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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg 1, 2, 3 Martin Vallières 4 Mahmoud A. Abdalah 5 Hugo J.W. Aerts 6, 7 Vincent Andrearczyk 8 Aditya Apte 9 Saeed Ashrafinia 10 Spyridon Bakas 11 Roelof J. Beukinga 12 Ronald Boellaard 12 Marta Bogowicz 13 Luca Boldrini 14 Irene Buvat 15 Gary J. R. Cook 16 Christos Davatzikos 11 Adrien Depeursinge 8, 17 Marie-Charlotte Desseroit 18 Nicola Dinapoli 14 Cuong Viet Dinh 19 Sebastian Echegaray 20 Issam El Naqa 4, 21 Andriy Fedorov 6 Roberto Gatta 14 Robert J Gillies 5 Vicky Goh 16 Michael Götz 3 Matthias Guckenberger 13 Sung Min Ha 11 Mathieu Hatt 18 Fabian Isensee 3 Philippe Lambin 22 Stefan Leger 1, 2, 3 Ralph T.H. Leijenaar 22 Jacopo Lenkowicz 14 Fiona Lippert 23 Are Losnegård 24 Klaus Maier-Hein 3 Olivier Morin 25 Henning Muller 8, 26 Sandy Napel 20 Christophe Nioche 15 Fanny Orlhac 15 Sarthak Pati 11 Elisabeth A.G. Pfaehler 12 Arman Rahmim 10, 27 Arvind U.K. Rao 21 Jonas Scherer 3 Muhammad Musib Siddique 16 Nanna M. Sijtsema 12 Jairo Socarras Fernandez 23 Emiliano Spezi 28, 29 Roel J.H.M. Steenbakkers 12 Stephanie Tanadini-Lang 13 Daniela Thorwarth 23 Esther G.C Troost 1, 2, 3 Taman Upadhaya 18, 30 Vincenzo Valentini 14 Lisanne V. van Dijk 12 Joost van Griethuysen 19, 22, 7, 6 Floris H.P. van Velden 31 Philip Whybra 28 Christian Richter 1, 2, 3 Steffen Löck 1, 2, 3
Abstract : Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.
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Submitted on : Monday, December 28, 2020 - 5:18:42 PM
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Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah, Hugo J.W. Aerts, Vincent Andrearczyk, et al.. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology, Radiological Society of North America, 2020, 295 (2), pp.328-338. ⟨10.1148/radiol.2020191145⟩. ⟨inserm-02954737⟩

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