Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps

A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade

AIM

To investigate the ability of arterial spin labelling (ASL) perfusion parameters to distinguish high-grade from low-grade gliomas.

MATERIALS AND METHODS

The PubMed and EMBASE databases were systematically searched for relevant articles published up to September 2015. Studies that evaluated both high- and low-grade gliomas using ASL were included. The random effect model was used to calculate the standardised mean difference (SMD) of maximum mean absolute tumour blood flow values (aTBF_max, aTBF_mean) and maximum mean relative tumour blood flow (rTBF_max, rTBF_mean) between high- and low-grade gliomas.

RESULTS

Nine studies encompassing 305 patients with high- and low-grade gliomas, met all inclusion and exclusion criteria and were included in the study. Compared with low-grade gliomas, high-grade gliomas had a significant increase in all ASL perfusion values: aTBF_max (SMD=0.70, 95% confidence interval [CI]: 0.22-1.19, p=0.0046); aTBF_mean (SMD=0.86, 95% CI: 0.2-1.52, p=0.01); rTBF_max (SMD=1.08, 95% CI: 0.54-1.63, p=0.0001) and rTBF_mean (SMD=0.88, 95% CI: 0.35-1.4, p=0.0011).

CONCLUSIONS

The current study results indicate that tumour blood flow from ASL differs significantly with respect to the glioma grade. Despite some limitations, there is evidence that ASL may be useful to distinguish high- and low-grade gliomas. Further larger-scale studies are necessary to examine the utility of ASL to distinguish tumour grade.

Diffusion, Perfusion, and Histopathologic Characteristics of Desmoplastic Infantile Ganglioglioma

We present a case series of a rare tumor, the desmoplastic infantile ganglioglioma (DIG) with MRI diffusion and perfusion imaging quantification as well as histopathologic characterization. Four cases with pathologically-proven DIG had diffusion weighted imaging (DWI) and two of the four had dynamic susceptibility contrast imaging. All four tumors demonstrate DWI findings compatible with low-grade pediatric tumors. For the two cases with perfusion imaging, a higher relative cerebral blood volume was associated with higher proliferation index on histopathology for one of the cases. Our results are discussed in conjunction with a literature review.

Application of Dynamic Contrast-Enhanced MRI Parameters for Differentiating Squamous Cell Carcinoma and Malignant Lymphoma of the Oropharynx

OBJECTIVE

The purpose of this study was to investigate the usefulness of histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) parameters for the differentiation of squamous cell carcinoma (SCC) and malignant lymphoma of the oropharynx.

MATERIALS AND METHODS

Pretreatment DCE-MRI was performed in 21 patients with pathologically confirmed oropharyngeal SCC and six patients with malignant lymphoma. DCE-MRI parameter maps including the volume transfer constant (K(trans)), flux rate constant (kep), and extravascular extracellular volume fraction (ve) based on the Tofts model were obtained. Enhancing tumors were manually segmented on each slice of the parameter maps, and the data were collected to obtain a histogram for the entire tumor volume. The Wilcoxon rank sum test was used to compare the histogram parameters of each DCE-MRI-derived variable of oropharyngeal SCC and lymphoma.

RESULTS

Histogram analysis of K(trans) and ve maps revealed that the median and mode of K(trans) were significantly higher in SCC than in lymphoma (p = 0.039 and 0.032, respectively), and the mode, skewness, and kurtosis of ve were significantly different in SCC than in lymphoma (p = 0.046, 0.039, and 0.032, respectively). On ROC analysis, the kurtosis of ve had the best discriminative value for distinguishing between oropharyngeal SCC and lymphoma (AUC, 0.865; cutoff value, 2.60; sensitivity, 83.3%; specificity, 90.5%).

CONCLUSION

Our preliminary evidence using histogram analysis of DCE-MRI parameters based on the whole tumor volume suggests that it might be useful for differentiating SCC from malignant lymphoma of the oropharynx.

Vascular Magnetic Resonance Imaging in Brain Tumors During Antiangiogenic Therapy--Are We There Yet?

Abnormal tumor vasculature is a potent mediator of treatment resistance because it results in heterogeneous perfusion, hypoxia, increased interstitial fluid pressure, and incomplete penetration of cytotoxic chemotherapies. Targeting this abnormal tumor vasculature is a promising therapeutic strategy, but results with antiangiogenic drugs in brain cancer have been mixed. Vasculature's response to treatment is a dynamic physiological process that can change rapidly throughout treatment, so it requires noninvasive techniques to serially monitor these changes in order to improve outcome. We review the role of vascular magnetic resonance imaging to measure tumor response to treatment and highlight opportunities and future avenues for expanding these promising techniques.

Quantitative analyses of T2-weighted MRI as a potential marker for response to somatostatin analogs in newly diagnosed acromegaly

In growth hormone (GH)-producing adenomas, T2-weighted MRI signal intensity is a marker for granulation pattern and response to somatostatin analogs (SSA). Prediction of treatment response is necessary for individualized treatment, and T2 intensity assessment might improve preoperative classification of somatotropinomas. The objectives of this study are (I) to explore the feasibility of quantitative T2-weighted MRI histogram analyses in newly diagnosed somatotroph adenomas and their relation to clinical and histological parameters and (II) to compare the quantitative method to conventional, visual assessment of T2 intensity. The study was a retrospective cohort study of 58 newly diagnosed patients. In 34 of these, response to primary SSA treatment after median 6 months was evaluated. Parameters from the T2 histogram analyses (T2 intensity ratio and T2 homogeneity ratio) were correlated to visually assessed T2 intensity (hypo-, iso-, hyperintense), baseline characteristics, response to SSA treatment, and histological granulation pattern (anti-Cam5.2). T2 intensity ratio was lowest in the hypointense tumors and highest in the hyperintense tumors (0.66 ± 0.10 vs. 1.07 ± 0.11; p < 0.001). T2 intensity at baseline correlated with reduction in GH (r = -0.67; p < 0.001) and IGF-1 (r = -0.36; p = 0.037) after primary SSA treatment (n = 34). The T2 homogeneity ratio correlated with adenoma size reduction (r = -0.45; p = 0.008). Sparsely granulated adenomas had a higher T2 intensity than densely or intermediately granulated adenomas. T2 histogram analyses are an applicable tool to assess T2 intensity in somatotroph adenomas. Quantitatively assessed T2 intensity ratio in GH-producing adenomas correlates with conventional assessment of T2 intensity, baseline characteristics, response to SSA treatment, and histological granulation pattern.

Quantitative Evaluation for Differentiating Malignant and Benign Thyroid Nodules Using Histogram Analysis of Grayscale Sonograms

OBJECTIVES

To evaluate the diagnostic value of histogram analysis using grayscale sonograms for differentiation of malignant and benign thyroid nodules.

