Recurrent glioblastoma: optimum area under the curve method derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging

Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment

OBJECTIVE

The purposes of this study were to evaluate diagnostic parameters measured with ultrafast MRI acquisition and with standard acquisition and to compare diagnostic utility for differentiating benign from malignant lesions.

MATERIALS AND METHODS

Ultrafast acquisition is a high-temporal-resolution (7 seconds) imaging technique for obtaining 3D whole-breast images. The dynamic contrast-enhanced 3-T MRI protocol consists of an unenhanced standard and an ultrafast acquisition that includes eight contrast-enhanced ultrafast images and four standard images. Retrospective assessment was performed for 60 patients with 33 malignant and 29 benign lesions. A computer-aided detection system was used to obtain initial enhancement rate and signal enhancement ratio (SER) by means of identification of a voxel showing the highest signal intensity in the first phase of standard imaging. From the same voxel, the enhancement rate at each time point of the ultrafast acquisition and the AUC of the kinetic curve from zero to each time point of ultrafast imaging were obtained.

RESULTS

There was a statistically significant difference between benign and malignant lesions in enhancement rate and kinetic AUC for ultrafast imaging and also in initial enhancement rate and SER for standard imaging. ROC analysis showed no significant differences between enhancement rate in ultrafast imaging and SER or initial enhancement rate in standard imaging.

CONCLUSION

Ultrafast imaging is useful for discriminating benign from malignant lesions. The differential utility of ultrafast imaging is comparable to that of standard kinetic assessment in a shorter study time.

Intravoxel Incoherent Motion MR Imaging in the Head and Neck: Correlation with Dynamic Contrast-Enhanced MR Imaging and Diffusion-Weighted Imaging

Objective

To investigate the correlation between perfusion- and diffusion-related parameters from intravoxel incoherent motion (IVIM) and those from dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted imaging in tumors and normal muscles of the head and neck.

Materials and Methods

We retrospectively enrolled 20 consecutive patients with head and neck tumors with MR imaging performed using a 3T MR scanner. Tissue diffusivity (D), pseudo-diffusion coefficient (D^*), and perfusion fraction (f) were derived from bi-exponential fitting of IVIM data obtained with 14 different b-values in three orthogonal directions. We investigated the correlation between D, f, and D^* and model-free parameters from the DCE-MRI (wash-in, T_max, E_max, initial AUC₆₀, whole AUC) and the apparent diffusion coefficient (ADC) value in the tumor and normal masseter muscle using a whole volume-of-interest approach. Pearson's correlation test was used for statistical analysis.

Results

No correlation was found between f or D^* and any of the parameters from the DCE-MRI in all patients or in patients with squamous cell carcinoma (p > 0.05). The ADC was significantly correlated with D values in the tumors (p < 0.001, r = 0.980) and muscles (p = 0.013, r = 0.542), despite its significantly higher value than D. The difference between ADC and D showed significant correlation with f values in the tumors (p = 0.017, r = 0.528) and muscles (p = 0.003, r = 0.630), but no correlation with D^* (p > 0.05, respectively).

Conclusion

Intravoxel incoherent motion shows no significant correlation with model-free perfusion parameters derived from the DCE-MRI but is feasible for the analysis of diffusivity in both tumors and normal muscles of the head and neck.

MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis

BACKGROUND

Distinction between tumor and treatment related changes is crucial for clinical management of patients with high-grade gliomas. Our purpose was to evaluate whether dynamic susceptibility contrast-enhanced (DSC) and dynamic contrast enhanced (DCE) perfusion-weighted imaging (PWI) metrics can effectively differentiate between recurrent tumor and posttreatment changes within the enhancing signal abnormality on conventional MRI.

