The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights

dc.contributor.authorWhybra, Philip
dc.contributor.authorZwanenburg, Alex
dc.contributor.authorAndrearczyk, Vincent
dc.contributor.authorSchaer, Roger
dc.contributor.authorApte, Aditya P.
dc.contributor.authorAyotte, Alexandre
dc.contributor.authorBaheti, Bhakti
dc.contributor.authorBakas, Spyridon
dc.contributor.authorBettinelli, Andrea
dc.contributor.authorBoellaard, Ronald
dc.contributor.authorBoldrini, Luca
dc.contributor.authorBuvat, Irène
dc.contributor.authorCook, Gary J. R.
dc.contributor.authorDietsche, Florian
dc.contributor.authorDinapoli, Nicola
dc.contributor.authorGabryś, Hubert S.
dc.contributor.authorGoh, Vicky
dc.contributor.authorGuckenberger, Matthias
dc.contributor.authorHatt, Mathieu
dc.contributor.authorHosseinzadeh, Mahdi
dc.contributor.authorIyer, Aditi
dc.contributor.authorLenkowicz, Jacopo
dc.contributor.authorLoutfi, Mahdi A. L.
dc.contributor.authorLöck, Steffen
dc.contributor.authorMarturano, Francesca
dc.contributor.authorMorin, Olivier
dc.contributor.authorNioche, Christophe
dc.contributor.authorOrlhac, Fanny
dc.contributor.authorPati, Sarthak
dc.contributor.authorRahmim, Arman
dc.contributor.authorRezaeijo, Seyed Masoud
dc.contributor.authorRookyard, Christopher G.
dc.contributor.authorSalmanpour, Mohammad R.
dc.contributor.authorSchindele, Andreas
dc.contributor.authorShiri, Isaac
dc.contributor.authorSpezi, Emiliano
dc.contributor.authorTanadini-Lang, Stephanie
dc.contributor.authorTixier, Florent
dc.contributor.authorUpadhaya, Taman
dc.contributor.authorValentini, Vincenzo
dc.contributor.authorvan Griethuysen, Joost J. M.
dc.contributor.authorYousefirizi, Fereshteh
dc.contributor.authorZaidi, Habib
dc.contributor.authorMüller, Henning
dc.contributor.authorVallières, Martin
dc.contributor.authorDepeursinge, Adrien
dc.contributor.departmentPathology and Laboratory Medicine, School of Medicine
dc.date.accessioned2025-03-24T15:33:25Z
dc.date.available2025-03-24T15:33:25Z
dc.date.issued2024
dc.description.abstractFilters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
dc.eprint.versionFinal published version
dc.identifier.citationWhybra P, Zwanenburg A, Andrearczyk V, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024;310(2):e231319. doi:10.1148/radiol.231319
dc.identifier.urihttps://hdl.handle.net/1805/46528
dc.language.isoen_US
dc.publisherRadiological Society of North America
dc.relation.isversionof10.1148/radiol.231319
dc.relation.journalRadiology
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectBiomarkers
dc.subjectMultimodal imaging
dc.subjectRadiomics
dc.titleThe Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
dc.typeArticle
ul.alternative.fulltexthttps://pmc.ncbi.nlm.nih.gov/articles/PMC10902595/
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