Evaluating potential of multi-parametric MRI using co-registered histology: Application to a mouse model of glioblastoma.

BACKGROUND: Conventional MRI fails to detect regions of glioblastoma cell infiltration beyond the contrast-enhanced T1 solid tumor region, with infiltrating tumor cells often migrating along host blood vessels. PURPOSE: MRI is capable of generating a range of image contrasts which are commonly assessed individually by qualitative visual inspection. It has long been hypothesized that better diagnoses could be achieved by combining these multiple images, so called multi-parametric or multi-spectral MRI. However, the lack of clinical histology and the difficulties of co-registration, has meant this hypothesis has never been rigorously tested. Here we test this hypothesis, using a previously published multi-dimensional dataset consisting of registered MR images and histology. STUDY TYPE: Animal Model. SUBJECTS: Mice bearing orthotopic glioblastoma xenografts generated from a patient-derived glioblastoma cell line. FIELD STRENGTH/SEQUENCES: 7 Tesla, T1/T2 weighted, T2 mapping, contrast enhance T1, diffusion-weighted, diffusion tensor imaging. ASSESSMENT: Immunohistochemistry sections were stained for Human Leukocyte Antigen (probing human-derived tumor cells). To achieve quantitative MRI-tissue comparison, multiple histological slices cut in the MRI plane were stacked to produce tumor cell density maps acting as 'ground truth'. STATISTICAL TESTS: Sensitivity, specificity, accuracy and Dice similarity indices were calculated. ANOVA, t-test, Bonferroni correction and Pearson coefficients were used for statistical analysis. RESULTS: Correlation coefficient analysis with co-registered 'ground truth' histology showed interactive regression maps had higher correlation coefficients and sensitivity values than T2W, ADC, FA, and T2map. Further, the interaction regression maps showed statistical improved detection of tumor volume. DATA CONCLUSION: Voxel-by-voxel analysis provided quantitative evidence confirming the hypothesis that mpMRI can, potentially, better distinguish between the tumor region and normal tissue.

Stacked in-plane histology for quantitative validation of non-invasive imaging biomarkers: Application to an infiltrative brain tumour model.

BACKGROUND: While it is generally agreed that histopathology is the gold standard for assessing non-invasive imaging biomarkers, most validation has been by qualitative visual comparison. To date, the difficulties involved in accurately co-registering histology sections with imaging slices have prevented a voxel-by-voxel assessment of imaging modalities. By contrast with previous studies, which focus on improving the registration algorithms, we have taken the approach of improving the quality of the histological processing and analysis. NEW METHOD: To account for imaging slice orientation and thickness, multiple histology sections were cut in the MR imaging plane and averaged to produce stacked in-plane histology (SIH) maps. When combined with intensity sensitive staining this approach gives histopathology maps, which can be used as the gold standard to validate imaging biomarkers. RESULTS: We applied this pipeline to a patient-derived mouse model of glioblastoma multiforme (GBM). Increasing the number of stacked histology sections significantly increased SIH measured tumour volume. The SIH technique proposed here resulted in reduced variability of volume measurements and this allowed significant improvements in the quantitative volumetric assessment of multiple MRI modalities. Further, high quality registration enabled a voxel-wise comparison between MRI and histopathology maps. Previous approaches to the validation of imaging biomarkers with histology, have been either qualitative or of limited accuracy. Here we propose a pipeline that allows for a more accurate validation via co-registration with SIH maps, potentially allowing validation in a voxel-wise mode. CONCLUSION: This work demonstrates that methodically produced SIH maps facilitate the quantitative histopathologic assessment of imaging biomarkers.

Quantitative histopathologic assessment of perfusion MRI as a marker of glioblastoma cell infiltration in and beyond the peritumoral edema region.

BACKGROUND: Conventional MRI fails to detect regions of glioblastoma cell infiltration beyond the contrast-enhanced T1 solid tumor region, with infiltrating tumor cells often migrating along host blood vessels. PURPOSE: To quantitatively and qualitatively analyze the correlation between perfusion MRI signal and tumor cell density in order to assess whether local perfusion perturbation could provide a useful biomarker of glioblastoma cell infiltration. STUDY TYPE: Animal model. SUBJECTS: Mice bearing orthotopic glioblastoma xenografts generated from a patient-derived glioblastoma cell line. FIELD STRENGTH/SEQUENCES: 7T perfusion images acquired using a high signal-to-noise ratio (SNR) multiple boli arterial spin labeling sequence were compared with conventional MRI (T1 /T2 weighted, contrast-enhanced T1 , diffusion-weighted, and apparent diffusion coefficient). ASSESSMENT: Immunohistochemistry sections were stained for human leukocyte antigen (probing human-derived tumor cells). To achieve quantitative MRI-tissue comparison, multiple histological slices cut in the MRI plane were stacked to produce tumor cell density maps acting as a "ground truth." STATISTICAL TESTS: Sensitivity, specificity, accuracy, and Dice similarity indices were calculated and a two-tailed, paired t-test used for statistical analysis. RESULTS: High comparison test results (Dice 0.62-0.72, Accuracy 0.86-0.88, Sensitivity 0.51-0.7, and Specificity 0.92-0.97) indicate a good segmentation for all imaging modalities and highlight the quality of the MRI tissue assessment protocol. Perfusion imaging exhibits higher sensitivity (0.7) than conventional MRI (0.51-0.61). MRI/histology voxel-to-voxel comparison revealed a negative correlation between tumor cell infiltration and perfusion at the tumor margins (P = 0.0004). DATA CONCLUSION: These results demonstrate the ability of perfusion imaging to probe regions of low tumor cell infiltration while confirming the sensitivity limitations of conventional imaging modalities. The quantitative relationship between tumor cell density and perfusion identified in and beyond the edematous T2 hyperintensity region surrounding macroscopic tumor could be used to detect marginal tumor cell infiltration with greater accuracy. LEVEL OF EVIDENCE: 1 Technical stage: 2 J. Magn. Reson. Imaging 2019;50:529-540.