American Journal of Neuroradiology · 2018 · 100 citations · 22 references
MedicineOncologyRadiologyDiagnostic RadiologyNeuro-oncologyTumor ProgressionBrain ImagingBrain TumorsMagnetic Resonance ImagingNeuro-oncologic ImagingNeuro-oncology ImagingTherapy Response AssessmentGlioblastoma ImagingImaging Of Tumor RecurrenceMultiparametric ImagingMultiparametric Magnetic Resonance Imaging (Mri)Neuroimaging Response AssessmentPreoperative Tumor CharacterizationShape FeaturesTumor Progression After Therapy
BACKGROUND AND PURPOSE: Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone. MATERIALS AND METHODS: = 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 "global" contour characteristics and 16 "local" curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier. RESULTS: The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression. CONCLUSIONS: Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.
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Gene Selection for Cancer Classification using Support Vector Machines
Isabelle Guyon, Jason Weston, S. Barnhill et al. · Machine Learning · 2002 · 9.8K citations · Full text
N4ITK: Improved N3 Bias Correction
Nicholas J. Tustison, Brian Avants, Philip A. Cook et al. · IEEE Transactions on Medical Imaging · 2010 · 6.4K citations
Medicine, Neuroscience, Radiology +16