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Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STA...

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Autores principales: Zhuo, Yaoyao, Feng, Mingxiang, Yang, Shuyi, Zhou, Lingxiao, Ge, Di, Lu, Shaohua, Liu, Lei, Shan, Fei, Zhang, Zhiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334418/
https://www.ncbi.nlm.nih.gov/pubmed/32622312
http://dx.doi.org/10.1016/j.tranon.2020.100820
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author Zhuo, Yaoyao
Feng, Mingxiang
Yang, Shuyi
Zhou, Lingxiao
Ge, Di
Lu, Shaohua
Liu, Lei
Shan, Fei
Zhang, Zhiyong
author_facet Zhuo, Yaoyao
Feng, Mingxiang
Yang, Shuyi
Zhou, Lingxiao
Ge, Di
Lu, Shaohua
Liu, Lei
Shan, Fei
Zhang, Zhiyong
author_sort Zhuo, Yaoyao
collection PubMed
description To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.
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spelling pubmed-73344182020-07-14 Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma Zhuo, Yaoyao Feng, Mingxiang Yang, Shuyi Zhou, Lingxiao Ge, Di Lu, Shaohua Liu, Lei Shan, Fei Zhang, Zhiyong Transl Oncol Original article To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma. Neoplasia Press 2020-07-01 /pmc/articles/PMC7334418/ /pubmed/32622312 http://dx.doi.org/10.1016/j.tranon.2020.100820 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original article
Zhuo, Yaoyao
Feng, Mingxiang
Yang, Shuyi
Zhou, Lingxiao
Ge, Di
Lu, Shaohua
Liu, Lei
Shan, Fei
Zhang, Zhiyong
Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title_full Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title_fullStr Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title_full_unstemmed Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title_short Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
title_sort radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma
topic Original article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334418/
https://www.ncbi.nlm.nih.gov/pubmed/32622312
http://dx.doi.org/10.1016/j.tranon.2020.100820
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