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Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study

To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models ba...

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Autores principales: Zhang, Haochuan, Wang, Shixiong, Deng, Zhenkai, Li, Yangli, Yang, Yingying, Huang, He
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838201/
https://www.ncbi.nlm.nih.gov/pubmed/36643621
http://dx.doi.org/10.7717/peerj.14559
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author Zhang, Haochuan
Wang, Shixiong
Deng, Zhenkai
Li, Yangli
Yang, Yingying
Huang, He
author_facet Zhang, Haochuan
Wang, Shixiong
Deng, Zhenkai
Li, Yangli
Yang, Yingying
Huang, He
author_sort Zhang, Haochuan
collection PubMed
description To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661–0.824]) for the logistic regression, 0.828 (95% CI [0.76–0.896]) for the support vector machine, and 0.917 (95% CI [0.869–0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694–0.913]), 0.726 (95% CI [0.598–0.854]), and 0.874 (95% CI [0.776–0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.
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spelling pubmed-98382012023-01-14 Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study Zhang, Haochuan Wang, Shixiong Deng, Zhenkai Li, Yangli Yang, Yingying Huang, He PeerJ Bioinformatics To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661–0.824]) for the logistic regression, 0.828 (95% CI [0.76–0.896]) for the support vector machine, and 0.917 (95% CI [0.869–0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694–0.913]), 0.726 (95% CI [0.598–0.854]), and 0.874 (95% CI [0.776–0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93. PeerJ Inc. 2023-01-10 /pmc/articles/PMC9838201/ /pubmed/36643621 http://dx.doi.org/10.7717/peerj.14559 Text en ©2022 Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Zhang, Haochuan
Wang, Shixiong
Deng, Zhenkai
Li, Yangli
Yang, Yingying
Huang, He
Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title_full Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title_fullStr Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title_full_unstemmed Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title_short Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
title_sort computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838201/
https://www.ncbi.nlm.nih.gov/pubmed/36643621
http://dx.doi.org/10.7717/peerj.14559
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