METHODS

A comprehensive literature search was performed for studies evaluating PWI-based differentiation of recurrent tumor and posttreatment changes in patients with high-grade gliomas (World Health Organization grades III and IV). Only studies published in the "temozolomide era" beginning in 2005 were included. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

RESULTS

Of 1581 abstracts screened, 28 articles were included. The pooled sensitivities and specificities of each study's best performing parameter were 90% and 88% (95% CI: 0.85-0.94; 0.83-0.92) and 89% and 85% (95% CI: 0.78-0.96; 0.77-0.91) for DSC and DCE, respectively. The pooled sensitivities and specificities for detecting tumor recurrence using the 2 most commonly evaluated parameters, mean relative cerebral blood volume (rCBV) (threshold range, 0.9-2.15) and maximum rCBV (threshold range, 1.49-3.1), were 88% and 88% (95% CI: 0.81-0.94; 0.78-0.95) and 93% and 76% (95% CI: 0.86-0.98; 0.66-0.85), respectively.

CONCLUSIONS

PWI-derived thresholds separating viable tumor from treatment changes demonstrate relatively good accuracy in individual studies. However, because of significant variability in optimal reported thresholds and other limitations in the existing body of literature, further investigation and standardization is needed before implementing any particular quantitative PWI strategy across institutions.

A pilot study to determine the timing and effect of bevacizumab on vascular normalization of metastatic brain tumors in breast cancer

Background

To determine the appropriate time of concomitant chemotherapy administration after antiangiogenic treatment, we investigated the timing and effect of bevacizumab administration on vascular normalization of metastatic brain tumors in breast cancer patients.

Methods

Eight patients who participated in a phase II trial for breast cancer-induced refractory brain metastases were enrolled and subjected to 4 dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examinations that evaluated Peak, Slope, iAUC₆₀, and Ktrans before and after treatment. The treatment comprised bevacizumab on Day 1, etoposide on Days 2–4, and cisplatin on Day 2 in a 21-day cycle for a maximum of 6 cycles. DCE-MRI was performed before treatment and at 1 h, 24 h, and 21 days after bevacizumab administration.

Results

Values of the 4 DCE-MRI parameters reduced after bevacizumab administration. Compared with baseline values, the mean reductions at 1 and 24 h were −12.8 and −24.7 % for Peak, −46.6 and −65.8 % for Slope, −27.9 and −55.5 % for iAUC₆₀, and −46.6 and −63.9 % for Ktrans, respectively (all P < .05). The differences in the 1 and 24 h mean reductions were significant (all P < .05) for all the parameters. The generalized estimating equation linear regression analyses of the 4 DCE-MRI parameters revealed that vascular normalization peaked 24 h after bevacizumab administration.

Conclusion

Bevacizumab induced vascular normalization of brain metastases in humans at 1 and 24 h after administration, and the effect was significantly higher at 24 h than at 1 h.

Trial registration

ClinicalTrials.gov, identifier NCT01281696, registered prospectively on December 24, 2010

Value of Dynamic Contrast-Enhanced MRI to Detect Local Tumor Recurrence in Primary Head and Neck Cancer Patients

Treatment failures in head and neck cancer patients are mainly related to locoregional tumor recurrence. The objective of the present study was to evaluate the diagnostic accuracy of model-free dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to detect local recurrence during the surveillance of head and neck cancer patients.Our retrospective study enrolled 24 patients with primary head and neck cancer who had undergone definitive treatment. Patients were grouped into local recurrence (n = 12) or posttreatment change (n = 12) groups according to the results of biopsy or clinicoradiologic follow-up. The types of time-signal intensity (TSI) curves were classified as follows: "progressive increment" as type I, "plateau" as type II, and "washout" as type III. TSI curve types and their parameters (i.e., wash-in, Emax, Tmax, area under the curve [AUC]60, AUC90, and AUC120) were compared between the 2 study groups.The distributions of TSI curve types for local recurrence versus posttreatment change were statistically significant (P < 0.001) (i.e., 0% vs 83.3% for type I, 58.3% vs 16.7% for type II, and 41.7% vs 0% for type III). There were statistically significant differences in Emax, Tmax, and all of the AUC parameters between 2 groups (P < 0.0083 [0.05/6]). Receiver operating characteristic (ROC) curve analyses indicated that the TSI curve type was the best predictor of local recurrence with a sensitivity of 100% (95% CI, 73.5-100.0) and a specificity of 83.3% (95% CI, 51.6-97.9) (cutoff with type II).Model-free DCE-MRI using TSI curves and TSI curve-derived parameters detects local recurrence in head and neck cancer patients with a high diagnostic accuracy.

Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma

OBJECTIVES

To determine the added value of amide proton transfer (APT) imaging to conventional and perfusion MRI for differentiating tumour progression (TP) from the treatment-related effect (TE) in patients with post-treatment glioblastomas.

METHODS

Sixty-five consecutive patients with enlarging contrast-enhancing lesions following concurrent chemoradiotherapy were assessed using contrast-enhanced T1-weighted MRI (CE-T1WI), 90th percentile histogram parameters of normalized cerebral blood volume (nCBV90) and APT asymmetry value (APT90). Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUC) and cross validations.

RESULTS

There were statistically significant differences in the mean APT90 between the TP and the TE groups (3.87-4.01 % vs. 1.38-1.41 %; P < .001). Compared with CE-T1WI alone, the addition of APT90 to CE-T1WI significantly improved cross-validated AUC from 0.58-0.74 to 0.89-0.91 for differentiating TP from TE. The combination of CE-T1WI, nCBV90 and APT90 resulted in greater diagnostic accuracy for differentiating TP from TE than the combination of CE-T1WI and nCBV90 (cross-validated AUC, 0.95-0.97 vs. 0.84-0.91). The inter-reader agreement between the expert and trainee was excellent for the measurements of APT90 (intraclass correlation coefficient, 0.94).

CONCLUSION

Adding APT imaging to conventional and perfusion MRI improves the diagnostic performance for differentiating TP from TE.

KEY POINTS

• APT imaging could provide a reliable distinction between TP and TE • Adding APT imaging to CE-T1WI improved the diagnostic accuracy versus CE-T1WI alone • Multimodal imaging using CE-T1WI, perfusion and APT imaging led to accurate diagnosis • The inter-reader agreement of APT histogram parameters was excellent.

Tumor Vascular Permeability Pattern Is Associated With Complete Response in Immunocompetent Patients With Newly Diagnosed Primary Central Nervous System Lymphoma: Retrospective Cohort Study

A dynamic contrast-enhanced MR imaging (DCE-MRI) could provide the information about tumor drug delivery efficacy. We investigated the potential utility of the permeability pattern of DCE-MRI for predicting tumor response to high dose-methotrexate treatment and progression-free survival (PFS) in patients with primary CNS lymphoma (PCNSL). Clinical and conventional imaging parameters were assessed as potential predictors of tumor response in 48 immunocompetent PCNSL patients in a preliminary study. Fifty additional immunocompetent patients (27 men and 23 women; mean age, 60.6 years) with PCNSL underwent DCE-MRI before starting first-line treatment with high dose-methotrexate. The DCE-MRI pattern was categorized as diffuse or nondiffuse. After 4 courses of high dose methotrexate, patients underwent follow-up brain MR imaging to identify their complete response (CR). Predictors of CR and PFS were analyzed using clinical parameters, conventional MRI, and DCE-MRI. CR was noted in 20 (74.1%) of 27 patients with diffuse DCE-MRI pattern and in 4 (17.4%) of 23 patients with nondiffuse DCE-MRI pattern. The diffuse DCE-MRI pattern showed a significantly higher association with CR than the nondiffuse pattern (P < 0.001). Multivariate Cox proportional hazards model revealed that the DCE-MRI pattern (hazard ratio = 0.70; P = 0.045), age (hazard ratio = 1.47; P = 0.041), and adjuvant autologous stem-cell transplantation (hazard ratio = 6.97; P = 0.003) tended to be associated with a PFS. The pretreatment diffuse DCE-MRI pattern can be used as a potential imaging biomarker for predicting CR and a longer PFS in patients with newly diagnosed PCNSLs.

Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis

Purpose

This meta-analysis examined roles of several metabolites in differentiating recurrent tumor from necrosis in patients with brain tumors using MR perfusion and spectroscopy.