METHODS

From July 2013 through October 2013, 579 nodules in 563 patients who had undergone ultrasound-guided fine-needle aspiration were included. For the grayscale histogram analysis, pixel echogenicity values in regions of interest were measured as 0 to 255 (0, black; 255, white) with in-house software. Five parameters (mean, skewness, kurtosis, standard deviation, and entropy) were obtained for each thyroid nodule. With principal component analysis, an index was derived. Diagnostic performance rates for the 5 histogram parameters and the principal component analysis index were calculated.

RESULTS

A total of 563 patients were included in the study (mean age ± SD, 50.3 ± 12.3 years;range, 15-79 years). Of the 579 nodules, 431 were benign, and 148 were malignant. Among the 5 parameters and the principal component analysis index, the standard deviation (75.546 ± 14.153 versus 62.761 ± 16.01; P < .001), kurtosis (3.898 ± 2.652 versus 6.251 ± 9.102; P < .001), entropy (0.16 ± 0.135 versus 0.239 ± 0.185; P < .001), and principal component analysis index (-0.386±0.774 versus 0.134 ± 0.889; P < .001) were significantly different between the malignant and benign nodules. With the calculated cutoff values, the areas under the curve were 0.681 (95% confidence interval, 0.643-0.721) for standard deviation, 0.661 (0.620-0.703) for principal component analysis index, 0.651 (0.607-0.691) for kurtosis, 0.638 (0.596-0.681) for entropy, and 0.606 (0.563-0.647) for skewness. The subjective analysis of grayscale sonograms by radiologists alone showed an area under the curve of 0.861 (0.833-0.888).

CONCLUSIONS

Grayscale histogram analysis was feasible for differentiating malignant and benign thyroid nodules but did not show better diagnostic performance than subjective analysis performed by radiologists. Further technical advances will be needed to objectify interpretations of thyroid grayscale sonograms.

Pre-Operative Perfusion Skewness and Kurtosis Are Potential Predictors of Progression-Free Survival after Partial Resection of Newly Diagnosed Glioblastoma

Objective

To determine whether pre-operative perfusion skewness and kurtosis derived from normalized cerebral blood volume (nCBV) histograms are associated with progression-free survival (PFS) of patients after partial resection of newly diagnosed glioblastoma.

Materials and Methods

A total of 135 glioblastoma patients who had undergone partial resection of tumor (resection of < 50% of pre-operative tumor volume or surgical biopsy) confirmed with immediate postsurgical MRI and examined with both conventional MRI and dynamic susceptibility contrast (DSC) perfusion MRI before the surgery were retrospectively reviewed in this study. They had been followed up post-surgical chemoradiotherapy for tumor progression. Using histogram analyses of nCBV derived from pre-operative DSC perfusion MRI, patients were sub-classified into the following four groups: positive skewness and leptokurtosis (group 1); positive skewness and platykurtosis (group 2); negative skewness and leptokurtosis (group 3); negative skewness and platykurtosis (group 4). Kaplan-Meier analysis and multivariable Cox proportional hazards regression analysis were performed to determine whether clinical and imaging covariates were associated with PFS or overall survival (OS) of these patients.

Results

According to the Kaplan-Meier method, median PFS of group 1, 2, 3, and 4 was 62, 51, 39, and 41 weeks, respectively, with median OS of 82, 77, 77, and 72 weeks, respectively. In multivariable analyses with Cox proportional hazards regression, pre-operative skewness/kurtosis pattern (hazard ratio: 2.98 to 4.64; p < 0.001), Karnofsky performance scale score (hazard ratio: 1.04; p = 0.003), and post-operative tumor volume (hazard ratio: 1.04; p = 0.02) were independently associated with PFS but not with OS.

Conclusion

Higher skewness and kurtosis of nCBV histogram before surgery were associated with longer PFS in patients with newly diagnosed glioblastoma after partial tumor resection.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas.

MATERIALS AND METHODS

This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis.

RESULTS

Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters.

CONCLUSIONS

In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV.

Comparison of the Effect of Vessel Size Imaging and Cerebral Blood Volume Derived from Perfusion MR Imaging on Glioma Grading

BACKGROUND AND PURPOSE

Vascular proliferation is a major criterion for grading gliomas on the basis of histology. Relative cerebral blood volume can provide pathophysiologic information about glioma grading. Vessel size imaging, in some animals, can be used to estimate the microvascular caliber of a glioma, but its clinical use remains unclear. Herein, we aimed to compare the predictive power of relative cerebral blood volume and vessel size imaging in glioma grading, with grading based on histology.

MATERIALS AND METHODS

Seventy patients with glioma participated in the study; 30 patients underwent MR perfusion imaging with a spin-echo sequence and vessel size imaging with a gradient-echo and spin-echo sequence successively at 24-hour intervals before surgery. We analyzed the vessel size imaging values and relative cerebral blood volume of differently graded gliomas. The microvessel parameters were histologically evaluated and compared with those on MR imaging. The cutoff values of vessel size imaging and relative cerebral blood volume obtained from receiver operating characteristic curve analyses were used to predict glioma grading in another 40 patients.

RESULTS

Vessel size imaging values and relative cerebral blood volume were both increased in high-grade gliomas compared with low-grade gliomas (P < .01). Moreover, vessel size imaging values had higher specificity and sensitivity in differentiating high-grade from low-grade gliomas compared with relative cerebral blood volume. In addition, a significant correlation was observed between vessel size imaging values and microvessel diameters (r > 0.8, P < .05) and between relative cerebral blood volume and microvessel area (r = 0.6579, P < .05). Most important, the use of vessel size imaging cutoff values to predict glioma grading was more accurate (100%) than use of relative cerebral blood volume (85%) values.

CONCLUSIONS

Vessel size imaging can provide more accurate information on glioma grading and may serve as an effective biomarker for the prognosis of patients with gliomas.

Spatial distribution of malignant tissue in gliomas: correlations of 11C-L-methionine positron emission tomography and perfusion- and diffusion-weighted magnetic resonance imaging

BACKGROUND

The prognosis of glioma patients is contingent on precise target selection for stereotactic biopsies and the extent of tumor resection. (11)C-L-methionine (MET) positron emission tomography (PET) demonstrates tumor heterogeneity and invasion with high diagnostic accuracy.

PURPOSE

To compare the spatial tumor distribution delineated by MET PET with that by perfusion- and diffusion-weighted magnetic resonance imaging (MRI), in order to understand the diagnostic value of these MRI methods, when PET is not available.

MATERIAL AND METHODS

Presurgical MET PET and MRI, including perfusion- and diffusion-weighted MRI, were acquired in 13 patients (7 high-grade gliomas, 6 low-grade gliomas). A quantitative volume of interest analysis was performed to compare the modalities objectively, supplemented by a qualitative evaluation that assessed the clinical applicability.

