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

Abstract

BackgroundGenetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor 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 tumor-rich targets in GBM.MethodsWe recruited primary GBM patients 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-tumor content (≥80% vs <80% tumor 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 tumor content. We confirmed model accuracy in a separate validation set.ResultsWe collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).ConclusionMulti-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

“…23 In short, each neurosurgeon collected an average of 5-6 tissue specimens from each tumor using stereotactic localization, following the smallest possible diameter craniotomies to minimize brain shift. Neurosurgeons generally selected targets separated by ≥1 cm from both ENH (T1 + C) and nonenhancing BAT (T1 + C,T2W) regions within different poles of the tumor based on clinical feasibility (e.g.…”

“…accessibility of the target site, overlying vessels, eloquent brain). 23 Neurosurgeons recorded biopsy locations to allow subsequent coregistration with multiparametric MRI datasets. The neurosurgeon visually validated stereotactic imaging locations with corresponding intracranial anatomic landmarks, such as vascular structures and ventricle margins, before recording specimen locations.…”

“…The neurosurgeon visually validated stereotactic imaging locations with corresponding intracranial anatomic landmarks, such as vascular structures and ventricle margins, before recording specimen locations. 23 …”

“…Conventional MRI and Advanced MRI parameters have been detailed previously. 23 Briefly, we acquired postcontrast T1-weighted (T1 + C) SPGR-IR (TI/TR/TE = 300/6.8/2.8 ms; matrix = 320 × 224; Field of View (FOV) 26 cm; thickness = 2 mm) and T2-weighted (T2W) fast-spin-echo (TR/TE = 5133/78 ms; matrix = 320 × 192; FOV = 26 cm; thickness = 2 mm). We acquired T1 + C images after completing dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI (pMRI) following total Gd-DTPA (gadobenate dimeglumine) dosage of 0.15 mmol/ kg as previously described.…”

“…We acquired T1 + C images after completing dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MRI (pMRI) following total Gd-DTPA (gadobenate dimeglumine) dosage of 0.15 mmol/ kg as previously described. 23,27 Diffusion Tensor Imaging (DTI)We acquired DTI using spin-echo echo-planar imaging (EPI) (TR/TE = 10000/85.2 ms, matrix = 256 × 256; FOV = 30 cm, slice = 3 mm, 25 directions, B = 0,1000). We calculated the following parameters as previously described: isotropic (p) and anisotropic (q) diffusion, mean diffusivity (MD) and fractional anisotrophy (FA).…”