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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Alex Zwanenburg - , OncoRay - National Centre for Radiation Research in Oncology, Nationales Zentrum für Tumorerkrankungen (NCT) Dresden, Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Martin Vallières - , McGill University (Autor:in)
  • Mahmoud A Abdalah - , McGill University (Autor:in)
  • Hugo J W L Aerts - , McGill University (Autor:in)
  • Vincent Andrearczyk - , McGill University (Autor:in)
  • Aditya Apte - , McGill University (Autor:in)
  • Saeed Ashrafinia - , McGill University (Autor:in)
  • Spyridon Bakas - , McGill University (Autor:in)
  • Roelof J Beukinga - , McGill University (Autor:in)
  • Ronald Boellaard - , McGill University (Autor:in)
  • Marta Bogowicz - , McGill University (Autor:in)
  • Luca Boldrini - , McGill University (Autor:in)
  • Irène Buvat - , McGill University (Autor:in)
  • Gary J R Cook - , McGill University (Autor:in)
  • Christos Davatzikos - , McGill University (Autor:in)
  • Adrien Depeursinge - , McGill University (Autor:in)
  • Marie-Charlotte Desseroit - , McGill University (Autor:in)
  • Nicola Dinapoli - , McGill University (Autor:in)
  • Cuong Viet Dinh - , McGill University (Autor:in)
  • Sebastian Echegaray - , McGill University (Autor:in)
  • Issam El Naqa - , McGill University (Autor:in)
  • Andriy Y Fedorov - , McGill University (Autor:in)
  • Roberto Gatta - , McGill University (Autor:in)
  • Robert J Gillies - , McGill University (Autor:in)
  • Vicky Goh - , McGill University (Autor:in)
  • Michael Götz - , McGill University (Autor:in)
  • Matthias Guckenberger - , McGill University (Autor:in)
  • Sung Min Ha - , McGill University (Autor:in)
  • Mathieu Hatt - , McGill University (Autor:in)
  • Fabian Isensee - , McGill University (Autor:in)
  • Philippe Lambin - , McGill University (Autor:in)
  • Stefan Leger - , OncoRay - Nationales Zentrum für Strahlenforschung in der Onkologie, Deutsches Konsortium für Translationale Krebsforschung (Partner: DKTK, DKFZ), Nationales Centrum für Tumorerkrankungen Dresden, OncoRay - National Centre for Radiation Research in Oncology, Universitätsklinikum Carl Gustav Carus Dresden, Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Ralph T H Leijenaar - , McGill University (Autor:in)
  • Jacopo Lenkowicz - , McGill University (Autor:in)
  • Fiona Lippert - , McGill University (Autor:in)
  • Are Losnegård - , McGill University (Autor:in)
  • Klaus H Maier-Hein - , McGill University (Autor:in)
  • Olivier Morin - , McGill University (Autor:in)
  • Henning Müller - , McGill University (Autor:in)
  • Sandy Napel - , McGill University (Autor:in)
  • Christophe Nioche - , McGill University (Autor:in)
  • Fanny Orlhac - , McGill University (Autor:in)
  • Sarthak Pati - , McGill University (Autor:in)
  • Elisabeth A G Pfaehler - , McGill University (Autor:in)
  • Arman Rahmim - , McGill University (Autor:in)
  • Arvind U K Rao - , McGill University (Autor:in)
  • Jonas Scherer - , McGill University (Autor:in)
  • Muhammad Musib Siddique - , McGill University (Autor:in)
  • Nanna M Sijtsema - , McGill University (Autor:in)
  • Jairo Socarras Fernandez - , McGill University (Autor:in)
  • Emiliano Spezi - , McGill University (Autor:in)
  • Roel J H M Steenbakkers - , McGill University (Autor:in)
  • Stephanie Tanadini-Lang - , McGill University (Autor:in)
  • Daniela Thorwarth - , McGill University (Autor:in)
  • Esther G C Troost - , Klinik und Poliklinik für Strahlentherapie und Radioonkologie, Deutsches Konsortium für Translationale Krebsforschung (Partner: DKTK, DKFZ), OncoRay - Nationales Zentrum für Strahlenforschung in der Onkologie, Nationales Centrum für Tumorerkrankungen Dresden, Universitätsklinikum Carl Gustav Carus Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Taman Upadhaya - , McGill University (Autor:in)
  • Vincenzo Valentini - , McGill University (Autor:in)
  • Lisanne V van Dijk - , McGill University (Autor:in)
  • Joost van Griethuysen - , McGill University (Autor:in)
  • Floris H P van Velden - , McGill University (Autor:in)
  • Philip Whybra - , McGill University (Autor:in)
  • Christian Richter - , Klinik und Poliklinik für Strahlentherapie und Radioonkologie, OncoRay - Nationales Zentrum für Strahlenforschung in der Onkologie, Deutsches Konsortium für Translationale Krebsforschung (Partner: DKTK, DKFZ), Deutsches Krebsforschungszentrum (DKFZ), Helmholtz-Zentrum Dresden-Rossendorf (Autor:in)
  • Steffen Löck - , OncoRay - Nationales Zentrum für Strahlenforschung in der Onkologie, Deutsches Konsortium für Translationale Krebsforschung (Partner: DKTK, DKFZ), Klinik und Poliklinik für Strahlentherapie und Radioonkologie, Universitätsklinikum Carl Gustav Carus Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)328-338
Seitenumfang11
FachzeitschriftRadiology
Jahrgang295
Ausgabenummer2
PublikationsstatusVeröffentlicht - Mai 2020
Peer-Review-StatusJa

Externe IDs

PubMedCentral PMC7193906
Scopus 85081738849
ORCID /0000-0002-7017-3738/work/142253975
ORCID /0000-0003-4261-4214/work/147143105

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

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