RESULTS

The inaccuracy of conventional MRI was confirmed (area under the curve for predicting voxels with high MET uptake = 0.657), whereas cerebral blood volume (CBV) maps calculated from perfusion data improved accuracy (area under the curve = 0.760). We considered CBV maps diagnostically comparable to MET PET in 5/7 cases of high-grade gliomas, but insufficient in all cases of low-grade gliomas when evaluated subjectively. Cerebral blood flow and apparent diffusion coefficient maps did not contribute to further accuracy.

CONCLUSION

Adding perfusion-weighted MRI to the presurgical protocol can increase the diagnostic accuracy of conventional MRI and is a simple and well-established method compared to MET PET. However, the definition of low-grade gliomas with subtle or no alterations on cerebral blood volume maps remains a diagnostic challenge for stand-alone MRI.

Preoperative biomarkers of tumour vascularity are elevated in patients with glioblastoma multiforme

We investigated the correlation between the circulating and imaging biomarkers of tumour vascularity, and examined whether they are prognostic of outcomes in patients with glioblastoma multiforme (GBM). Despite the increasing use of anti-angiogenic agents within neuro-oncology, there are still no validated biomarkers to monitor for a treatment response or relapse. The pre- and postoperative circulating endothelial cell (CEC) and progenitor cell (CEP) levels were assessed. Preoperative perfusion-weighted MRI (PWI) was also performed, and the relative cerebral blood volume (rCBV) histogram statistics of the contrast-enhancing tumour were analysed. A novel PWI parameter (rCBVload) was developed to estimate the total volume of perfused tumour vessels, and it was hypothesised that this parameter would correlate with CEC and CEP concentrations. In total, 24 GBM patients were included. The mean preoperative CEC concentration was significantly higher in GBM patients than the controls (p=0.019), and it then declined significantly postoperatively (p=0.009). The preoperative CEP levels were significantly correlated with the median tumour rCBV (Spearman rank-order coefficient=0.526; p=0.039). Neither CEC nor CEP was correlated with the total tumour vessel volume, as measured by rCBVload. None of the biomarkers that were investigated showed a significant correlation with progression-free or overall survival. We conclude that CEC are potentially useful biomarkers to monitor GBM patients during treatment. We found that CEC are increased in the presence of GBM, and that CEP levels appear to be proportional to tumour vascularity, as measured on PWI. However, in this study, none of the biomarkers of GBM vascularity were highly prognostic of patient outcomes.

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.

Perfusion MRI derived indices of microvascular shunting and flow control correlate with tumor grade and outcome in patients with cerebral glioma

Objectives

Deficient microvascular blood flow control is thought to cause tumor hypoxia and increase resistance to therapy. In glioma patients, we tested whether perfusion-weighted MRI (PWI) based indices of microvascular flow control provide more information on tumor grade and patient outcome than does the established PWI angiogenesis marker, cerebral blood volume (CBV).

Material and Methods

Seventy-two glioma patients (sixty high-grade, twelve low-grade gliomas) were included. Capillary transit time heterogeneity (CTH) and the coefficient of variation (COV), its ratio to blood mean transit time, provide indices of microvascular flow control and the extent to which oxygen can be extracted by tumor tissue. The ability of these parameters and CBV to differentiate tumor grade were assessed by receiver operating characteristic curves and logistic regression. Their ability to predict time to progression and overall survival was examined by the Cox proportional-hazards regression model, and by survival curves using log-rank tests.

Results

The best prediction of grade (AUC = 0.876; p < 0.05) was achieved by combining knowledge of CBV and CTH in the enhancing tumor and peri-focal edema, and patients with glioblastoma multiforme were identified best by CTH (AUC = 0.763; p<0.001). CTH outperformed CBV and COV in predicting time to progression and survival in all gliomas and in a subgroup consisting of only high-grade gliomas.

Conclusion

Our study confirms the importance of microvascular flow control in tumor growth by demonstrating that determining CTH improves tumor grading and outcome prediction in glioma patients compared to CBV alone.

CT perfusion with acetazolamide challenge in C6 gliomas and angiogenesis

Background

This study was performed to investigate the correlation between CT perfusion with acetazolamide challenge and angiogenesis in C6 gliomas.

Methods

Thirty-two male Sprague-Dawley rats were evaluated. The rats were divided randomly to four groups: eight rats with orthotopically implanted C6 gliomas at 10-days old (Group A), eight rats with gliomas at 14-days old (Group B), eight rats with gliomas at 18-days old (Group C), eight rats with orthotopically injected normal saline served as controls. CT perfusion was performed before and after administration of acetazolamide. Changes in perfusion parameters due to acetazolamide administration were calculated and analyzed.

Results

Elevated carbon dioxide partial pressure and decreased pH were found in all 32 rats post acetazolamide challenge (P<0.01). Cerebral blood flow_{pre-challenge} was increased in group C (95.0±2.5 ml/100g/min), as compared to group B (80.1±11.3 ml/100g/min) and group A (63.1±2.1 ml/100g/min). Cerebral blood flow percentage changes were detected with a reduction in group C (54.2±4.8%) as compared to controls (111.3±22.2%). Cerebral blood volume _{pre-challenge} was increased in group C (50.8±1.7ml/100g), as compared to group B (45.7±1.9 ml/100g) and group A (38.2±0.8 ml/100g). Cerebral blood volume percentage changes were decreased in group C (23.5±4.6%) as compared to controls (113.5±30.4%). Angiogenesis ratio = [(CD105-MVD) / (FVIII-MVD)] ×100%. Positive correlations were observed between CD105-microvessel density, angiogenesis ratio, vascular endothelial growth factor, proliferation marker and cerebral blood flow_{pre-challenge}, cerebral blood volume _{pre-challenge}. Negative correlations were observed between CD105-microvessel density and cerebral blood flow percentage changes (P<0.01, correlation coefficient r=-0.788), cerebral blood volume percentage changes (P<0.01, r=-0.703). Negative correlations were observed between angiogenesis ratio, vascular endothelial growth factor, proliferation marker and cerebral blood flow percentage changes, cerebral blood volume percentage changes.

Conclusion

Our findings suggest that CT perfusion with challenge can provide new insight into non-invasive assessment of rat C6 glioma angiogenesis.

High- and low-grade glioma differentiation: the role of percentage signal recovery evaluation in MR dynamic susceptibility contrast imaging

PURPOSE

Evaluation of cerebral blood volume (CBV) with magnetic resonance (MR) imaging can differentiate low-grade from high-grade gliomas. The percentage of signal recovery (PSR) in the venous phase of perfusion curves is inversely proportional to blood-brain barrier (BBB) permeability. Since even BBB permeability relates to glioma malignancy grade, we carried out a comparative evaluation between CBV and PSR to characterise cerebral gliomas.

