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

Radiology · 2020 · Vol. 295(2) · pp. 328–338
Alex ZwanenburgMartin VallièresMahmoud A. AbdalahHugo J.W.L. AertsVincent AndrearczykAditya ApteSaeed AshrafiniaSpyridon BakasRoelof J. BeukingaRonald BoellaardMarta BogowiczLuca BoldriniIrène BuvatGary CookChristos DavatzikosAdrien DepeursingeMarie-Charlotte DesseroitN. DinapoliCuong V. DinhSebastian EchegarayIssam El NaqaAndriy FedorovRoberto GattaRobert J. GilliesVicky GohMichael GötzMatthias GückenbergerSung Min HaMathieu HattFabian IsenseePhilippe LambinStefan LegerRalph T. H. LeijenaarJacopo LenkowiczFiona LippertAre LosnegårdKlaus Maier‐HeinOlivier MorinHenning MüllerSandy NapelChristophe NiocheFanny OrlhacSarthak PatiElisabeth PfaehlerArman RahmimArvind U K RaoJonas SchererMuhammad Musib SiddiqueNanna M. SijtsemaJairo Socarras FernandezEmiliano SpeziRoel J.H.M. SteenbakkersStephanie Tanadini‐LangDaniela ThorwarthEsther G.C. TroostTaman UpadhayaVincenzo ValentiniLisanne V. van DijkJoost J. M. van GriethuysenFloris H. P. van VeldenP. WhybraChristian RichterSteffen Löck

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 <i>Online supplemental material is available for this article.</i> See also the editorial by Kuhl and Truhn in this issue.

Radiomics and Machine Learning in Medical ImagingAI in cancer detectionColorectal Cancer Surgical TreatmentsStandardizationBiomarkerBenchmark (surveying)MedicineImaging biomarkerRadiomicsThroughputField (mathematics)Image processingImage (mathematics)

MeSH terms

CalibrationHumansImage Processing, Computer-AssistedLung NeoplasmsMagnetic Resonance ImagingPhenotypeSarcomaSoftwareTomography, X-Ray ComputedReproducibility of ResultsBiomarkersPhantoms, ImagingRadiopharmaceuticalsFluorodeoxyglucose F18Positron-Emission Tomography

Funding

  • National Science Foundation
  • Wellcome
  • Brigham and Women's Hospital
  • Cancer Research UK
  • Department of Health and Social Care
  • Cardiff University
  • Agence Nationale de la Recherche
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
  • Universiteit Leiden
  • Universiteit Maastricht
  • Bundesministerium für Bildung und Forschung
  • Technische Universität Dresden
  • Leids Universitair Medisch Centrum
  • Universität Zürich
  • Innovative Medicines Initiative
  • Helmholtz-Zentrum Dresden-Rossendorf
  • National Institutes of Health
  • Horizon 2020 Framework Programme
  • Seventh Framework Programme
  • Medical Research Council
  • Engineering and Physical Sciences Research Council
  • European Research Council
  • Stichting voor de Technische Wetenschappen
  • National Cancer Institute
  • National Institute of Neurological Disorders and Stroke
  • Interreg
  • Eurostars
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