Tomography · 2020 · 13 citations · 9 references
MedicineOncologyRadiologyDiagnostic ImagingDiagnostic RadiologyNeuro-oncologyBrain ImagingMagnetic Resonance ImagingMedical Image AnalysisMedical ImagingNeuro-oncology ImagingTumor GradingDynamic Magnetic SusceptibilityDynamic Susceptibility Contrast ImagingSoftware BenchmarkingTherapy Response EvaluationTreatment Response ClassificationTreatment Response PredictionTumor Grade AssessmentTumor Grade Classification
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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The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Kenneth Clark, Bruce A. Vendt, Kirk Smith et al. · Journal of Digital Imaging · 2013 · 4.6K citations · Full text
Kathleen M. Schmainda, Zheng Zhang, Melissa Prah et al. · Neuro-Oncology · 2015 · 126 citations · Full text
J. L. Boxerman, Zheng Zhang, Yair Safriel et al. · Neuro-Oncology · 2013 · 83 citations · Full text
Kathleen M. Schmainda, Melissa Prah, Scott D. Rand et al. · American Journal of Neuroradiology · 2018 · 56 citations · Full text