MATERIALS AND METHODS

Forty-nine patients with cerebral gliomas were studied with MR perfusion imaging. In all tumours, both maximum CBV and minimum PSR were calculated. The difference between the CBV and PSR mean values among the low-grade and high-grade gliomas was assessed using statistical methods. We also examined whether there was an additional difference between low-grade and grade III gliomas. Finally, CBV and PSR diagnostic sensitivity and specificity in identifying low-grade gliomas compared to all gliomas and low-grade gliomas compared to all gliomas excluding glioblastomas was assessed.

RESULTS

A significant difference between low-grade and high-grade gliomas with both CBV and PSR was demonstrated. Conversely, there was a significant difference between low-grade and grade III gliomas only with PSR, while CBV did not show significant difference. Finally, superior sensitivity and specificity of PSR compared to CBV in identifying low-grade gliomas was demonstrated both compared to all gliomas and all gliomas excluding glioblastomas.

CONCLUSION

The PSR evaluation proved better than CBV for determining the grade of brain and is therefore a useful tool to be considered in the MR evaluation of gliomas.

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.

A generic support vector machine model for preoperative glioma survival associations

PURPOSE

To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of this model in autonomous patient data.

MATERIALS AND METHODS

Institutional and regional medical ethics committees approved the study, and all patients signed a consent form. Two hundred thirty-five preoperative adult patients from two institutions with a subsequent histologically confirmed diagnosis of glioma after surgery were included retrospectively. An SVM learning technique was applied to MR imaging-based whole-tumor relative cerebral blood volume (rCBV) histograms. SVM models with the highest diagnostic accuracy for 6-month and 1-, 2-, and 3-year survival associations were trained on 101 patients from the first institution. With Cox survival analysis, the diagnostic effectiveness of the SVM models was tested on independent data from 134 patients at the second institution.

RESULTS

were adjusted for known survival predictors, including patient age, tumor size, neurologic status, and postsurgery treatment, and were compared with survival associations from an expert reader.

RESULTS

Compared with total qualitative assessment by an expert reader, the whole-tumor rCBV-based SVM model was the strongest parameter associated with 6-month and 1-, 2-, and 3-year survival in the independent patient data (area under the receiver operating characteristic curve, 0.794-0.851; hazard ratio, 5.4-21.2).

DISCUSSION

Machine learning by means of SVM in combination with whole-tumor rCBV histogram analysis can be used to identify early patient survival in aggressive gliomas. The SVM model returned higher diagnostic accuracy values than an expert reader, and the model appears to be insensitive to patient, observer, and institutional variations.

Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review

BACKGROUND

Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice.

METHODOLOGY

The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared.

PRINCIPAL FINDINGS

Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description.

CONCLUSIONS

In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.

Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity

BACKGROUND AND PURPOSE

To apply a texture analysis of apparent diffusion coefficient (ADC) maps to evaluate glioma heterogeneity, which was correlated with tumor grade.

MATERIALS AND METHODS

Forty patients with glioma (WHO grade II (n = 8), grade III (n = 10) and grade IV (n = 22)) underwent diffusion-weighted imaging (DWI), and the corresponding ADC maps were obtained. Regions of interest containing the lesions were drawn on every section of the ADC map containing the tumor, and volume-based data of the entire tumor were constructed. Texture and first order features including entropy, skewness and kurtosis were derived from the ADC map using in-house software. A histogram analysis of the ADC map was also performed. The texture and histogram parameters were compared between low-grade and high-grade gliomas using an unpaired student's t-test. Additionally, a one-way analysis of variance analysis with a post-hoc test was performed to compare the parameters of each grade.

RESULTS

Entropy was observed to be significantly higher in high-grade gliomas than low-grade tumors (6.861±0.539 vs. 6.261±0.412, P  = 0.006). The fifth percentiles of the ADC cumulative histogram also showed a significant difference between high and low grade gliomas (836±235 vs. 1030±185, P = 0.037). Only entropy proved to be significantly different between grades III and IV (6.295±0.4963 vs. 7.119±0.3165, P<0.001). The diagnostic accuracy of ADC entropy was significantly higher than that of the fifth percentile of the ADC histogram (P = 0.0034) in distinguishing high- from low-grade glioma.

CONCLUSION

A texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.

Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging

The purpose of our study was to explore the difference between isocitrate dehydrogenase (IDH)-1/2 gene mutation-positive and -negative high-grade gliomas (HGGs) using histogram analysis of apparent diffusion coefficient (ADC) and normalized cerebral blood volume (nCBV) maps. We enrolled 52 patients with histopathologically confirmed HGGs with IDH1/2 (P) (n = 16) or IDH1/2 (N) (n = 36). Histogram parameters of ADC and nCBV maps were correlated with gene mutations by using the unpaired student's t test and multivariable stepwise logistic regression analysis. The mean ADC value was higher in the IDH1 (P) group than IDH1 (N) (1,282.8 vs. 1,159.6 mm(2)/s, P = .0113). In terms of the cumulative ADC histograms, the 10th and 50th percentile values were also higher in the IDH1 (P) than IDH1 (N) (P = .0104 and .0183, respectively). We observed a higher 90th percentile value (3.121 vs. 2.397, P = .0208) and a steeper slope between the 10th (C10) and 90th (C90) of cumulative nCBV histograms (0.03386 vs. 0.02425/%, P = .0067) in the IDH1 (N) group. Multivariate analysis showed that the mean ADC mean value (P = .0048), the C90 value (P = .0113), and the slope between C10 and C90 (P = .0049) were the significant variables in the differentiation of IDH1 (P) from IDH1 (N). In conclusion, histogram analysis of ADC and nCBV maps based on entire tumor volume can be a useful tool for distinguishing IDH1 (P) and IDH1 (N), and it predicts that IDH (P) tumors have a more heterogeneous microenvironment than IDH (N) ones.

Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index

PURPOSE

To evaluate the correlation between apparent diffusion coefficient ( ADC apparent diffusion coefficient ) values and the Ki-67 labeling index for luminal-type (estrogen receptor-positive) breast cancer not otherwise specified ( NOS not otherwise specified ) diagnosed by means of biopsy.

MATERIALS AND METHODS

The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Between December 2009 and December 2012, 86 patients with 86 lesions with luminal-type invasive breast cancer NOS not otherwise specified underwent magnetic resonance imaging, including dynamic contrast material-enhanced imaging and diffusion-weighted imaging with b values of 0 and 1000 sec/mm(2). Conventional measurement of the minimum and mean ADC apparent diffusion coefficient s by placing regions of interest and histogram analysis of pixel-based ADC apparent diffusion coefficient data of the entire tumor were performed by two observers independently and correlated with the Ki-67 labeling index of surgical specimens.

