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Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?

BACKGROUND: The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodul...

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Autores principales: Wang, Xiang, Chen, Kaili, Wang, Wei, Li, Qingchu, Liu, Kai, Li, Qianyun, Cui, Xing, Tu, Wenting, Sun, Hongbiao, Xu, Shaochun, Zhang, Rongguo, Xiao, Yi, Fan, Li, Liu, Shiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024795/
https://www.ncbi.nlm.nih.gov/pubmed/33841926
http://dx.doi.org/10.21037/jtd-20-2981
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author Wang, Xiang
Chen, Kaili
Wang, Wei
Li, Qingchu
Liu, Kai
Li, Qianyun
Cui, Xing
Tu, Wenting
Sun, Hongbiao
Xu, Shaochun
Zhang, Rongguo
Xiao, Yi
Fan, Li
Liu, Shiyuan
author_facet Wang, Xiang
Chen, Kaili
Wang, Wei
Li, Qingchu
Liu, Kai
Li, Qianyun
Cui, Xing
Tu, Wenting
Sun, Hongbiao
Xu, Shaochun
Zhang, Rongguo
Xiao, Yi
Fan, Li
Liu, Shiyuan
author_sort Wang, Xiang
collection PubMed
description BACKGROUND: The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs). METHODS: Preoperative thin-section chest CT images were reviewed retrospectively in 622 patients with a total of 687 pulmonary GGNs. GGNs are classified according to clinical management strategies as invasive lesions (IAC) and non-invasive lesions (AAH, AIS and MIA). The two volumes of interest (VOIs) identified on CT were the gross tumor volume (GTV) and the gross volume of tumor incorporating peritumoral region (GPTV). Three dimensional (3D) DenseNet was used to model and predict GGN invasiveness, and five-fold cross validation was performed. We used GTV and GPTV as inputs for the comparison model. Prediction performance was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: The GTV-based model was able to successfully predict GGN invasiveness, with an AUC of 0.921 (95% CI, 0.896–0.937). Using GPTV, the AUC of the model increased to 0.955 (95% CI, 0.939–0.971). CONCLUSIONS: The deep learning method performed well in predicting GGN invasiveness. The predictive ability of the GPTV-based model was more effective than that of the GTV-based model.
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spelling pubmed-80247952021-04-08 Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules? Wang, Xiang Chen, Kaili Wang, Wei Li, Qingchu Liu, Kai Li, Qianyun Cui, Xing Tu, Wenting Sun, Hongbiao Xu, Shaochun Zhang, Rongguo Xiao, Yi Fan, Li Liu, Shiyuan J Thorac Dis Original Article BACKGROUND: The peri-tumor microenvironment plays an important role in the occurrence, growth and metastasis of cancer. The aim of this study is to explore the value and application of a CT image-based deep learning model of tumors and peri-tumors in predicting the invasiveness of ground-glass nodules (GGNs). METHODS: Preoperative thin-section chest CT images were reviewed retrospectively in 622 patients with a total of 687 pulmonary GGNs. GGNs are classified according to clinical management strategies as invasive lesions (IAC) and non-invasive lesions (AAH, AIS and MIA). The two volumes of interest (VOIs) identified on CT were the gross tumor volume (GTV) and the gross volume of tumor incorporating peritumoral region (GPTV). Three dimensional (3D) DenseNet was used to model and predict GGN invasiveness, and five-fold cross validation was performed. We used GTV and GPTV as inputs for the comparison model. Prediction performance was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: The GTV-based model was able to successfully predict GGN invasiveness, with an AUC of 0.921 (95% CI, 0.896–0.937). Using GPTV, the AUC of the model increased to 0.955 (95% CI, 0.939–0.971). CONCLUSIONS: The deep learning method performed well in predicting GGN invasiveness. The predictive ability of the GPTV-based model was more effective than that of the GTV-based model. AME Publishing Company 2021-03 /pmc/articles/PMC8024795/ /pubmed/33841926 http://dx.doi.org/10.21037/jtd-20-2981 Text en 2021 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Wang, Xiang
Chen, Kaili
Wang, Wei
Li, Qingchu
Liu, Kai
Li, Qianyun
Cui, Xing
Tu, Wenting
Sun, Hongbiao
Xu, Shaochun
Zhang, Rongguo
Xiao, Yi
Fan, Li
Liu, Shiyuan
Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title_full Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title_fullStr Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title_full_unstemmed Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title_short Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
title_sort can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024795/
https://www.ncbi.nlm.nih.gov/pubmed/33841926
http://dx.doi.org/10.21037/jtd-20-2981
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