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The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation

In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image...

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Autores principales: Bettinelli, Andrea, Marturano, Francesca, Sarnelli, Anna, Bertoldo, Alessandra, Paiusco, Marta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9653377/
https://www.ncbi.nlm.nih.gov/pubmed/36371503
http://dx.doi.org/10.1038/s41597-022-01715-6
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author Bettinelli, Andrea
Marturano, Francesca
Sarnelli, Anna
Bertoldo, Alessandra
Paiusco, Marta
author_facet Bettinelli, Andrea
Marturano, Francesca
Sarnelli, Anna
Bertoldo, Alessandra
Paiusco, Marta
author_sort Bettinelli, Andrea
collection PubMed
description In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image pre-processing, standardise the formulation and nomenclature of 169 radiomic features and share two benchmark digital phantoms for software calibration. However, to better assess the concordance of radiomic tools, more heterogeneous phantoms are needed. We created two digital phantoms, called ImSURE phantoms, having isotropic and anisotropic voxel size, respectively, and 90 regions of interest (ROIs) each. To use these phantoms, we designed a systematic feature extraction workflow including 919 different feature values (obtained from the 169 IBSI-standardised features considering all possible combinations of feature aggregation and intensity discretisation methods). The ImSURE phantoms will allow to assess the concordance of radiomic software depending on interpolation, discretisation and aggregation methods, as well as on ROI volume and shape. Eventually, we provide the feature values extracted from these phantoms using five open-source IBSI-compliant software.
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spelling pubmed-96533772022-11-15 The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation Bettinelli, Andrea Marturano, Francesca Sarnelli, Anna Bertoldo, Alessandra Paiusco, Marta Sci Data Data Descriptor In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image pre-processing, standardise the formulation and nomenclature of 169 radiomic features and share two benchmark digital phantoms for software calibration. However, to better assess the concordance of radiomic tools, more heterogeneous phantoms are needed. We created two digital phantoms, called ImSURE phantoms, having isotropic and anisotropic voxel size, respectively, and 90 regions of interest (ROIs) each. To use these phantoms, we designed a systematic feature extraction workflow including 919 different feature values (obtained from the 169 IBSI-standardised features considering all possible combinations of feature aggregation and intensity discretisation methods). The ImSURE phantoms will allow to assess the concordance of radiomic software depending on interpolation, discretisation and aggregation methods, as well as on ROI volume and shape. Eventually, we provide the feature values extracted from these phantoms using five open-source IBSI-compliant software. Nature Publishing Group UK 2022-11-12 /pmc/articles/PMC9653377/ /pubmed/36371503 http://dx.doi.org/10.1038/s41597-022-01715-6 Text en © The Author(s) 2022 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 Data Descriptor
Bettinelli, Andrea
Marturano, Francesca
Sarnelli, Anna
Bertoldo, Alessandra
Paiusco, Marta
The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title_full The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title_fullStr The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title_full_unstemmed The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title_short The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation
title_sort imsure phantoms: a digital dataset for radiomic software benchmarking and investigation
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9653377/
https://www.ncbi.nlm.nih.gov/pubmed/36371503
http://dx.doi.org/10.1038/s41597-022-01715-6
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