RESULTS

For the interobserver reliability, interclass correlation coefficients for all parameters with the exception of the minimum ADC apparent diffusion coefficient exceeded 0.8, indicating almost perfect agreement. The minimum ADC apparent diffusion coefficient and mean ADC apparent diffusion coefficient and the 25th, 50th, and 75th percentiles of the histograms showed negative correlations with the Ki-67 labeling index (r = -0.49, -0.55, -0.54, -0.53, and -0.48, respectively). Receiver operating characteristic curve analysis for the differential diagnosis between the high-proliferation (Ki-67 ≥ 14; n = 44) and low-proliferation (Ki-67 < 14; n = 42) groups revealed that the most effective threshold for the mean ADC apparent diffusion coefficient was lower than 1097 × 10(-6) mm(2)/sec, with sensitivity and specificity of 82% and 71%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.81 for the mean ADC apparent diffusion coefficient . There were no significant differences in the AUC among the parameters.

CONCLUSION

Considering convenience for routine practice, the authors suggest that the mean ADC apparent diffusion coefficient of the conventional method would be practical to use for estimating the Ki-67 labeling index.

Intraoperative perfusion magnetic resonance imaging: Cutting-edge improvement in neurosurgical procedures

The goal in brain tumor surgery is to remove the maximum achievable amount of the tumor, preventing damage to “eloquent” brain regions as the amount of brain tumor resection is one of the prognostic factors for time to tumor progression and median survival. To achieve this goal, a variety of technical advances have been introduced, including an operating microscope in the late 1950s, computer-assisted devices for surgical navigation and more recently, intraoperative imaging to incorporate and correct for brain shift during the resection of the lesion. However, surgically induced contrast enhancement along the rim of the resection cavity hampers interpretation of these intraoperatively acquired magnetic resonance images. To overcome this uncertainty, perfusion techniques [dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI)] have been introduced that can differentiate residual tumor from surgically induced changes at the rim of the resection cavity and thus overcome this remaining uncertainty of intraoperative MRI in high grade brain tumor resection.

Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma

Imaging heterogeneous cancer lesions is a real challenge. For diagnosis, histology often remains the reference, but it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in establishing a link between clinical in vivo imaging and the biologic properties of tissues. In this study, we propose to construct histology-resembling images based on tissue microvascularization, a magnetic resonance imaging (MRI) accessible source of contrast. To integrate the large amount of information collected with microvascular MRI, we combined a manual delineation of a spatial region of interest with an unsupervised, model-based cluster analysis (Mclust). This approach was applied to two rat models of glioma (C6 and F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissular oxygen saturation, and cerebral metabolic rate of oxygen. Five clusters, defined by their MRI features, were found to correspond to specific histologic features, and revealed intratumoral spatial structures. These results suggest that the presence of a cluster within a tumor can be used to assess the presence of a tissue type. In addition, the cluster composition, i.e., a signature of the intratumoral structure, could be used to characterize tumor models as histology does.

Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis

BACKGROUND AND PURPOSE

The multiparametric imaging can show us different aspects of tumor behavior and may help differentiation of tumor recurrence from treatment related change. Our aim was to differentiate tumor progression from pseudoprogression in patients with glioblastoma by using multiparametric histogram analysis of 2 consecutive MR imaging studies with relative cerebral blood volume and ADC values.

MATERIALS AND METHODS

Thirty-five consecutive patients with glioblastoma with new or increased size of enhancing lesions after concomitant chemoradiation therapy following surgical resection were included. Combined histograms were made by using the relative cerebral blood volume and ADC values of enhancing areas for initial and follow-up MR imaging, and subtracted histograms were also prepared. The histogram parameters between groups were compared. The diagnostic accuracy of tumor progression based on the histogram parameters of initial and follow-up MR imaging and subtracted histograms was compared and correlated with overall survival.

RESULTS

Twenty-four pseudoprogressions and 11 tumor progressions were determined. Diagnosis based on the subtracted histogram mode with a multiparametric approach was more accurate than the diagnosis based on the uniparametric approach (area under the receiver operating characteristic curve of 0.877 versus 0.801), with 81.8% sensitivity and 100% specificity. A high mode of relative cerebral blood volume on the subtracted histogram by using a multiparametric approach (relative cerebral blood volume ×ADC) was the best predictor of true tumor progression (P < .001) and worse survival (P = .003).

CONCLUSIONS

Multiparametric histogram analysis of posttreatment glioblastoma was useful to predict true tumor progression and worse survival.

Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors

PET using O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) provides important diagnostic information in addition to that from conventional MR imaging on tumor extent and activity of cerebral gliomas. Recent studies suggest that perfusion-weighted MR imaging (PWI), especially maps of regional cerebral blood volume (rCBV), may provide similar diagnostic information. In this study, we directly compared (18)F-FET PET and PWI in patients with brain tumors.

METHODS

Fifty-six patients with gliomas were investigated using static (18)F-FET PET and PWI. For comparison, 8 patients with meningiomas were included. We generated a set of tumor and reference volumes of interest (VOIs) based on morphologic MR imaging and transferred these VOIs to the corresponding (18)F-FET PET scans and PWI maps. From these VOIs, tumor-to-brain ratios (TBR) were calculated, and normalized histograms were generated for (18)F-FET PET and rCBV maps. Furthermore, in rCBV maps and in (18)F-FET PET scans, tumor volumes, their spatial congruence, and the distance between the local hot spots were assessed.

RESULTS

For patients with glioma, TBR was significantly higher in (18)F-FET PET than in rCBV maps (TBR, 2.28 ± 0.99 vs. 1.62 ± 1.13; P < 0.001). Histogram analysis of the VOIs revealed that (18)F-FET scans could clearly separate tumor from background. In contrast, deriving this information from rCBV maps was difficult. Tumor volumes were significantly larger in (18)F-FET PET than in rCBV maps (tumor volume, 24.3 ± 26.5 cm(3) vs. 8.9 ± 13.9 cm(3); P < 0.001). Accordingly, spatial overlap of both imaging parameters was poor (congruence, 11.0%), and mean distance between the local hot spots was 25.4 ± 16.1 mm. In meningioma patients, TBR was higher in rCBV maps than in (18)F-FET PET (TBR, 5.33 ± 2.63 vs. 2.37 ± 0.32; P < 0.001) whereas tumor volumes were comparable.

CONCLUSION

In patients with cerebral glioma, tumor imaging with (18)F-FET PET and rCBV yields different information. (18)F-FET PET shows considerably higher TBRs and larger tumor volumes than rCBV maps. The spatial congruence of both parameters is poor. The locations of the local hot spots differ considerably. Taken together, our data show that metabolically active tumor tissue of gliomas as depicted by amino acid PET is not reflected by rCBV as measured with PWI.

Cerebral blood volume calculated by dynamic susceptibility contrast-enhanced perfusion MR imaging: preliminary correlation study with glioblastoma genetic profiles

Purpose

To evaluate the usefulness of dynamic susceptibility contrast (DSC) enhanced perfusion MR imaging in predicting major genetic alterations in glioblastomas.

