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


Journal

Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192

Informations de publication

Date de publication:
12 11 2022
Historique:
received: 15 03 2022
accepted: 16 09 2022
entrez: 13 11 2022
pubmed: 14 11 2022
medline: 16 11 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36371503
doi: 10.1038/s41597-022-01715-6
pii: 10.1038/s41597-022-01715-6
pmc: PMC9653377
doi:

Types de publication

Dataset Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

695

Informations de copyright

© 2022. The Author(s).

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Auteurs

Andrea Bettinelli (A)

Medical Physics Department, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy. andrea.bettinelli@phd.unipd.it.
Department of Information Engineering, University of Padova, Padova, Italy. andrea.bettinelli@phd.unipd.it.

Francesca Marturano (F)

Medical Physics Department, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy.

Anna Sarnelli (A)

IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.

Alessandra Bertoldo (A)

Department of Information Engineering, University of Padova, Padova, Italy.
Padova Neuroscience Centre (PNC), University of Padova, Padova, Italy.

Marta Paiusco (M)

Medical Physics Department, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy.

Classifications MeSH