John P. Karis

Relative Cerebral Blood Volume Values to Differentiate High-Grade Glioma Recurrence from Posttreatment Radiation Effect: Direct Correlation between Image-Guided Tissue Histopathology and Localized Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging Measurements

BACKGROUND AND PURPOSE: Differentiating tumor growth from posttreatment radiation effect (PTRE) remains a common problem in neuro-oncology practice. To our knowledge, useful threshold relative cerebral blood volume (rCBV) values that accurately distinguish the 2 entities do not exist. Our prospective study uses image-guided neuronavigation during surgical resection of MR imaging lesions to correlate directly specimen histopathology with localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC) measurements and to establish accurate rCBV threshold values, which differentiate PTRE from tumor recurrence. MATERIALS AND METHODS: Preoperative 3T gradient-echo DSC and contrast-enhanced stereotactic T1-weighted images were obtained in patients with high-grade glioma (HGG) previously treated with multimodality therapy. Intraoperative neuronavigation documented the stereotactic location of multiple tissue specimens taken randomly from the periphery of enhancing MR imaging lesions. Coregistration of DSC and stereotactic images enabled calculation of localized rCBV within the previously recorded specimen locations. All tissue specimens were histopathologically categorized as tumor or PTRE and were correlated with corresponding rCBV values. All rCBV values were T1-weighted leakage-corrected with preload contrast-bolus administration and T2/T2*-weighted leakage-corrected with baseline subtraction integration. RESULTS: Forty tissue specimens were collected from 13 subjects. The PTRE group (n = 16) rCBV values ranged from 0.21 to 0.71, tumor (n = 24) values ranged from 0.55 to 4.64, and 8.3% of tumor rCBV values fell within the PTRE group range. A threshold value of 0.71 optimized differentiation of the histopathologic groups with a sensitivity of 91.7% and a specificity of 100%. CONCLUSIONS: rCBV measurements obtained by using DSC and the protocol we have described can differentiate HGG recurrence from PTRE with a high degree of accuracy.

Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

INTRODUCTION: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)–based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. METHODS: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRI–fractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. RESULTS: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r = 0.82; P < .0001), which was the only imaging metric that correlated with OS (P<.02). CONCLUSION: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.

Bilateral Temporal Hypometabolism in Epilepsy

Summary: Purpose: Positron emission tomography (PET) has proven useful in epilepsy surgery for its ability to identify unilateral temporal hypometabolism (UTH), which is predictive of good surgical outcome. The significance of bilateral temporal hypometabolism (BTH) is not known.

Correlations between Perfusion MR Imaging Cerebral Blood Volume, Microvessel Quantification, and Clinical Outcome Using Stereotactic Analysis in Recurrent High-Grade Glioma

BACKGROUND AND PURPOSE: Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific “glomeruloid” vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG. MATERIALS AND METHODS: We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression. RESULTS: We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17). CONCLUSIONS: rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.

MR Imaging of Papillary Tumor of the Pineal Region

SUMMARY: We report the imaging features of 4 cases of patients with papillary tumor of the pineal region, a tumor newly recognized in the 2007 World Health Organization “Classification of Tumors of the Nervous System.” In each case, the tumor was intrinsically hyperintense on T1-weighted images with a characteristic location in the posterior commissure or pineal region. The pathologic hallmarks of the tumor are discussed, including a possible explanation for the MR imaging characteristics in our cases.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Preliminary Experience with 3-Tesla MRI and Cushing's Disease

Because radiographic visualization of a pituitary microadenoma is frequently difficult, we hypothesized that microadenomas associated with Cushings disease may be better resolved and localized via acquisition with 3-Tesla (3T) compared with standard 1.5-Tesla (1.5T) magnetic resonance imaging (MRI). Five patients (four females, one male; age range, 14 to 50 years old) with endocrine and clinical confirmation of Cushings disease underwent 1.5T and 3T MRI and corticotropin-releasing hormone stimulation/inferior petrosal sinus sampling (IPSS) as part of their preoperative evaluation. All patients underwent a transnasal trans-sphenoidal pituitary adenomectomy. In two cases, tumor could not be localized on either 1.5T or 3T MRI on the initial radiologists review. In two other cases, the 1.5T images delineated the tumor location, but it was more clearly defined on 3T MRI. In a fifth case, the 1.5T MRI showed a probable right-sided adenoma. However, on both 3T MRI and at surgical exploration the tumor was localized on the left side. Therefore, in three of five cases, 3T MRI either more clearly defined tumors seen on 1.5T MRI or predicted the location of tumor contrary to the 1.5T images. IPSS identified the correct side of the tumor in two patients, an incorrect location in two patients, and was indeterminate in one patient. In certain cases 3T MRI is a new tool that may ameliorate imaging difficulties associated with adrenocorticotrophic hormone-secreting pituitary adenomas. Its role in the diagnostic evaluation of Cushings disease will be better defined with further experience.

Revised motion estimation algorithm for PROPELLER MRI.

To introduce a new algorithm for estimating data shifts (used for both rotation and translation estimates) for motion‐corrected PROPELLER MRI. The method estimates shifts for all blades jointly, emphasizing blade‐pair correlations that are both strong and more robust to noise.

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

Background 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. Methods 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. Results 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). Conclusion Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Turboprop IDEAL: a motion-resistant fat-water separation technique.

Suppression of the fat signal in MRI is very important for many clinical applications. Multi‐point water–fat separation methods, such as IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least‐squares estimation), can robustly separate water and fat signal, but inevitably increase scan time, making separated images more easily affected by patient motions. PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) and Turboprop techniques offer an effective approach to correct for motion artifacts. By combining these techniques together, we demonstrate that the new TP‐IDEAL method can provide reliable water–fat separation with robust motion correction. The Turboprop sequence was modified to acquire source images, and motion correction algorithms were adjusted to assure the registration between different echo images. Theoretical calculations were performed to predict the optimal shift and spacing of the gradient echoes. Phantom images were acquired, and results were compared with regular FSE‐IDEAL. Both T1‐ and T2‐weighted images of the human brain were used to demonstrate the effectiveness of motion correction. TP‐IDEAL images were also acquired for pelvis, knee, and foot, showing great potential of this technique for general clinical applications. Magn Reson Med 61:188–195, 2009.