Materials and Methods

Twenty-five patients (M:F = 13∶12, mean age: 52.1±15.2 years) with pathologically proven glioblastoma who underwent DSC MR imaging before surgery were included. On DSC MR imaging, the normalized relative tumor blood volume (nTBV) of the enhancing solid portion of each tumor was calculated by using dedicated software (Nordic TumorEX, NordicNeuroLab, Bergen, Norway) that enabled semi-automatic segmentation for each tumor. Five major glioblastoma genetic alterations (epidermal growth factor receptor (EGFR), phosphatase and tensin homologue (PTEN), Ki-67, O6-methylguanine-DNA methyltransferase (MGMT) and p53) were confirmed by immunohistochemistry and analyzed for correlation with the nTBV of each tumor. Statistical analysis was performed using the unpaired Student t test, ROC (receiver operating characteristic) curve analysis and Pearson correlation analysis.

Results

The nTBVs of the MGMT methylation-negative group (mean 9.5±7.5) were significantly higher than those of the MGMT methylation-positive group (mean 5.4±1.8) (p = .046). In the analysis of EGFR expression-positive group, the nTBVs of the subgroup with loss of PTEN gene expression (mean: 10.3±8.1) were also significantly higher than those of the subgroup without loss of PTEN gene expression (mean: 5.6±2.3) (p = .046). Ki-67 labeling index indicated significant positive correlation with the nTBV of the tumor (p = .01).

Conclusion

We found that glioblastomas with aggressive genetic alterations tended to have a high nTBV in the present study. Thus, we believe that DSC-enhanced perfusion MR imaging could be helpful in predicting genetic alterations that are crucial in predicting the prognosis of and selecting tailored treatment for glioblastoma patients.

Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods

Purpose

To compare the reproducibilities of manual and semiautomatic segmentation method for the measurement of normalized cerebral blood volume (nCBV) using dynamic susceptibility contrast-enhanced (DSC) perfusion MR imaging in glioblastomas.

Materials and Methods

Twenty-two patients (11 male, 11 female; 27 tumors) with histologically confirmed glioblastoma (WHO grade IV) were examined with conventional MR imaging and DSC imaging at 3T before surgery or biopsy. Then nCBV (means and standard deviations) in each mass was measured using two DSC MR perfusion analysis methods including manual and semiautomatic segmentation method, in which contrast-enhanced (CE)-T1WI and T2WI were used as structural imaging. Intraobserver and interobserver reproducibility were assessed according to each perfusion analysis method or each structural imaging. Interclass correlation coefficient (ICC), Bland-Altman plot, and coefficient of variation (CV) were used to evaluate reproducibility.

Results

Intraobserver reproducibilities on CE-T1WI and T2WI were ICC of 0.74–0.89 and CV of 20.39–36.83% in manual segmentation method, and ICC of 0.95–0.99 and CV of 8.53–16.19% in semiautomatic segmentation method, repectively. Interobserver reproducibilites on CE-T1WI and T2WI were ICC of 0.86–0.94 and CV of 19.67–35.15% in manual segmentation method, and ICC of 0.74–1.0 and CV of 5.48–49.38% in semiautomatic segmentation method, respectively. Bland-Altman plots showed a good correlation with ICC or CV in each method. The semiautomatic segmentation method showed higher intraobserver and interobserver reproducibilities at CE-T1WI-based study than other methods.

Conclusion

The best reproducibility was found using the semiautomatic segmentation method based on CE-T1WI for structural imaging in the measurement of the nCBV of glioblastomas.

¹⁸F-FDG PET metabolic parameters and MRI perfusion and diffusion parameters in hepatocellular carcinoma: a preliminary study

Objectives

Glucose metabolism, perfusion, and water diffusion may have a relationship or affect each other in the same tumor. The understanding of their relationship could expand the knowledge of tumor characteristics and contribute to the field of oncologic imaging. The purpose of this study was to evaluate the relationships between metabolism, vasculature and cellularity of advanced hepatocellular carcinoma (HCC), using multimodality imaging such as ¹⁸F-FDG positron emission tomography (PET), dynamic contrast enhanced (DCE)-MRI, and diffusion weighted imaging(DWI).

Materials and Methods

Twenty-one patients with advanced HCC underwent ¹⁸F-FDG PET, DCE-MRI, and DWI before treatment. Maximum standard uptake values (SUV_max) from ¹⁸F-FDG-PET, variables of the volume transfer constant (K^trans) from DCE-MRI and apparent diffusion coefficient (ADC) from DWI were obtained for the tumor and their relationships were examined by Spearman’s correlation analysis. The influence of portal vein thrombosis on SUV_max and variables of K^trans and ADC was evaluated by Mann-Whitney test.

Results

SUV_max showed significant negative correlation with K^trans_max (ρ = −0.622, p = 0.002). However, variables of ADC showed no relationship with variables of K^trans or SUV_max (p>0.05). Whether portal vein thrombosis was present or not did not influence the SUV _max and variables of ADC and K^trans (p>0.05).

Conclusion

In this study, SUV was shown to be correlated with K_trans in advanced HCCs; the higher the glucose metabolism a tumor had, the lower the perfusion it had, which might help in guiding target therapy.

Quantitative MR perfusion parameters related to survival time in high-grade gliomas

OBJECTIVES

To evaluate the quantitative parameters obtained from dynamic MR T2*-weighted images as predictors of survival taking into consideration the biasing effects of other survival-related covariates.

METHODS

Thirty-nine patients (60 ± 14 years; survival 267 ± 191 days) with high-grade gliomas (8 grade III, 31 grade IV) were retrospectively included in the study. Additional data incorporated Karnofsky performance scale, tumour resection extension after surgery and type of treatment. Dynamic T2*-weighted MRI was acquired before treatment. Tumour curves were extracted for each voxel, and several quantitative parameters were obtained from the whole tumour volume and the 10 % maximum values. Additional image covariates included the presence of necrosis, single or multiple lesions, and tumour and oedema volumes. The relationship between quantitative parameters and survival was assessed using clusterisation techniques and the log-rank method. Cox regression analysis was used to evaluate each parameter's predictive value.

RESULTS

Only the mean of the 10 % maximum values of the transfer coefficient showed an independent relationship with patient survival (log-rank chi-squared test <0.001, Cox regression P = 0.015), with higher values corresponding to lower survival rates.

CONCLUSIONS

High maximum transfer coefficient values show an independent statistical relationship with low survival in high-grade glioma patients. This imaging biomarker can be used as a predictor of prognosis.

Gliomas: application of cumulative histogram analysis of normalized cerebral blood volume on 3 T MRI to tumor grading

Background

Glioma grading assumes significant importance in that low- and high-grade gliomas display different prognoses and are treated with dissimilar therapeutic strategies. The objective of our study was to retrospectively assess the usefulness of a cumulative normalized cerebral blood volume (nCBV) histogram for glioma grading based on 3 T MRI.

