BACKGROUND: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous @entity5 ( @entity463 ). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate @entity5 in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative @entity5 within surrounding non-enhancing parenchyma, or @entity1313 ( @entity1313 ), despite the importance of characterizing this @entity5 segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating @entity5 -rich targets in @entity463 . METHODS: We recruited primary @entity463 @entity1 undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low- @entity5 content (>= 80% vs <80% @entity5 nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish @entity5 content. We confirmed model accuracy in a separate validation set. RESULTS: We collected 82 biopsies from 18 GBMs throughout ENH and @entity1313 . The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low- @entity5 in the training set (60 biopsies, 11 @entity1 ). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 @entity1 ). CONCLUSION: Multi-parametric MRI and texture analysis can help characterize and visualize @entity463 's spatial histologic heterogeneity to identify regional @entity5 -rich biopsy targets.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in XXXX .

@entity5 :: ('MESH:D009369', 'Disease') :: ['tumors like Glioblastoma', 'tumor'] @entity1 :: ('9606', 'Species') :: ['patients'] @entity463 :: ('MESH:D005909', 'Disease') :: ['GBM'] @entity1313 :: ('MESH:D001932', 'Disease') :: ['brain-around-tumor', 'BAT']
@entity463 :: (MESH:D005909,Disease) :: ['Glioblastoma']