Methods

Medline, Cochrane, EMBASE, and Google Scholar were searched for studies using perfusion MRI and/or MR spectroscopy published up to March 4, 2015 which differentiated between recurrent tumor vs. necrosis in patients with primary brain tumors or brain metastasis. Only two-armed, prospective or retrospective studies were included. A meta-analysis was performed on the difference in relative cerebral blood volume (rCBV), ratios of choline/creatine (Cho/Cr) and/or choline/N-acetyl aspartate (Cho/NAA) between participants undergoing MRI evaluation. A χ²-based test of homogeneity was performed using Cochran’s Q statistic and I².

Results

Of 397 patients in 13 studies who were analyzed, the majority had tumor recurrence. As there was evidence of heterogeneity among 10 of the studies which used rCBV for evaluation (Q statistic = 31.634, I² = 97.11%, P < 0.0001) a random-effects analysis was applied. The pooled difference in means (2.18, 95%CI = 0.85 to 3.50) indicated that the average rCBV in a contrast-enhancing lesion was significantly higher in tumor recurrence compared with radiation injury (P = 0.001). Based on a fixed-effect model of analysis encompassing the six studies which used Cho/Cr ratios for evaluation (Q statistic = 8.388, I² = 40.39%, P = 0.137), the pooled difference in means (0.77, 95%CI = 0.57 to 0.98) of the average Cho/Cr ratio was significantly higher in tumor recurrence than in tumor necrosis (P = 0.001). There was significant difference in ratios of Cho to NAA between recurrent tumor and necrosis (1.02, 95%CI = 0.03 to 2.00, P = 0.044).

Conclusions

MR spectroscopy and MR perfusion using Cho/NAA and Cho/Cr ratios and rCBV may increase the accuracy of differentiating necrosis from recurrent tumor in patients with primary brain tumors or metastases.

Brain perfusion: computed tomography and magnetic resonance techniques

Cerebral perfusion imaging provides assessment of regional microvascular hemodynamics in the living brain, enabling in vivo measurement of a variety of different hemodynamic parameters. Perfusion imaging techniques that are used in the clinical setting usually rely upon X-ray computed tomography (CT) or magnetic resonance imaging (MRI). This chapter reviews CT- and MRI-based perfusion imaging techniques, with attention to image acquisition, clinically relevant aspects of image postprocessing, and fundamental differences between CT- and MRI-based techniques. Correlations with cerebrovascular physiology and potential clinical applications of perfusion imaging are reviewed, focusing upon the two major classes of neurologic disease in which perfusion imaging is most often performed: primary perfusion disorders (including ischemic stroke, transient ischemic attack, and reperfusion syndrome), and brain tumors.

Uninterpretable Dynamic Susceptibility Contrast-Enhanced Perfusion MR Images in Patients with Post-Treatment Glioblastomas: Cross-Validation of Alternative Imaging Options

Purpose

The purpose of this study was to evaluate the accuracy of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) perfusion MR imaging for distinguishing tumor recurrence from post-treatment effect as alternatives to dynamic-susceptibility contrast-enhanced (DSC) perfusion MR imaging when the DSC image is uninterpretable.

Materials and Methods

This retrospective study was approved by our institutional review board. Seventy one post-treatment glioblastoma patients who showed enlarged contrast-enhancing lesions on follow-up MR images after concurrent chemoradiotherapy and uninterpretable DSC images for corresponding enhancing lesions, underwent additional DWI and DCE MR imaging. The primary outcome was the frequency of interpretable DWI and DCE MR cases in these 71 patients. The secondary outcome was the area under the receiver operating characteristic curve (AUC) of DWI and DCE imaging parameters for distinguishing tumor recurrence from post-treatment effect in selected patients with interpretable DWI and DCE images. The imaging parameters were quantified as 10% cumulative histogram cutoff of apparent diffusion coefficient (ADC10) and 90% cumulative histogram cutoff of initial area under the time signal intensity curve (IAUC90). The AUCs were cross-validated by using leave-one-out method.

