Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Objectives. To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods. In this study, 36 patients with central pulmonary cancer and atelectasis between...

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Autores principales:	Rui Chai, Qi Wang, Pinle Qin, Jianchao Zeng, Jiwei Ren, Ruiping Zhang, Lin Chu, Xuting Zhang, Yun Guan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/7969af2199fd465cb88ab6c5a044f837
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id	oai:doaj.org-article:7969af2199fd465cb88ab6c5a044f837
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spelling	oai:doaj.org-article:7969af2199fd465cb88ab6c5a044f8372021-11-29T00:55:27ZDifferentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features2314-614110.1155/2021/5522452https://doaj.org/article/7969af2199fd465cb88ab6c5a044f8372021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5522452https://doaj.org/toc/2314-6141Objectives. To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods. In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results. Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions. Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.Rui ChaiQi WangPinle QinJianchao ZengJiwei RenRuiping ZhangLin ChuXuting ZhangYun GuanHindawi LimitedarticleMedicineRENBioMed Research International, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Rui Chai Qi Wang Pinle Qin Jianchao Zeng Jiwei Ren Ruiping Zhang Lin Chu Xuting Zhang Yun Guan Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
description	Objectives. To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods. In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results. Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions. Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.
format	article
author	Rui Chai Qi Wang Pinle Qin Jianchao Zeng Jiwei Ren Ruiping Zhang Lin Chu Xuting Zhang Yun Guan
author_facet	Rui Chai Qi Wang Pinle Qin Jianchao Zeng Jiwei Ren Ruiping Zhang Lin Chu Xuting Zhang Yun Guan
author_sort	Rui Chai
title	Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
title_short	Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
title_full	Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
title_fullStr	Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
title_full_unstemmed	Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features
title_sort	differentiating central lung tumors from atelectasis with contrast-enhanced ct-based radiomics features
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/7969af2199fd465cb88ab6c5a044f837
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_version_	1718407777219510272

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

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