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Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules
Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient's prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on hig...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592239/ https://www.ncbi.nlm.nih.gov/pubmed/36299411 http://dx.doi.org/10.1155/2022/2671772 |
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author | Chen, Xinyue Yao, Benbo Li, Juan Liang, Chunxiao Qi, Rui Yu, Jianqun |
author_facet | Chen, Xinyue Yao, Benbo Li, Juan Liang, Chunxiao Qi, Rui Yu, Jianqun |
author_sort | Chen, Xinyue |
collection | PubMed |
description | Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient's prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on high-resolution computed tomography (HRCT). Between January 2014 and July 2019, 276 pulmonary nodules manifesting as GGNs on preoperative HRCTs, whose histological results were available, were collected. The nodules were randomly classified into training (n = 221) and independent testing (n = 55) cohorts. Three logistic models using features derived from HRCT were fit in the training cohort and validated in both aforementioned cohorts for invasive adenocarcinoma and preinvasive-minimally invasive adenocarcinoma (MIA) differentiation. The model with the best performance was presented as a nomogram and was validated using a calibration curve before performing a decision curve analysis. The benefit of using the proposed model was also shown by groups of management strategies recommended by The Fleischner Society. The combined model showed the best differentiation performance (area under the curve (AUC), training set = 0.89, and testing set = 0.92). The quantitative texture model showed better performance (AUC, training set = 0.87, and testing set = 0.91) than the semantic model (AUC, training set = 0.83, and testing set = 0.79). Of the 94 type 2 nodules that were IACs, 66 were identified by this model. Models using features derived from imaging are effective for differentiating between preinvasive-MIA and IACs among lung adenocarcinomas appearing as GGNs on CT images. |
format | Online Article Text |
id | pubmed-9592239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95922392022-10-25 Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules Chen, Xinyue Yao, Benbo Li, Juan Liang, Chunxiao Qi, Rui Yu, Jianqun Can Respir J Research Article Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient's prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on high-resolution computed tomography (HRCT). Between January 2014 and July 2019, 276 pulmonary nodules manifesting as GGNs on preoperative HRCTs, whose histological results were available, were collected. The nodules were randomly classified into training (n = 221) and independent testing (n = 55) cohorts. Three logistic models using features derived from HRCT were fit in the training cohort and validated in both aforementioned cohorts for invasive adenocarcinoma and preinvasive-minimally invasive adenocarcinoma (MIA) differentiation. The model with the best performance was presented as a nomogram and was validated using a calibration curve before performing a decision curve analysis. The benefit of using the proposed model was also shown by groups of management strategies recommended by The Fleischner Society. The combined model showed the best differentiation performance (area under the curve (AUC), training set = 0.89, and testing set = 0.92). The quantitative texture model showed better performance (AUC, training set = 0.87, and testing set = 0.91) than the semantic model (AUC, training set = 0.83, and testing set = 0.79). Of the 94 type 2 nodules that were IACs, 66 were identified by this model. Models using features derived from imaging are effective for differentiating between preinvasive-MIA and IACs among lung adenocarcinomas appearing as GGNs on CT images. Hindawi 2022-10-17 /pmc/articles/PMC9592239/ /pubmed/36299411 http://dx.doi.org/10.1155/2022/2671772 Text en Copyright © 2022 Xinyue Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Chen, Xinyue Yao, Benbo Li, Juan Liang, Chunxiao Qi, Rui Yu, Jianqun Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title | Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title_full | Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title_fullStr | Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title_full_unstemmed | Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title_short | Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules |
title_sort | feasibility of using high-resolution computed tomography features for invasiveness differentiation of malignant nodules manifesting as ground-glass nodules |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592239/ https://www.ncbi.nlm.nih.gov/pubmed/36299411 http://dx.doi.org/10.1155/2022/2671772 |
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