Results

Of the 71 patients, the uninterpretable DSC images were associated with treatment-related hemorrhage within the corresponding enhancing lesions (n = 54, 76.1%) and a near skull base location (n = 17, 23.9%). The frequencies of interpretable DWI and DCE image were 51 (71.8%) and 59 (83.1%) of the 71 cases with uninterpretable DSC images, respectively. Of the 45 selected patients with interpretable DWI and DCE images, the combination of DWI with DCE imaging showed a superior diagnostic performance than DWI or DCE imaging alone for differentiating tumor recurrence from post-treatment effect (cross-validated AUC: 0.78 versus 0.55 and 0.73 for reader 1; cross-validated AUC: 0.78 versus 0.53 and 0.75 for reader 2, respectively). Cross-validated accuracy of the single and combined imaging parameters also showed the highest for the combination of DWI with DCE MR imaging (72.9% for reader 1; 72.5% for reader 2) and the lowest for DWI alone (54.0% for reader 1; 56.4% for reader 2). Inter-reader agreement for DCE imaging was higher than that for DWI (intraclass correlation coefficient: 0.95 versus 0.87).

Conclusion

DCE MR imaging could be a superior and more reproducible imaging biomarker than DWI for differentiating tumor recurrence from post-treatment effect in patients with post-treatment glioblastoma when DSC MR images are not interpretable.

Pre- and Posttreatment Glioma: Comparison of Amide Proton Transfer Imaging with MR Spectroscopy for Biomarkers of Tumor Proliferation

PURPOSE

To correlate and compare diagnostic performance with amide proton transfer (APT) imaging as a tumor proliferation index with that with magnetic resonance (MR) spectroscopy in subgroups of patients with pre- and posttreatment glioma.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board. In 40 patients with pretreatment glioma and 25 patients with posttreatment glioma, correlation between APT asymmetry and the choline-to-creatine and choline-to-N-acetylaspartate ratios in corresponding voxels of interest was determined, and the 90% histogram cutoff of APT asymmetry values (APT90) for the entire solid portion of gliomas was calculated for diagnostic performance. Area under the receiver operating characteristic curve (AUC), leave-one-out cross validation, and intraclass correlation coefficients were analyzed.

RESULTS

The APT asymmetry values showed a moderate correlation (r = 0.49, P < .001) with the choline-to-creatine ratios and a mild correlation with the choline-to-N-acetyl-aspartate ratios (r = 0.32, P = .011) in the corresponding lesions. The APT90 showed comparable diagnostic accuracy for grading of gliomas (AUC, 0.81-0.84 vs 0.86; P = .582-.864) and superior accuracy for differentiation of tumor progression from treatment-related change (AUC, 0.89-0.90 vs 0.60; P = .031-.046) compared with those with MR spectroscopy. The cross-validated area under the curve and accuracy of the APT90 in posttreatment gliomas were 0.89-0.90 and 72%, respectively. The interreader agreement for APT90 was excellent in both pretreatment and posttreatment gliomas (intraclass correlation coefficient, 0.95 and 0.96, respectively).

CONCLUSION

APT imaging used as a tumor proliferation index showed moderate correlation with MR spectroscopic values and is a superior imaging method to MR spectroscopy, particularly for assessment of posttreatment gliomas.

Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma

Pseudoprogression may present as transient new or increasing enhancing lesions that mimic recurrent tumors in treated glioblastoma. The purpose of this study was to examine the utility of dynamic contrast enhanced T1 magnetic resonance imaging (DCE MRI) in differentiating between pseudoprogression and tumor progression and devise a cut-off value sensitive for pseudoprogression. We retrospectively examined 37 patients with glioblastoma treated with radiation and temozolomide after surgical resection that then developed new or increasing enhancing lesion(s) indeterminate for pseudoprogression versus progression. Volumetric plasma volume (Vp) and time-dependent leakage constant (Ktrans) maps were measured for the enhancing lesion and the mean and ninetieth percentile histogram values recorded. Lesion outcome was determined by clinical follow up with pseudoprogression defined as stable disease not requiring new treatment. Statistical analysis was performed with Wilcoxon rank-sum tests. Patients with pseudoprogression (n = 13) had Vp (mean) = 2.4 and Vp (90 %tile) = 3.2; and Ktrans (mean) = 3.5 and Ktrans (90 %tile) = 4.2. Patients with tumor progression (n = 24) had Vp (mean) = 5.3 and Vp (90 %tile) = 6.6; and Ktrans (mean) = 7.4 and Ktrans (90 %tile) = 9.1. Compared with tumor progression, pseudoprogression demonstrated lower Vp perfusion values (p = 0.0002) with a Vp (mean) cutoff <3.7 yielding 85% sensitivity and 79% specificity for pseudoprogression. Ktrans (mean) of >3.6 had a 69% sensitivity and 79% specificity for disease progression. DCE MRI shows lower plasma volume and time dependent leakage constant values in pseudoprogression than in tumor progression. A cut-off value with high sensitivity for pseudoprogression can be applied to aid in interpretation of DCE MRI.