Methods

From February 2010 to April 2012, 63 patients with astrocytic tumors underwent 3 T MRI with dynamic susceptibility contrast perfusion-weighted imaging. Regions of interest containing the entire tumor volume were drawn on every section of the co-registered relative CBV (rCBV) maps and T2-weighted images. The percentile values from the cumulative nCBV histograms and the other histogram parameters were correlated with tumor grades. Cochran’s Q test and the McNemar test were used to compare the diagnostic accuracies of the histogram parameters after the receiver operating characteristic curve analysis. Using the parameter offering the highest diagnostic accuracy, a validation process was performed with an independent test set of nine patients.

Results

The 99th percentile of the cumulative nCBV histogram (nCBV C99), mean and peak height differed significantly between low- and high-grade gliomas (P = <0.001, 0.014 and <0.001, respectively) and between grade III and IV gliomas (P = <0.001, 0.001 and <0.001, respectively). The diagnostic accuracy of nCBV C99 was significantly higher than that of the mean nCBV (P = 0.016) in distinguishing high- from low-grade gliomas and was comparable to that of the peak height (P = 1.000). Validation using the two cutoff values of nCBV C99 achieved a diagnostic accuracy of 66.7% (6/9) for the separation of all three glioma grades.

Conclusion

Cumulative histogram analysis of nCBV using 3 T MRI can be a useful method for preoperative glioma grading. The nCBV C99 value is helpful in distinguishing high- from low-grade gliomas and grade IV from III gliomas.

Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging

BACKGROUND

Pseudoprogression is a treatment-related reaction with an increase in contrast-enhancing lesion size, followed by subsequent improvement. Differentiating tumor recurrence from pseudoprogression remains a problem in neuro-oncology.

PURPOSE

To validate the added value of arterial spin labeling (ASL), compared with dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) alone, in distinguishing early tumor progression from pseudoprogression in patients with newly diagnosed glioblastoma multiforme (GBM).

MATERIAL AND METHODS

We retrospectively evaluated 117 consecutive patients with newly diagnosed GBM who underwent surgical resection and concurrent chemoradiotherapy (CCRT) as standard treatment modality. Sixty-two patients who developed contrast-enhancing lesions were assessed by both ASL and DSC perfusion MRI and classified into groups of early tumor recurrence (n = 34) or pseudoprogression (n = 28) based on pathologic analysis or clinical-radiologic follow-up. We used a qualitative analysis and semi-quantitative grade system on the basis of the tumor perfusion signal intensity into those equal to white matter (grade I), gray matter (grade II), and blood vessels (grade III) on ASL imaging. ASL grade was correlated with histogram parameters derived from DSC perfusion MRI.

RESULTS

Pseudoprogression was observed in 15 (53.6%) patients with ASL grade I, 13 (46.4%) with grade II, and 0 (0%) with grade III, with early tumor progression observed in seven (20.6%) patients with ASL grade I, 11 (32.3%) with grade II, and 16 (47.1%) with grade III (P = 0.0022). DSC perfusion histogram parameters differed significantly among ASL grades. ASL grade was an independent predictor differentiating pseudoprogression from early tumor progression (odds ratio, 4.73; P = 0.0017). On qualitative review, adjunctive ASL produced eight (12.9%) more accurate results than DSC perfusion MRI alone.

CONCLUSION

ASL improves the diagnostic accuracy of DSC perfusion MRI in differentiating pseudoprogression from early tumor progression.

Intravascular contrast agent T2* relaxivity in brain tissue

Dynamic susceptibility-weighted contrast-enhanced (DSC) MRI perfusion measurements depend on estimating intravascular contrast agent (CA) concentrations (C) from signal intensity changes in T2*-weighted images after bolus injection. Generally, linearity is assumed between relaxation and C, but previous studies have shown that compartmentalization of CA and secondary magnetic field perturbations generate deviations from linearity. Physical phantoms using bulk blood have been used to empirically determine the relationship between relaxation rate and C in large vessels. However, the relaxivity of CA in the microvasculature is not easily measured since constructing appropriate phantoms is difficult. Instead, theoretical relaxivity models have been developed. In this study, we empirically tested a non-linear expression based on static dephasing regime (SDR) and linear approximation. Signal-time curves in white (WM) and grey matter (GM) were converted to concentration time curves (CTCs) using both expressions. Parameters for both linear and non-linear formulations were adjusted to give a best agreement between cerebral blood volumes (CBV) calculated from WM and arterial CTCs in a group of normal subjects scanned at 3T. Optimized parameters were used to calculate blood volume in WM and GM in healthy subjects scanned at 3T and in meningioma patients scanned at 1.5T. Results from this study showed that a non-linear SDR formulation gave an acceptable functional form for tissue relaxivity, giving reliable CBV estimates at different field strengths and echo times.

Atypical imaging features of Epstein-Barr virus-positive primary central nervous system lymphomas in patients without AIDS

BACKGROUND AND PURPOSE

Recent clinical experience with EBV-positive PCNSL in patients without acquired immune deficiency syndrome showed that they tended to have atypical features seen on conventional MR imaging. The purpose of our study was to evaluate the MR imaging features of EBV-positive PCNSL in patients without AIDS and to compare these imaging findings with those of EBV-negative PCNSL.

MATERIALS AND METHODS

MR images were obtained in 55 consecutive patients with pathologically proved EBV-positive (n = 10) or EBV-negative (n = 45) PCNSL. We statistically analyzed the differences between the patient groups regarding the occurrence of tumor necrosis or hemorrhage and ADC, rCBVmax, rCBVr, and the Cho/NAA ratio in the tumor area.

RESULTS

Tumor necrosis and hemorrhage were observed in 9 (90%) and 7 (70%), respectively, of the patients with EBV-positive PCNSL; necrosis was observed in 8 (18%), and hemorrhage, in 3 (7%) patients with EBV-negative PCNSL (P < .0001 each). The necrotic core was hyperintense relative to contralateral white matter, as seen on DWI in 4 patients with EBV-positive PCNSL, though the ADC between the 2 patient groups did not differ significantly. rCBVmax, rCBVr, and the Cho/NAA ratios did not differ significantly between the 2 groups. The sensitivity and specificity of necrosis and hemorrhage for differentiating the 2 groups were 89.2% and 81.7% and 78.5% and 94.1%, respectively.

CONCLUSIONS

Our initial clinical experience with a small number of patients suggests that EBV-positive PCNSL in patients without AIDS tends to present with atypical MR imaging features.

Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements

OBJECTIVE

Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator.

METHODS

MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade.

RESULTS

A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis.

CONCLUSION

Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator.

