11 research outputs found
Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma
<div><p>Background</p><p>Genetic 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.</p><p>Methods</p><p>We 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.</p><p>Results</p><p>We 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).</p><p>Conclusion</p><p>Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.</p></div
ML-based MRI invasion maps show tumor-rich (>80% tumor nuclei) extent throughout ENH and BAT.
<p>(A,B,C,E) Biopsy locations within the non-enhancing BAT zone (green dots, arrows) on T1+C (A,D) and T2W (B,E) images correspond with high-tumor (>80% tumor nuclei) and low-tumor (<80% tumor nuclei) tissue samples on histologic analysis. (C,F) Color overlay maps with manual tracings (green) around BAT show the probability (range 0–1) of tumor-rich (red) vs tumor-poor (green/blue) content, based on ML analysis and multi-parametric MRI in 60 training biopsies and 22 validation biopsies. The maps show correspondence between tumor-rich (B, red) and tumor-poor (D, blue/gray) biopsy samples.</p
ML-based model improves tumor-rich biopsy delineation compared with CE-MRI.
<p>(A) ML-based MRI texture model in the full dataset (n = 82, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141506#pone.0141506.t001" target="_blank">Table 1</a>) shows higher positive predictive values (PPV) (66.7% in BAT, 81.3% in ENH) for recovering tumor-rich samples compared with CE-MRI (21.2% in BAT, 59.2% in ENH). These PPVs suggest that the ML-based model would help recover tumor-rich BAT samples with over three times greater efficiency compared with CE-MRI guidance. (B) ML-based MRI texture model in the subanalysis (n = 76, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141506#pone.0141506.s002" target="_blank">S2 Appendix</a>) provides higher positive predictive values (PPV) (57.1% in BAT, 80.6% in ENH) for recovering tumor-rich samples (>80% tumor nuclei) compared with CE-MRI (13.8% in BAT, 59.6% in ENH). Based on these PPVs, the ML-based model would enable four times more efficient tumor-rich recovery from BAT compared with CE-MRI guidance.</p
Summary of selected MRI-based texture features to optimize CV training accuracy.
<p>Machine learning (ML) selected the 3 MRI-based texture features that optimized cross validation (CV) accuracy based on leave-one-out cross validation (LOOCV) of the training set data (60 biopsies, 11 patients). The overall CV accuracy based on the 3 features is 85%.</p><p>Summary of selected MRI-based texture features to optimize CV training accuracy.</p
Additional file 6: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Table S5. Simulation analyses. (XLSX 28 kb
Additional file 4: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Table S3. Percentage of variance explained by the combined sets of single nucleotide polymorphisms used as instrumental variables. (XLSX 33 kb
Additional file 5: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Table S4. Range of odds ratios for which study had < 80% power, for each atopy-related trait (P = 0.05, two-sided). (XLSX 9 kb
Additional file 3: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Table S2. Table of single nucleotide polymorphisms (SNPs) reported in the NHGRI-EBI Genome-wide Association Studies Catalog for each trait, with correlations between SNPs. (XLSX 48 kb
Additional file 2: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Table S1. Summary of the eight glioma genome-wide association studies. (XLSX 29 kb
Additional file 1: of Impact of atopy on risk of glioma: a Mendelian randomisation study
Figure S1. Forest plot of Wald odds ratios (ORs) and 95% confidence intervals generated from single nucleotide polymorphisms (SNPs) associated with atopic dermatitis, including rs909341. ORs for individual SNPs are listed according to magnitude of effect in the instrumental variable analysis and are presented with pooled effects using the inverse-variance weighting method. Squares represent the point estimate, and the bars are the 95% confidence intervals. (DOCX 89 kb