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Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study

For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strat...

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Autores principales: Keller, Harald, Shek, Tina, Driscoll, Brandon, Xu, Yiwen, Nghiem, Brian, Nehmeh, Sadek, Grkovski, Milan, Schmidtlein, Charles Ross, Budzevich, Mikalai, Balagurunathan, Yoganand, Sunderland, John J., Beichel, Reinhard R., Uribe, Carlos, Lee, Ting-Yim, Li, Fiona, Jaffray, David A., Yeung, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025788/
https://www.ncbi.nlm.nih.gov/pubmed/35448725
http://dx.doi.org/10.3390/tomography8020091
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author Keller, Harald
Shek, Tina
Driscoll, Brandon
Xu, Yiwen
Nghiem, Brian
Nehmeh, Sadek
Grkovski, Milan
Schmidtlein, Charles Ross
Budzevich, Mikalai
Balagurunathan, Yoganand
Sunderland, John J.
Beichel, Reinhard R.
Uribe, Carlos
Lee, Ting-Yim
Li, Fiona
Jaffray, David A.
Yeung, Ivan
author_facet Keller, Harald
Shek, Tina
Driscoll, Brandon
Xu, Yiwen
Nghiem, Brian
Nehmeh, Sadek
Grkovski, Milan
Schmidtlein, Charles Ross
Budzevich, Mikalai
Balagurunathan, Yoganand
Sunderland, John J.
Beichel, Reinhard R.
Uribe, Carlos
Lee, Ting-Yim
Li, Fiona
Jaffray, David A.
Yeung, Ivan
author_sort Keller, Harald
collection PubMed
description For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.
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spelling pubmed-90257882022-04-23 Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study Keller, Harald Shek, Tina Driscoll, Brandon Xu, Yiwen Nghiem, Brian Nehmeh, Sadek Grkovski, Milan Schmidtlein, Charles Ross Budzevich, Mikalai Balagurunathan, Yoganand Sunderland, John J. Beichel, Reinhard R. Uribe, Carlos Lee, Ting-Yim Li, Fiona Jaffray, David A. Yeung, Ivan Tomography Article For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models. MDPI 2022-04-13 /pmc/articles/PMC9025788/ /pubmed/35448725 http://dx.doi.org/10.3390/tomography8020091 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Keller, Harald
Shek, Tina
Driscoll, Brandon
Xu, Yiwen
Nghiem, Brian
Nehmeh, Sadek
Grkovski, Milan
Schmidtlein, Charles Ross
Budzevich, Mikalai
Balagurunathan, Yoganand
Sunderland, John J.
Beichel, Reinhard R.
Uribe, Carlos
Lee, Ting-Yim
Li, Fiona
Jaffray, David A.
Yeung, Ivan
Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title_full Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title_fullStr Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title_full_unstemmed Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title_short Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
title_sort noise-based image harmonization significantly increases repeatability and reproducibility of radiomics features in pet images: a phantom study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025788/
https://www.ncbi.nlm.nih.gov/pubmed/35448725
http://dx.doi.org/10.3390/tomography8020091
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