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Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT...

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Autores principales: Cho, Hwan-ho, Lee, Ho Yun, Kim, Eunjin, Lee, Geewon, Kim, Jonghoon, Kwon, Junmo, Park, Hyunjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590002/
https://www.ncbi.nlm.nih.gov/pubmed/34773070
http://dx.doi.org/10.1038/s42003-021-02814-7
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author Cho, Hwan-ho
Lee, Ho Yun
Kim, Eunjin
Lee, Geewon
Kim, Jonghoon
Kwon, Junmo
Park, Hyunjin
author_facet Cho, Hwan-ho
Lee, Ho Yun
Kim, Eunjin
Lee, Geewon
Kim, Jonghoon
Kwon, Junmo
Park, Hyunjin
author_sort Cho, Hwan-ho
collection PubMed
description Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.
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spelling pubmed-85900022021-11-15 Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans Cho, Hwan-ho Lee, Ho Yun Kim, Eunjin Lee, Geewon Kim, Jonghoon Kwon, Junmo Park, Hyunjin Commun Biol Article Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well. Nature Publishing Group UK 2021-11-12 /pmc/articles/PMC8590002/ /pubmed/34773070 http://dx.doi.org/10.1038/s42003-021-02814-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cho, Hwan-ho
Lee, Ho Yun
Kim, Eunjin
Lee, Geewon
Kim, Jonghoon
Kwon, Junmo
Park, Hyunjin
Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title_full Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title_fullStr Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title_full_unstemmed Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title_short Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
title_sort radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from ct scans
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590002/
https://www.ncbi.nlm.nih.gov/pubmed/34773070
http://dx.doi.org/10.1038/s42003-021-02814-7
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