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  4. The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI–Dynamic Contrast-Enhanced challenge
 
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2024
Journal Article
Title

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI–Dynamic Contrast-Enhanced challenge

Abstract
Purpose: (Formula presented.) 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 (Formula presented.) 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 (Formula presented.) measurement. Methods: A framework was created to evaluate (Formula presented.) values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for (Formula presented.) quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' (Formula presented.) values, the applied software, and a standard operating procedure. These were evaluated using the proposed (Formula presented.) score defined with accuracy, repeatability, and reproducibility components. Results: Across the 10 received submissions, the (Formula presented.) 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 (Formula presented.) 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 (Formula presented.) 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.
Author(s)
Shalom, Eve S.
Kim, Harrison
van der Heijden, Rianne A.
Ahmed, Zaki
Patel, Reyna A.
Hormuth, David A.
DiCarlo, Julie C.
Yankeelov, Thomas Edward
Sisco, Nicholas J.
Dortch, Richard D.
Stokes, Ashley M.
Inglese, Marianna
Grech-Sollars, Matthew
Toschi, Nicola
Sahoo, Prativa
Singh, Anup
Verma, Sanjay Kumar
Rathore, Divya K.
Kazerouni, Anum S.
Partridge, Savannah C.
LoCastro, Eve M.
Paudyal, Ramesh
Wolansky, Ivan A.
Shukla-Dave, Amita
Schouten, Pepijn
Gurney-Champion, Oliver J.
Jiřík, Radovan
Macíček, Ondřej
Bartoš, Michal
Vitouš, Jiri
Das, Ayesha Bharadwaj
Kim, Sungheon Gene
Bokacheva, Louisa
Mikheev, Artem V.
Rusinek, Henry
Berks, Michael
Cristinacce, Penny L.Hubbard
Little, Ross A.
Cheung, Susan
O’Connor, James P.B.
Parker, Geoff J.M.
Moloney, Brendan
LaViolette, Peter S.
Bobholz, Samuel A.
Duenweg, Savannah R.
Virostko, John M.
Laue, Hendrik
Fraunhofer-Institut für Digitale Medizin MEVIS  
Sung, Kyunghyun
Nabavizadeh, Seyed Ali
Saligheh Rad, Hamidreza
Hu, Leland S.
Sourbron, Steven P.
Bell, Laura C.
Fathi Kazerooni, Anahita
Journal
Magnetic resonance in medicine : MRM  
Open Access
DOI
10.1002/mrm.29909
Additional link
Full text
Language
English
Fraunhofer-Institut für Digitale Medizin MEVIS  
Keyword(s)
  • challenge

  • data analysis

  • DCE-MRI

  • glioblastoma

  • open-science

  • perfusion

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