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Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis

BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. METHODS: A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography...

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Autores principales: Sun, Wenzheng, Jiang, Mingyan, Dang, Jun, Chang, Panchun, Yin, Fang-Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173915/
https://www.ncbi.nlm.nih.gov/pubmed/30290849
http://dx.doi.org/10.1186/s13014-018-1140-9
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author Sun, Wenzheng
Jiang, Mingyan
Dang, Jun
Chang, Panchun
Yin, Fang-Fang
author_facet Sun, Wenzheng
Jiang, Mingyan
Dang, Jun
Chang, Panchun
Yin, Fang-Fang
author_sort Sun, Wenzheng
collection PubMed
description BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. METHODS: A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests. RESULTS: The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74). CONCLUSIONS: The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-61739152018-10-15 Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis Sun, Wenzheng Jiang, Mingyan Dang, Jun Chang, Panchun Yin, Fang-Fang Radiat Oncol Research BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. METHODS: A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests. RESULTS: The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74). CONCLUSIONS: The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-05 /pmc/articles/PMC6173915/ /pubmed/30290849 http://dx.doi.org/10.1186/s13014-018-1140-9 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sun, Wenzheng
Jiang, Mingyan
Dang, Jun
Chang, Panchun
Yin, Fang-Fang
Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title_full Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title_fullStr Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title_full_unstemmed Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title_short Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
title_sort effect of machine learning methods on predicting nsclc overall survival time based on radiomics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173915/
https://www.ncbi.nlm.nih.gov/pubmed/30290849
http://dx.doi.org/10.1186/s13014-018-1140-9
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