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The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset

Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly...

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Autores principales: Ibrahim, Abdalla, Refaee, Turkey, Leijenaar, Ralph T. H., Primakov, Sergey, Hustinx, Roland, Mottaghy, Felix M., Woodruff, Henry C., Maidment, Andrew D. A., Lambin, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104396/
https://www.ncbi.nlm.nih.gov/pubmed/33961646
http://dx.doi.org/10.1371/journal.pone.0251147
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author Ibrahim, Abdalla
Refaee, Turkey
Leijenaar, Ralph T. H.
Primakov, Sergey
Hustinx, Roland
Mottaghy, Felix M.
Woodruff, Henry C.
Maidment, Andrew D. A.
Lambin, Philippe
author_facet Ibrahim, Abdalla
Refaee, Turkey
Leijenaar, Ralph T. H.
Primakov, Sergey
Hustinx, Roland
Mottaghy, Felix M.
Woodruff, Henry C.
Maidment, Andrew D. A.
Lambin, Philippe
author_sort Ibrahim, Abdalla
collection PubMed
description Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those–the “ComBatable” RFs–which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.
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spelling pubmed-81043962021-05-18 The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset Ibrahim, Abdalla Refaee, Turkey Leijenaar, Ralph T. H. Primakov, Sergey Hustinx, Roland Mottaghy, Felix M. Woodruff, Henry C. Maidment, Andrew D. A. Lambin, Philippe PLoS One Research Article Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those–the “ComBatable” RFs–which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets. Public Library of Science 2021-05-07 /pmc/articles/PMC8104396/ /pubmed/33961646 http://dx.doi.org/10.1371/journal.pone.0251147 Text en © 2021 Ibrahim 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ibrahim, Abdalla
Refaee, Turkey
Leijenaar, Ralph T. H.
Primakov, Sergey
Hustinx, Roland
Mottaghy, Felix M.
Woodruff, Henry C.
Maidment, Andrew D. A.
Lambin, Philippe
The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title_full The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title_fullStr The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title_full_unstemmed The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title_short The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
title_sort application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104396/
https://www.ncbi.nlm.nih.gov/pubmed/33961646
http://dx.doi.org/10.1371/journal.pone.0251147
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