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Accès ouvert déclaré 2023 article

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

13Citations signalées, ce qui n’est pas une note de qualité
49Institutions déclarées
9Pays d’affiliation déclarés

Rattachement africain : gb, us, nl, it, de, in, sg, cz, ir. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

Abstract Purpose 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 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 measurement. Methods A framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components. Results Across the 10 received submissions, the 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 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 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.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.

Titre Crossref
The <scp>ISMRM</scp> Open Science Initiative for Perfusion Imaging (<scp>OSIPI</scp>): Results from the <scp>OSIPI–Dynamic Contrast‐Enhanced</scp> challenge
Date Crossref
19/12/2023
Éditeur
Wiley
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

University of LeedsUniversity of SheffieldUniversity of Alabama at BirminghamUniversity of Wisconsin–MadisonErasmus MCBeaumont Hospital, TroyMayo Clinic in ArizonaThe University of Texas at AustinLivestrong FoundationThe University of Texas MD Anderson Cancer CenterBarrow Neurological InstituteUniversity of Rome Tor VergataImperial College LondonUniversity College London Hospitals NHS Foundation TrustNational Hospital for Neurology and NeurosurgeryUniversity College LondonHarvard UniversityAthinoula A. Martinos Center for Biomedical ImagingUniversitätsmedizin GöttingenIndian Institute of Technology DelhiInstitute of Bioengineering and NanotechnologySingapore Bioimaging ConsortiumKing's College LondonUniversity of WashingtonMemorial Sloan Kettering Cancer CenterAmsterdam University of Applied SciencesUniversity of AmsterdamDutch Cancer SocietyCancer Center AmsterdamCzech Academy of Sciences, Institute of Scientific InstrumentsCzech Academy of Sciences, Institute of Information Theory and AutomationCornell UniversityNew York UniversityUniversity of ManchesterCancer Research UK Manchester InstituteInstitute of Cancer ResearchThe Christie HospitalAbsynth Biologics (United Kingdom)Advanced Imaging Research (United States)Medical College of WisconsinFraunhofer Institute for Digital MedicineUniversity of California, Los AngelesChildren's Hospital of PhiladelphiaUniversity of PennsylvaniaSimulation Technologies (United States)Tehran University of Medical SciencesWinnMedMayo Clinic HospitalMayo Clinic in Florida

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

MRI in cancer diagnosisRadiomics and Machine Learning in Medical ImagingAdvanced MRI Techniques and Applications

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