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Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge.
Bell, Laura C; Semmineh, Natenael; An, Hongyu; Eldeniz, Cihat; Wahl, Richard; Schmainda, Kathleen M; Prah, Melissa A; Erickson, Bradley J; Korfiatis, Panagiotis; Wu, Chengyue; Sorace, Anna G; Yankeelov, Thomas E; Rutledge, Neal; Chenevert, Thomas L; Malyarenko, Dariya; Liu, Yichu; Brenner, Andrew; Hu, Leland S; Zhou, Yuxiang; Boxerman, Jerrold L; Yen, Yi-Fen; Kalpathy-Cramer, Jayashree; Beers, Andrew L; Muzi, Mark; Madhuranthakam, Ananth J; Pinho, Marco; Johnson, Brian; Quarles, C Chad.
Afiliação
  • Bell LC; Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ.
  • Semmineh N; Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ.
  • An H; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO.
  • Eldeniz C; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO.
  • Wahl R; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO.
  • Schmainda KM; Departments of Radiology; and.
  • Prah MA; Biophysics, Medical College of Wisconsin, Milwaukee, WI.
  • Erickson BJ; Biophysics, Medical College of Wisconsin, Milwaukee, WI.
  • Korfiatis P; Department of Radiology, Mayo Clinic, Rochester, MN.
  • Wu C; Department of Radiology, Mayo Clinic, Rochester, MN.
  • Sorace AG; Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX.
  • Yankeelov TE; Department of Radiology, University of Alabama at Birmingham, Birmingham, AL.
  • Rutledge N; Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX.
  • Chenevert TL; Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX.
  • Malyarenko D; Department of Radiology, University of Michigan, Ann Arbor, MI.
  • Liu Y; Department of Radiology, University of Michigan, Ann Arbor, MI.
  • Brenner A; UT Health San Antonio, San Antonio, TX.
  • Hu LS; UT Health San Antonio, San Antonio, TX.
  • Zhou Y; Department of Radiology, Mayo Clinic, Scottsdale, AZ.
  • Boxerman JL; Department of Radiology, Mayo Clinic, Scottsdale, AZ.
  • Yen YF; Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI.
  • Kalpathy-Cramer J; Department of Radiology, MGH-Martinos Center for Biomedical Imaging, Boston, MA.
  • Beers AL; Department of Radiology, MGH-Martinos Center for Biomedical Imaging, Boston, MA.
  • Muzi M; Department of Radiology, MGH-Martinos Center for Biomedical Imaging, Boston, MA.
  • Madhuranthakam AJ; Radiology, University of Washington, Seattle, WA.
  • Pinho M; Department of Radiology, UT Southwestern Medical Center, Dallas, TX; and.
  • Johnson B; Department of Radiology, UT Southwestern Medical Center, Dallas, TX; and.
  • Quarles CC; Department of Radiology, UT Southwestern Medical Center, Dallas, TX; and.
Tomography ; 6(2): 203-208, 2020 06.
Article em En | MEDLINE | ID: mdl-32548297
We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Volume Sanguíneo Cerebral Tipo de estudo: Guideline / Observational_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Volume Sanguíneo Cerebral Tipo de estudo: Guideline / Observational_studies Limite: Humans Idioma: En Revista: Tomography Ano de publicação: 2020 Tipo de documento: Article