Comparison of Glioblastomas and Brain Metastases using Dynamic Contrast-Enhanced Perfusion MRI

PURPOSE

To compare glioblastoma and brain metastases using T1-weighted dynamic contrast-enhanced (DCE)-MRI perfusion technique.

METHODS

26 patients with glioblastoma and 32 patients with metastatic brain lesions with no treatment who underwent DCE-MRI were, retrospectively, analyzed. DCE perfusion parameters K(trans) and Vp were calculated for the whole tumor. Signal intensity time curves were quantified by calculating the area under the curve (AUC) and the logarithmic slope of the washout phase to explore the heterogeneous tumor characteristics.

RESULTS

Glioblastoma did not differ from all brain metastases in K(trans) (P = .34) or Vp (P = .47). Glioblastoma and melanoma metastases differed from hypovascular metastases in AUC and log slope of the washout phase of the signal intensity time curve (P < .05); however, glioblastoma and melanoma metastases did not differ from each other (AUC: P = .78, Log slope: P = .77). Glioblastoma and melanoma metastases differed from hypovascular metastases in the ratio of Voxelneg /Voxelpos (P< .03); however, they did not differ from each other. Glioblastoma and melanoma metastases differed from each other in Voxelneg_threshold at higher negative log slope threshold.

CONCLUSION

DCE-MRI showed that it has a potential to differentiate glioblastomas, melanoma metastases and hypovascular brain tumors. Logarithmic slope of the washout phase and AUC of the signal intensity time curve were shown to be the best discriminator between hypervascular and hypovascular neoplasms.

Apparent diffusion coefficient parametric response mapping MRI for follow-up of glioblastoma

OBJECTIVES

To determine the diagnostic superiority of parametric response mapping of apparent diffusion coefficient (ADCPR) for predicting glioblastoma treatment response, compared to single time point measurement.

METHODS

Fifty post-treatment glioblastoma patients were enrolled. ADCPR was calculated from serial apparent diffusion coefficient (ADC) maps acquired before and at the time of first detection of an enlarged contrast-enhancing lesion on voxel-by-voxel basis. The percentage-decrease in ADCPR and tenth percentile histogram cutoff value of ADC (ADC10) were compared at subsequent 3-month and 1-year follow-ups.

RESULTS

The percentage-decrease in ADCPR was significantly higher in the progression group (mean = 33.2-38.3 %) than in the stable-response group (mean = 9.7 %) at 3 months follow-up (corrected p < 0.001 for both readers). ADCPR significantly improved area under the receiver operating characteristic curve from 0.67 to 0.88 (corrected p = 0.037) and from 0.70 to 0.92 (corrected p = 0.020) for both readers, respectively, compared to ADC10 at 3-month follow-up, but did not significantly improve at 1-year follow-up. The inter-reader agreement was higher for ADCPR than ADC10 (intraclass correlation coefficient, 0.93 versus 0.86).

CONCLUSION

Voxel-based ADCPR appears to be a superior imaging biomarker than ADC, particularly for predicting early tumour progression in patients with glioblastoma.

KEY POINTS

• Treatment response pattern of glioblastoma was evaluated using voxel-based ADCPR and ADC10. • Voxel-based ADCPR was more accurate in predicting treatment response pattern than ADC10. • Inter-reader agreement was higher in ADCPR calculation than in ADC10 calculation. • Voxel-based ADCPR can be a predictor of early treatment response pattern for glioblastoma.

