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The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping

  • Alex Zwanenburg
  • , Martin Vallières
  • , Mahmoud A. Abdalah
  • , Hugo J.W.L. Aerts
  • , Vincent Andrearczyk
  • , Aditya Apte
  • , Saeed Ashrafinia
  • , Spyridon Bakas
  • , Roelof J. Beukinga
  • , Ronald Boellaard
  • , Marta Bogowicz
  • , Luca Boldrini
  • , Irène Buvat
  • , Gary J.R. Cook
  • , Christos Davatzikos
  • , Adrien Depeursinge
  • , Marie-Charlotte Desseroit
  • , Nicola Dinapoli
  • , Cuong Viet Dinh
  • , Sebastian Echegaray
  • Issam El Naqa, Andriy Y. Fedorov, Roberto Gatta, Robert J. Gillies, Vicky Goh, Michael Götz, Matthias Guckenberger, Sung Min Ha, Mathieu Hatt, Fabian Isensee, Philippe Lambin, Stefan Leger, Ralph T.H. Leijenaar, Jacopo Lenkowicz, Fiona Lippert, Are Losnegård, Klaus H. Maier-Hein, Olivier Morin, Henning Müller, Sandy Napel, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Elisabeth A.G. Pfaehler, Arman Rahmim, Arvind U.K. Rao, Jonas Scherer, Muhammad Musib Siddique, Nanna M. Sijtsema, Jairo Socarras Fernandez, Emiliano Spezi, Roel J.H.M. Steenbakkers, Stephanie Tanadini-Lang, Daniela Thorwarth, Esther G.C. Troost, Taman Upadhaya, Vincenzo Valentini, Lisanne V. Van Dijk, Joost Van Griethuysen, Floris H.P. Van Velden, Philip Whybra, Christian Richter, Steffen Löck
  • Technische Universität Dresden (TU Dresden)
  • McGill University
  • Moffitt Cancer Center
  • Harvard University
  • University of Applied Sciences Western Switzerland
  • Memorial Sloan-Kettering Cancer Center
  • Johns Hopkins University
  • University of Pennsylvania
  • University of Groningen
  • University of Zurich
  • Université Paris-Saclay
  • King's College London
  • UBL
  • Netherlands Cancer Institute
  • Stanford University
  • German Cancer Research Center
  • Maastricht University Medical Center
  • University of Tübingen
  • University of Bergen
  • University of California at San Francisco
  • University of Michigan, Ann Arbor
  • Cardiff University
  • Leiden University

Risultato della ricerca: Contributo in rivistaArticolo

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.
Lingua originaleInglese
pagine (da-a)328-338
Numero di pagine11
RivistaRadiology
Volume295
DOI
Stato di pubblicazionePubblicato - 2020

OSS delle Nazioni Unite

Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile

  1. SDG 3 - Salute e benessere
    SDG 3 Salute e benessere

Keywords

  • Biomarkers
  • Calibration
  • Fluorodeoxyglucose F18
  • Humans
  • Image Processing, Computer-Assisted
  • Lung Neoplasms
  • Magnetic Resonance Imaging
  • Phantoms, Imaging
  • Phenotype
  • Positron-Emission Tomography
  • Radiopharmaceuticals
  • Reproducibility of Results
  • Sarcoma
  • Software
  • Tomography, X-Ray Computed

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