Dynamic Contrast Enhanced T1-Weighted MRI (DCE-T1) using the contrast agent (CA) Gd-DTPA was performed on ten patients with glioblastoma (GBM). Nested models with as many as three parameters were employed to estimate plasma volume (vp), or vp and forward vascular transfer constant (Ktrans), or vp, Ktrans, and the reverse vascular transfer constant (kep). These constituted Models 1, 2, and 3, respectively. Model 1 predominated in normal non-leaky brain tissue, showing little or no leakage of CA. Model 3 predominated in regions associated with aggressive portions of the tumor, and Model 2 bordered Model 3 regions, showing leakage at reduced rates. In the patient sample, vp was about four times that of white matter in the enhancing part of the tumor. Ktrans varied by a factor of ten between the Model 2 (1.9 ×10−3 min−1) and Model 3 regions (1.9 ×10−2 min−1). The mean calculated interstitial space (Model 3) was 5.5%. In Model 3 regions, excellent curve fits were obtained to summarize concentration-time data (mean R2 = 0.99). We conclude that the three parameters of the Standard Model are sufficient to fit DCE-T1 data in GBM under the conditions of the experiment.

The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.

BACKGROUND AND PURPOSE:Tumor angiogenesis is very heterogeneous and in vivo correlation of perfusion imaging parameters with angiogenic markers can help in better understanding the role of perfusion imaging as an imaging biomarker. The purpose of this study was to correlate PCT parameters such as CBV and PS with histologic and molecular angiogenic markers in gliomas.

Differentiating treatment-induced necrosis (TIN) from recurrent/progressive tumor (RPT) in brain tumor patients using conventional morphologic imaging features is a very challenging task. Functional imaging techniques also offer moderate success due to the complexity of the tissue microenvironment and the inherent limitation of the various modalities and techniques. The purpose of this retrospective study was to assess the utility of nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion (DCET1MRP) in differentiating TIN from RPT. Twenty-nine patients with previously treated brain tumors who showed recurrent or progressive enhancing lesion on follow-up MRI underwent DCET1MRP. Another 8 patients with treatment-naive high-grade gliomas who also underwent DCET1MRP were included as the control group. Semiquantitative indices derived from DCET1MRP included maximum slope of enhancement in initial vascular phase (MSIVP), normalized MSIVP (nMSIVP), normalized slope of delayed equilibrium phase (nSDEP), and initial area under the time-intensity curve (IAUC) at 60 and 120 s (IAUC(60) and IAUC(120)) obtained from the enhancement curve. There was a statistically significant difference between the 2 groups (P < .01), with the RPT group showing higher MSIVP (15.78 vs 8.06), nMSIVP (0.046 vs 0.028), nIAUC(60) (33.07 vs 6.44), and nIAUC(120) (80.14 vs 65.55) compared with the TIN group. nSDEP was significantly lower in the RPT group (7.20 × 10(-5) vs 15.35 × 10(-5)) compared with the TIN group. Analysis of the receiver-operating-characteristic curve showed nMSIVP to be the best single predictor of RPT, with very high (95%) sensitivity and high (78%) specificity. Thus, nonmodel-based semiquantitative indices derived from DCET1MRP that are relatively easy to derive and do not require a complex model-based approach may aid in differentiating RPT from TIN and can be used as robust noninvasive imaging biomarkers.

BACKGROUND AND PURPOSE:Integration of imaging and genomic data is critical for a better understanding of gliomas, particularly considering the increasing focus on the use of imaging biomarkers for patient survival and treatment response. The purpose of this study was to correlate CBV and PS measured by using PCT with the genes regulating angiogenesis in GBM.

Purpose: To retrospectively correlate various diffusion tensor imaging (DTI) metrics in patients with glioblastoma multiforme (GBM) with patient survival analysis and also degree of tumor proliferation index determined histologically. Materials and Methods:Thirty-four patients with histologically confirmed treatment naive GBMs underwent DTI on a 3.0 Tesla (T) scanner. Region-of-interest was placed on the whole lesion including the enhancing as well as nonenhancing component of the lesion to determine the various DTI metrics. Kaplan-Meier estimates and Cox proportional hazards regression methods were used to assess the relationship of DTI metrics (minimum and mean values) and Ki-67 with progression free survival (PFS). To study the relationship between DTI metrics and Ki-67, Pearson's correlation coefficient was computed.Results: Univariate analysis showed that patients with fractional anisotropy (FA) mean 0.2, apparent diffusion coefficient (ADC) min 0.6, planar anisotropy (CP) min 0.002, spherical anisotropy (CS) mean > 0.68 and Ki-67 > 0.3 had lower PFS rate. The multivariate analysis demonstrated that only CP min was the best predictor of survival in these patients, after adjusting for age, Karnofsky performance scale and extent of resection. No significant correlation between DTI metrics and Ki-67 were observed.Conclusion: DTI metrics can be used as a sensitive and early indicator for PFS in patients with glioblastomas. This could be useful for treatment planning as high-grade gliomas with lower ADC min , FA mean , CP min , and higher CS mean values may be treated more aggressively.

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Brain tumor patients undergo various combinations therapies, leading to very complex and confusing imaging appearances on follow up MR imaging and hence, differentiating recurrent or progressing tumors from treatment induced necrosis or effects has always been a challenge in neuro-oncologic imaging. This particular topic has become more relevant these days because of the advent of newer anti-angiogenic and anti-neoplastic chemotherapeutic agents as well as use of salvage radiation therapy. Various clinically available functional imaging modalities can provide additional physiologic and metabolic information about the tumors which could be useful in identifying viable tumor from treatment induced necrosis and hence, can guide treatment planning. In this review we will discuss various functional neuro-imaging modalities, their advantages and limitations and also their utility in treatment planning.