Histogram Analysis of Amide Proton Transfer Imaging to Identify Contrast-enhancing Low-Grade Brain Tumor That Mimics High-Grade Tumor: Increased Accuracy of MR Perfusion

PURPOSE

To determine whether histogram analysis of amide proton transfer (APT) imaging provides increased accuracy of magnetic resonance (MR) perfusion imaging for the identification of contrast material-enhancing low-grade tumor (World Health Organization grades 1 and 2) that mimics high-grade tumor (World Health Organization grades 3 and 4).

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board. Forty-five patients with pathologically proven, solitary, contrast-enhancing tumors were enrolled in this study. APT-derived signal intensity from the calculated APT asymmetry at the offset frequency of 3.5 ppm and normalized cerebral blood volume (nCBV) were measured on solid portions of the tumor by using a 90% histogram cutoff (denoted as APT90 and nCBV90, respectively). The diagnostic performance of the imaging parameters was determined with leave-one-out cross validation. Interobserver agreement was assessed by using the intraclass correlation coefficient.

RESULTS

APT90 demonstrated a significant difference between contrast-enhancing low-grade and high-grade tumors for both readers (P < .001 for both readers). Compared with nCBV90, adding APT90 significantly improved the area under the receiver operating characteristic curve (AUC) for the identification of contrast-enhancing low-grade tumor from 0.80 to 0.97 for reader 1 (P = .023) and from 0.82 to 0.97 for reader 2 (P = .035), respectively. By using leave-one-out cross-validation, the cross-validated AUC of the combination of nCBV90 and APT90 was 0.95 for reader 1 and 0.96 for reader 2. The intraclass correlation coefficient for the APT90 calculations was 0.89.

CONCLUSION

Histogram analysis of APT imaging provided increased accuracy of MR perfusion imaging for the identification of contrast-enhancing low-grade tumor that mimics high-grade tumor.

ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain

MR perfusion imaging is becoming an increasingly common means of evaluating a variety of cerebral pathologies, including tumors and ischemia. In particular, there has been great interest in the use of MR perfusion imaging for both assessing brain tumor grade and for monitoring for tumor recurrence in previously treated patients. Of the various techniques devised for evaluating cerebral perfusion imaging, the dynamic susceptibility contrast method has been employed most widely among clinical MR imaging practitioners. However, when implementing DSC MR perfusion imaging in a contemporary radiology practice, a neuroradiologist is confronted with a large number of decisions. These include choices surrounding appropriate patient selection, scan-acquisition parameters, data-postprocessing methods, image interpretation, and reporting. Throughout the imaging literature, there is conflicting advice on these issues. In an effort to provide guidance to neuroradiologists struggling to implement DSC perfusion imaging in their MR imaging practice, the Clinical Practice Committee of the American Society of Functional Neuroradiology has provided the following recommendations. This guidance is based on review of the literature coupled with the practice experience of the authors. While the ASFNR acknowledges that alternate means of carrying out DSC perfusion imaging may yield clinically acceptable results, the following recommendations should provide a framework for achieving routine success in this complicated-but-rewarding aspect of neuroradiology MR imaging practice.

Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set

PURPOSE

To validate a volume-weighted voxel-based multiparametric clustering (VVMC) method for magnetic resonance imaging data that is designed to differentiate between pseudoprogression and early tumor progression (ETP) in patients with glioblastoma in an independent test set.

MATERIALS AND METHODS

This retrospective study was approved by the local institutional review board, with waiver of the need to obtain informed consent. The study patients were grouped chronologically into a training set (108 patients) and a test set (54 patients). The reference standard was pathologic findings or subsequent clinical-radiologic study results. By using the optimal cutoff determined in the training set, the diagnostic performance of VVMC was subsequently tested in the test set and was compared with that of single-parameter measurements (apparent diffusion coefficient [ADC], normalized cerebral blood volume [nCBV], and initial area under the time-signal intensity curve).