ADVANCES IN KNOWLEDGE

Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

PURPOSE

Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data.

MATERIALS AND METHODS

Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics.

RESULTS

Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology.

CONCLUSION

The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results

PURPOSE

To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs).

MATERIALS AND METHODS

DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations.

RESULTS

There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters.

CONCLUSION

Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.

Histogram analysis of apparent diffusion coefficient map of standard and high B-value diffusion MR imaging in head and neck squamous cell carcinoma: a correlation study with histological grade

RATIONALE AND OBJECTIVES

A histologic grade in head and neck squamous cell carcinoma (HNSCC) is clinically important because of its association with prognosis. The purpose of this study was to investigate the efficacy of histographic analysis of apparent diffusion coefficient (ADC) maps on the basis of the entire tumor volume in differentiating histologic grades in HNSCC at standard (b = 1000 s/mm(2)) and high (b = 2000 s/mm(2)) b values.

MATERIALS AND METHODS

Fifty-four patients with HNSCC, including well-differentiated (WD; n = 35), moderately differentiated (MD; n = 13) and poorly differentiated (PD; n = 6) carcinomas, were retrospectively evaluated. ADC maps were obtained at two different b values (1000 and 2000 s/mm(2)) in each patient. Tumors were delineated on each slice of ADC maps, and data were collected to obtain a histogram for the entire tumor volume. Histographic parameters were calculated, including mean, standard deviation, kurtosis, skewness, and the ratio of the kurtosis measured at b values of 1000 and 2000 s/mm(2). These parameters were correlated with histologic grades.

RESULTS

There was no significant correlation between tumor grades and histographic parameters obtained from ADC maps at b = 1000 s/mm(2). However, mean ADC at b = 2000 s/mm(2) was significantly higher in WD HNSCC (881 ± 131 × 10(-6) mm(2)/s) than in MD and PD HNSCC (770 ± 163 and 780 ± 158 × 10(-6) mm(2)/s, respectively) (P < .05). Kurtosis ratio was significantly higher in PD HNSCC (115 ± 10%) compared to WD and MD HNSCC (91 ± 21% and 86 ± 26%, respectively) (P < .05). Diagnostic accuracy was 100%, 76.9%, and 65.8% for PD, MD, and WD HNSCC, respectively.

CONCLUSIONS

Histographic analysis of ADC maps on the basis of the entire tumor volume can be useful in differentiating histologic grades of HNSCC using mean ADC at b = 2000 s/mm(2) and kurtosis ratio.

Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas

PURPOSE

To test the predictive value of skewness and kurtosis changes of normalized cerebral blood volume (nCBV) during the early treatment period for differentiating early tumor progression from pseudoprogression in patients with newly diagnosed glioblastomas.

MATERIALS AND METHODS

The institutional review board approved this retrospective study. The authors assessed 135 patients with newly diagnosed glioblastomas who underwent concurrent chemotherapy and radiation therapy (CCRT) after surgical resection. Patients who developed new or enlarged contrast material-enhanced lesions after CCRT were assessed by means of conventional and perfusion magnetic resonance (MR) imaging. The percent change of skewness and kurtosis on nCBV histograms between the first and second post-CCRT follow-up were classified into four categories. Independent predictors of early tumor progression were determined by means of logistic regression analysis.

RESULTS

Of 135 patients, 79 had new or enlarged contrast-enhanced lesions after CCRT, subsequently classified as early tumor progression (n = 42, 53.2%) and pseudoprogression (n = 37, 46.8%). Pseudoprogression was observed in 23 of 24 (95.8%) patients in category 1, 10 of 15 (66.7%) in category 2, four of 20 (20.0%) in category 3, and 0 of 20 (0%) in category 4 (χ(2) test, P < .0001). The histographic pattern of nCBV was the best independent predictor (odds ratio, 3.51; P = .0032) for early tumor progression, rather than each percent change of skewness or kurtosis; the histographic pattern of nCBV represented the largest area under the receiver operating characteristic curve (0.934; 95% confidence interval: 0.855, 0.977), with a sensitivity of 85.7% and a specificity of 89.2%.

CONCLUSION

The percent change of skewness and kurtosis of nCBV may be a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas.

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.

SVM-based glioma grading: Optimization by feature reduction analysis

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.

¹⁸F-Fluoromisonidazole positron emission tomography may differentiate glioblastoma multiforme from less malignant gliomas

PURPOSE

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor and its prognosis is significantly poorer than those of less malignant gliomas. Pathologically, necrosis is one of the most important characteristics that differentiate GBM from lower grade gliomas; therefore, we hypothesized that (18)F fluoromisonidazole (FMISO), a radiotracer for hypoxia imaging, accumulates in GBM but not in lower grade gliomas. We aimed to evaluate the diagnostic value of FMISO positron emission tomography (PET) for the differential diagnosis of GBM from lower grade gliomas.

METHODS

This prospective study included 23 patients with pathologically confirmed gliomas. All of the patients underwent FMISO PET and (18)F-fluorodeoxyglucose (FDG) PET within a week. FMISO images were acquired 4 h after intravenous administration of 400 MBq of FMISO. Tracer uptake in the tumor was visually assessed. Lesion to normal tissue ratios and FMISO uptake volume were calculated.

RESULTS

Of the 23 glioma patients, 14 were diagnosed as having GBM (grade IV glioma in the 2007 WHO classification), and the others were diagnosed as having non-GBM (5 grade III and 4 grade II). In visual assessment, all GBM patients showed FMISO uptake in the tumor greater than that in the surrounding brain tissues, whereas all the non-GBM patients showed FMISO uptake in the tumor equal to that in the surrounding brain tissues (p ≤ 0.001). One GBM patient was excluded from FDG PET study because of hyperglycemia. All GBM patients and three of the nine (33%) non-GBM patients showed FDG uptake greater than or equal to that in the gray matter. The sensitivity and specificity for diagnosing GBM were 100 and 100% for FMISO, and 100 and 66% for FDG, respectively. The lesion to cerebellum ratio of FMISO uptake was higher in GBM patients (2.74 ± 0.60, range 1.71-3.81) than in non-GBM patients (1.22 ± 0.06, range 1.09-1.29, p ≤ 0.001) with no overlap between the groups. The lesion to gray matter ratio of FDG was also higher in GBM patients (1.46 ± 0.75, range 0.91-3.79) than in non-GBM patients (1.07 ± 0.62, range 0.66-2.95, p ≤ 0.05); however, overlap of the ranges did not allow clear differentiation between GBM and non-GBM. The uptake volume of FMISO was larger in GBM (27.18 ± 10.46%, range 14.02-46.67%) than in non-GBM (6.07 ± 2.50%, range 2.12-9.22%, p ≤ 0.001).

CONCLUSION

These preliminary data suggest that FMISO PET may distinguish GBM from lower grade gliomas.