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The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping

Zwanenburg, Alex, Vallières, Martin, Abdalah, Mahmoud A., Aerts, Hugo J.W.L., Andrearczyk, Vincent, Apte, Aditya, Ashrafinia, Saeed, Bakas, Spyridon, Beukinga, Roelof J., Boellaard, Ronald, Bogowicz, Marta, Boldrini, Luca, Buvat, Irène, Cook, Gary J.R., Davatzikos, Christos, Depeursinge, Adrien, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian, El Naqa, Issam, Fedorov, Andriy Y., Gatta, Roberto, Gillies, Robert J., Goh, Vicky, Guckenberger, Matthias, Götz, Michael, Ha, Sung Min, Hatt, Mathieu, Isensee, Fabian, Lambin, Philippe, Leger, Stefan, Leijenaar, Ralph T.H., Lenkowicz, Jacopo, Lippert, Fiona, Losnegård, Are, Maier-Hein, Klaus H., Morin, Olivier, Müller, Henning, Napel, Sandy, Nioche, Christophe, Orlhac, Fanny, Pati, Sarthak, Pfaehler, Elisabeth A.G., Rahmim, Arman, Rao, Arvind U.K., Scherer, Jonas, Siddique, Muhammad Musib, Sijtsema, Nanna M., Fernandez, Jairo Socarras, Spezi, Emiliano, Steenbakkers, Roel J.H.M, Tanadini-Lang, Stephanie, Thorwarth, Daniela, Troost, Esther G.C., Upadhaya, Taman, Valentini, Vincenzo, van Dijk, Lisanne V., van Griethuysen, Joost, van Velden, Floris H.P., Whybra, Philip, Richter, Christian and Löck, Steffen 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology 295 (2) , pp. 328-338. 10.1148/radiol.2020191145

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Radiological Society of North America (RSNA)
ISSN: 0033-8419
Date of First Compliant Deposit: 10 January 2020
Date of Acceptance: 6 January 2020
Last Modified: 25 Nov 2020 08:10
URI: http://orca.cf.ac.uk/id/eprint/128432

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