RESULTS

Interreader agreement was highest for VVMC (intraclass correlation coefficient, 0.87-0.89). Receiver operating characteristic curve analysis revealed that VVMC performed the best as a classifier, although statistical significance was not demonstrated with respect to the nCBV in the training set. In the test set, the diagnostic accuracy of VVMC was higher than that of any single-parameter measurements, but this trend reached significance only for the ADC. When the entire population was considered, VVMC had significantly better diagnostic accuracy than did any single parameter (P = .003-.046 for reader 1; P = .002-.016 for reader 2). Results of fivefold cross validation confirmed the trends in both the training set and the test set.

CONCLUSION

VVMC is a superior and more reproducible imaging biomarker than single-parameter measurements for differentiating between pseudoprogression and ETP in patients with glioblastoma. Online supplemental material is available for this article.

Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map

Conflicting results on differentiating edema and glioma by diffusion tensor imaging (DTI) are possibly attributable to dissimilar spatial distribution of the lesions. Combining DTI-parameters and enhanced registration might improve prediction. Regions of edema surrounding 22 metastases were compared to tumor-infiltrated regions from WHO grade 2 (12), 3 (10) and 4 (18) gliomas. DTI data was co-registered using Tract Based Spatial Statistics (TBSS), to measure Fractional Anisotropy (FA) and Mean Diffusivity (MD) for white matter only, and relative changes compared to matching reference regions (dFA and dMD). A two-factor principal component analysis (PCA) on metastasis and grade 2 glioma was performed to explore a possible differentiating combined factor. Edema demonstrated equal MD and higher FA compared to grade 2 and 3 glioma (P < 0.001), but did not differ from glioblastoma. Differences were non-significant when corrected for spatial distribution, since reference regions differed strongly (P < 0.001). The second component of the PCA (PCA-C2) did differentiate edema and low-grade tumor (sensitivity 91.7%, specificity 86.4%). PCA-C2 scores were plotted voxel-wise as a probability-map, discerning distinct areas of presumed edema or tumor infiltration. Correction of spatial dependency appears essential when differentiating glioma from edema. A tumor-infiltration probability-map is presented, based on supplementary information of multiple DTI parameters and spatial normalization.

Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility

PURPOSE

To compare the added value of dynamic contrast material-enhanced ( CE contrast enhanced ) ( DCE dynamic CE ) magnetic resonance (MR) imaging with that of dynamic susceptibility CE contrast enhanced ( DSC dynamic susceptibility CE ) MR imaging with the combination of CE contrast enhanced T1-weighted imaging and diffusion-weighted ( DW diffusion weighted ) imaging for predicting recurrent glioblastoma.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, with the requirement for informed patient consent waived. CE contrast enhanced T1-weighted images, DW diffusion weighted images, DSC dynamic susceptibility CE MR images, and DCE dynamic CE MR images in 169 patients with pathologically or clinicoradiologically diagnosed recurrent glioblastoma (n = 87) or radiation necrosis (n = 82) were retrospectively reviewed. Histogram cutoffs of quantitative parametric values were calculated from DW diffusion weighted images, DSC dynamic susceptibility CE MR images, and DCE dynamic CE MR images. Area under the receiver operating characteristic curve ( Az area under the ROC curve ) and interreader agreement were assessed.

RESULTS

For predicting recurrent glioblastoma, adding DCE dynamic CE MR imaging to the combination of CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging significantly improved Az area under the ROC curve from 0.84 to 0.96 for reader 1 and from 0.81 to 0.97 for reader 2, respectively. Adding DSC dynamic susceptibility CE MR imaging also significantly improved Az area under the ROC curve (0.95 for reader 1 and 0.93 for reader 2). However, there was no significant difference in Az between the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DSC dynamic susceptibility CE MR imaging and the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DCE dynamic CE MR imaging for both readers. The interreader agreement was highest for the combination of CE contrast enhanced T1-weighted imaging, DW diffusion weighted imaging, and DCE dynamic CE MR imaging (κ = 0.78) and lowest for CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging (κ = 0.65).

CONCLUSION

Adding perfusion MR imaging to the combination of CE contrast enhanced T1-weighted imaging and DW diffusion weighted imaging significantly improves the prediction of recurrent glioblastoma; however, selection of perfusion MR method does not affect the diagnostic performance.