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The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge
ES. Shalom, H. Kim, RA. van der Heijden, Z. Ahmed, R. Patel, DA. Hormuth, JC. DiCarlo, TE. Yankeelov, NJ. Sisco, RD. Dortch, AM. Stokes, M. Inglese, M. Grech-Sollars, N. Toschi, P. Sahoo, A. Singh, SK. Verma, DK. Rathore, AS. Kazerouni, SC....
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články
Grantová podpora
2282622
EPSRC-CASE
Bracco Diagnostics
PubMed
38115695
DOI
10.1002/mrm.29909
Knihovny.cz E-zdroje
- MeSH
- algoritmy MeSH
- kontrastní látky * MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- reprodukovatelnost výsledků MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE: Ktrans$$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize Ktrans$$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate Ktrans$$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' Ktrans$$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in Ktrans$$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within Ktrans$$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.
Advanced Imaging Research Center Oregon Health and Science Institute Portland Oregon USA
Athinoula A Martinos Center for Biomedical Imaging Harvard Medical School Boston Massachusetts USA
Cancer Center Amsterdam Imaging and Biomarkers Amsterdam The Netherlands
Center for Biomedical Engineering Indian Institute of Technology Delhi New Delhi India
Clinical Imaging Group Genentech Inc South San Francisco California USA
Corewell Health William Beaumont University Hospital Royal Oak Michigan USA
Czech Academy of Sciences Institute of Information Theory and Automation Praha Czech Republic
Czech Academy of Sciences Institute of Scientific Instruments Brno Czech Republic
Department of Biomedicine and Prevention University of Rome Tor Vergata Italy
Department of Computer Science University College London London UK
Department of Diagnostic Medicine University of Texas Austin Texas USA
Department of Imaging Physics MD Anderson Cancer Center Houston Texas USA
Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield UK
Department of Medical Physics Memorial Sloan Kettering Cancer Center New York New York USA
Department of Neurosurgery Perelman School of Medicine University of Pennsylvania Pennsylvania USA
Department of Radiological Sciences University of California Los Angeles California USA
Department of Radiology and Nuclear Medicine University of Amsterdam Amsterdam The Netherlands
Department of Radiology Grossman School of Medicine New York University New York New York USA
Department of Radiology Medical College of Wisconsin Milwaukee Wisconsin USA
Department of Radiology Memorial Sloan Kettering Cancer Center New York New York USA
Department of Radiology Neuroradiology Division Mayo Clinic Scottsdale Arizona USA
Department of Radiology The Christie Hospital NHS Trust Manchester UK
Department of Radiology University of Alabama Birmingham Alabama USA
Department of Radiology University of Washington Seattle Washington USA
Department of Radiology University of Wisconsin Madison Madison Wisconsin USA
Department of Radiology Weill Cornell Medical College New York New York USA
Department of Surgery and Cancer Imperial College London UK
Department of Translational Neuroscience Barrow Neurological Institute Phoenix Arizona USA
Division of Cancer Sciences University of Manchester Manchester UK
Division of Radiotherapy and Imaging The Institute of Cancer Research London UK
Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany
Institute of Bioengineering and Bioimaging Singapore Singapore
Institute of Psychiatry Psychology and Neuroscience King's College London UK
Neuroradiology Division Department of Radiology Mayo Clinic Phoenix Arizona USA
Oden Institute for Computational Engineering and Sciences The University of Texas Austin Texas USA
School of Physics and Astronomy University of Leeds Leeds UK
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