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  • 1
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 19, No. 1 ( 2017-01-01), p. 128-137
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
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  • 2
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-1-17)
    Abstract: Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol. Methods The study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV & lt; 1.0), tumor with moderate angiogenesis (1.0 & lt; sRCBV & lt; 1.75), and tumor with high angiogenesis (sRCBV & gt; 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold. Results The mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%). Conclusions The optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
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  • 3
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 10, No. 11 ( 2015-11-24), p. e0141506-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2015
    detail.hit.zdb_id: 2267670-3
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 3748-3748
    Abstract: Purpose: This study uses spatially accurate image-guided tissue analysis to evaluate the correlation between Perfusion MRI (pMRI) and tissue microvascular density parameters as markers for tumor recurrence and treatment response in high-grade glioma (HGG). Methods: Following Institutional Review Board approval, we recruited previously treated (including chemo-radiation therapy) HGG patients undergoing surgical re-resection of new enhancing lesions on surveillance MRI. Preoperatively, we acquired pMRI and contrast-enhanced stereotactic T1-Weighted (T1W) MRI data sets. Intraoperatively, we recorded stereotactic locations of multiple biopsies based on T1W data sets that guided surgery. Following surgery, we coregistered pMRI and T1W data sets to calculate localized relative cerebral blood volume (rCBV) for each stereotactic specimen location. For each specimen, we performed 1) standard H & E staining to diagnose tumor recurrence or post-treatment radiation effect (PTRE); and 2) immunohistochemical analysis with CD34 to highlight tissue vessels. We analyzed CD-34 stained slides with Axiovision Automeasure 3.4 (Zeiss, Germany) to calculate both total microvessel number (MVN) and total microvessel area (MVA), each normalized to total slide specimen area (μm2). We calculated Pearson correlations to establish relationships between a) rCBV and MVN; and b) rCBV and MVA. We also performed t-test to compare MVN, MVA, and rCBV values between tumor and PTRE samples. A neuroradiologist performed all coregistration and pMRI calculations, and a neuropathologist analyzed all tissue specimens, without knowledge of corresponding data. A biostatistician performed all statistical comparisons. Results: In this preliminary study, we included 16 tissue specimens (from 8 subjects), each diagnosed as either tumor (n=7) or PTRE (n=9). We successfully calculated localized rCBV and determined both MVA and MVN for each specimen. The rCBV values showed highest correlation with total vessel area (MVA) (r=0.65, p=0.007) and slightly less correlation with vessel number (MVN) (r=0.52, p=0.04). Tumor showed significantly higher values than PTRE for all parameters: rCBV (1.87 + 0.82 versus 0.78 + 0.2, p=.002); MVA (0.22 + 0.03 versus 0.04 + 0.007, p=0.0001); and MVN (0.0068 + 0.001 versus. 0.0016 + 0.0006, p=0.0004). Conclusion: These preliminary results show the promise of Perfusion MRI to non-invasively estimate tissue microvessel density and distinguish tumor recurrence from treatment effects. The current study is ongoing to confirm results in a larger patient population. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3748.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2010
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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