Article Dans Une Revue Radiology Année : 2024

The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics

1 Cardiff University
2 HES-SO - Haute École spécialisée de Suisse occidentale = HES-SO University of Applied Sciences and Arts Western Switzerland = Fachhochschule Westschweiz [Schweiz]
3 MSK2C - Memorial Sloan Kettering Cancer Center [New York, NY, USA]
4 UdeS - Université de Sherbrooke
5 University of Pennsylvania
6 Veneto Institute of Oncology IOV-IRCCS [Padua, Italy]
7 Amsterdam UMC - Amsterdam University Medical Centers
8 Fondazione Policlinico Universitario Agostino Gemelli IRCCS
9 LITO - Laboratoire d'Imagerie Translationnelle en Oncologie
10 King‘s College London
11 Division of Gastroenterology, University Hospital Zurich, Zurich, Switzerland.
12 University hospital of Zurich [Zurich]
13 UZH - Universität Zürich [Zürich] = University of Zurich
14 LaTIM - Laboratoire de Traitement de l'Information Medicale
15 TECVICO Corp - Technological Virtual Collaboration , Vancouver, Canada
16 Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Institute of Microbiology, Rome, Italy.
17 TU Dresden - Technische Universität Dresden = Dresden University of Technology
18 IOV - Istituto Oncologico Veneto - IRCCS
19 UC San Francisco - University of California [San Francisco]
20 Indiana University School of Medicine
21 UBC - University of British Columbia [Canada]
22 AJUMS - Ahvaz Jundishapur University of Medical Sciences
23 UNIA - Universität Augsburg [Deutschland] = University of Augsburg [Germany] = Université d'Augsburg [Allemagne]
24 HUG - Hôpitaux universitaires de Genève = University Hospitals of Geneva
25 USZ - Universitätsspital Zürich
26 MRC Centre for Transplantation, Urology Department, Guy's and St Thomas NHS Foundation Trust & King's College London
27 Cedars-Sinai Medical Center
28 NKI - Netherlands Cancer Institute
29 Utrecht Brain Center [UMC]
30 BC Cancer Research Institute, Vancouver, Canada
31 Geneva University Hospitals and Geneva University
32 CSSS-IUGS - Institut Universitaire de Gériatrie de Sherbrooke
33 CHUV - Centre Hospitalier Universitaire Vaudois = Lausanne University Hospital [Lausanne]
Philip Whybra
  • Fonction : Auteur
Bhakti Baheti
Spyridon Bakas
Ronald Boellaard
Gary Cook
Hubert Gabryś
Vicky Goh
Mahdi A. L Loutfi
Francesca Marturano
Olivier Morin
Christopher Rookyard
Emiliano Spezi
Stephanie Tanadini-Lang
Taman Upadhaya

Résumé

Filters 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.
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Dates et versions

inserm-04783231 , version 1 (14-11-2024)

Identifiants

Citer

Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya Apte, et al.. The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics. Radiology, 2024, 310 (2), pp.e231319. ⟨10.1148/radiol.231319⟩. ⟨inserm-04783231⟩
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