media S.A. 2022-08-26 /pmc/articles/PMC9458920/ /pubmed/36090345 http://dx.doi.org/10.3389/fsurg.2022.973523 Text en © 2022 Yan, Chang, Jiang, Liu, Yuan and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Surgery Yan, Bo Chang, Yuanyuan Jiang, Yifeng Liu, Yuan Yuan, Junyi Li, Rong A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title | A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title_full | A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title_fullStr | A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title_full_unstemmed | A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title_short | A predictive model based on ground glass nodule features via high-resolution CT for identifying invasiveness of lung adenocarcinoma |
title_sort | predictive model based on ground glass nodule features via high-resolution ct for identifying invasiveness of lung adenocarcinoma |
topic | Surgery |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458920/ https://www.ncbi.nlm.nih.gov/pubmed/36090345 http://dx.doi.org/10.3389/fsurg.2022.973523 |
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