Cumulative Distribution Function Plots Comparison.

<p>Cumulative Distribution Function (CDF) plots comparison, between: (a). APOE e4 carriers <b>(e3/e4 and e4/e4)</b> and non-carriers <b>(e3/e3)</b>, (b). APOE e4 homozygotes <b>(e4/e4)</b> and heterozygotes <b>(e3/e4)</b>, (c). APOE e4 homozygotes <b>(e4/e4)</b> and non-carriers <b>(e3/e3)</b>, (d). APOE e4 heterozygotes <b>(e3/e4)</b> and non-carriers <b>(e3/e3)</b> in the full ADNI cohorts. The results demonstrate the accelarated hippocampal atrophy in the longitudinal study.</p

Shape Differences between Heterozygotes and Homozygotes in Nondemented.

<p>(a), (b) and (c): Illustration of local shape differences (<i>p</i>-values) between the APOE e4 homozygotes (e4/e4) and heterozygotes (e3/e4) in the non-demented cohorts at 6-months, 12-months and 24-months, respectively. Non-blue colours show vertices with statistical differences, at the nominal 0.05 level, uncorrected. The overall significance after multiple comparisons with permutation test is: (a) <i>p</i> < 0.1351, (b) <i>p</i> < 0.0204, (c) <i>p</i> < 0.187.</p

Shape Differences between Non-carriers and Carriers in Nondemented.

<p>(a), (b) and (c): Illustration of local shape differences (<i>p</i>-values) between the APOE e4 carriers <b>(e3/e4 and e4/e4)</b> and non-carriers <b>(e3/e3)</b> in the non-demented cohorts at 6-months, 12-months and 24-months, respectively. Non-blue colours show vertices with statistical differences, at the nominal 0.05 level, uncorrected. The overall significance after multiple comparisons with permutation test is: (a) <i>p</i> < 0.001, (b) <i>p</i> < 0.0005, (c) <i>p</i> < 0.0015.</p

Table of Demographic Data by Diagnositic and Genotype Groups.

<p>Demographic data by diagnositic and genotype groups. N₆, N₁₂, and N₂₄ indicate sample size of the 6-month, 12-month and 24-month follow up cohorts, respectively. The number of women in the samples is indicated in parentheses. Means are followed by standard deviations in parentheses for age and MMSE measures.</p

Shape Differences between Heterozygotes and Homozygotes in Full ADNI.

<p>(a), (b) and (c): Illustration of local shape differences (<i>p</i>-values) between the APOE e4 homozygotes (e4/e4) and heterozygotes (e3/e4) in the full ADNI cohorts at 6-months, 12-months and 24 months, respectively. Non-blue colours show vertices with statistical differences, at the nominal 0.05 level, uncorrected. The overall significance after multiple comparisons with permutation test is: (a) <i>p</i> < 0.0117, (b) <i>p</i> < 0.0024, (c) <i>p</i> < 0.0959.</p

Shape Differences between Non-carriers and Homozygotes in Nondemented.

<p>(a), (b) and (c): Illustration of local shape differences (<i>p</i>-values) between the APOE e4 homozygotes <b>(e4/e4)</b> and non-carriers <b>(e3/e3)</b> in the non-demented cohorts at 6-months, 12-months and 24-months, respectively. Non-blue colours show vertices with statistical differences, at the nominal 0.05 level, uncorrected. The overall significance after multiple comparisons with permutation test is: (a) <i>p</i> < 0.0035, (b) <i>p</i> < 0.001, (c) <i>p</i> < 0.077.</p

Shape Differences between Non-carriers and Homozygotes in Full ADNI.

<p>(a), (b) and (c): Illustration of local shape differences (<i>p</i>-values) between the APOE e4 homozygotes (e4/e4) and non-carriers (e3/e3) in the full ADNI cohorts at 6-months, 12-months and 24 months, respectively. Non-blue colours show vertices with statistical differences, at the nominal 0.05 level, uncorrected. The overall significance after multiple comparisons with permutation test is: (a) <i>p</i> < 0.0001, (b) <i>p</i> < 0.0001, (c) <i>p</i> < 0.0001.</p

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

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

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