Predicting Spatial Patterns of Recurrence for Glioblastoma Using Multi-Parametric MRI Classification

Keywords

abbreviations ability abnormal accuracies accuracy acquisition added additional axial bottom capture cerebral classification classifier consistent correction cross curve deviation diagnosed difficult diffusion diffusivity distant dose drawn earlier enhanced even expert farther feature find finding flair focused fractional generated grade grim healthy hope human ignored improve improved incidence included indicate infiltrative inhomogeneity institution intermediate intra investigated just knowledge known landmarks life location machine manual manually maps modalities model months nearby nearer necrosis nevertheless noses note operative originated overlaid patients patterns planning post practice predicted prediction predictions prior probability processing produce produced progressed progression promise quality quickly quite radial radiology radiotherapy recur recurrence recurring relevant resection reveal section segmentation selected sens sensitivity site skull smoothing spatial spec specificity statistics stripping subj subject subjects subsequent summary surprisingly survival table tends tensor therapy thinking tissue tissues tool tools trained treatment tumor tumors ultimately useful validated variable variate varied various vector volume whereas wolf years