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Are radiomics features universally applicable to different organs?
BACKGROUND: Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common ra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028225/ https://www.ncbi.nlm.nih.gov/pubmed/33827699 http://dx.doi.org/10.1186/s40644-021-00400-y |
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author | Lee, Seung-Hak Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin |
author_facet | Lee, Seung-Hak Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin |
author_sort | Lee, Seung-Hak |
collection | PubMed |
description | BACKGROUND: Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments. METHODS: Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated. RESULTS: Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified. CONCLUSION: Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-021-00400-y. |
format | Online Article Text |
id | pubmed-8028225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80282252021-04-08 Are radiomics features universally applicable to different organs? Lee, Seung-Hak Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin Cancer Imaging Research Article BACKGROUND: Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments. METHODS: Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated. RESULTS: Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified. CONCLUSION: Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-021-00400-y. BioMed Central 2021-04-07 /pmc/articles/PMC8028225/ /pubmed/33827699 http://dx.doi.org/10.1186/s40644-021-00400-y Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Article Lee, Seung-Hak Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin Are radiomics features universally applicable to different organs? |
title | Are radiomics features universally applicable to different organs? |
title_full | Are radiomics features universally applicable to different organs? |
title_fullStr | Are radiomics features universally applicable to different organs? |
title_full_unstemmed | Are radiomics features universally applicable to different organs? |
title_short | Are radiomics features universally applicable to different organs? |
title_sort | are radiomics features universally applicable to different organs? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028225/ https://www.ncbi.nlm.nih.gov/pubmed/33827699 http://dx.doi.org/10.1186/s40644-021-00400-y |
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