Glioma: Application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.

Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas

PURPOSE

Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value.

MATERIALS AND METHODS

In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram.

RESULTS

The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively.

CONCLUSION

Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.

Structural and practical identifiability of contrast transport models for DCE-MRI

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of  ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Structural and practical identifiability of contrast transport models for DCE-MRI

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Immune cell identity behind the Ktrans mapping of mouse glioblastoma

Dynamic contrast-enhanced MR imaging (DCE-MRI) can assess the integrity of the blood brain barrier (BBB) and has been used in GBM patients to determine glioma grade, predict prognosis, evaluate treatment response, and differentiate treatment-induced effect from recurrence. The volume transfer constant Ktrans is the most frequently used metric in tumor assessment. Based on previous studies that a higher WHO grade of brain tumor was associated with greater impairments of immunity and that Ktrans value was associated with the pathological grading, the relationship between differential composition of immune cells in GBM tissue and dynamic changes in Ktrans mapping was anticipated in this study. The present study utilized an orthotopic allograft model of GBM in which mouse GL26 cells are implanted into Ccr2RFP/wtCx3cr1GFP/wt mice on a C57 background. The brain tumors exhibited heterogenous Ktrans values with the coefficients of variation (CV) above 75%, or relatively homogeneous Ktrans maps with CV values below 50%. The Ktrans values of homogeneous tumors ranged between 0.02/min-0.32/min with a median value of 0.10/min. The immune cell composition defined by quantitative immunohistochemistry and cell sorting was compared between the tumors with Ktrans values above 0.10/min (higher Ktrans) or below 0.10/min (lower Ktrans). Histological analysis showed that tumors with higher Ktrans values exhibited greater numbers of CCR2pos cells (257.60 ± 16.42/mm2 vs 203.23 ± 12.20/mm2, p = 0.04) and an increased ratio of CCR2pos cells to CX3CR1pos cells (1.20 ± 0.02 vs 0.38 ± 0.04, p = 0.001), the numbers of CX3CR1pos cells did not differ significantly based on Ktrans values (219.70 ± 16.20/mm2 vs 250.38 ± 21.20/mm2, p = 0.19). Flowcytometry analysis showed that tumors with higher Ktrans values (above 0.1/min) were associated with greater numbers of both overall monocytes (54.93 ± 6.81% vs 29.75 ± 3.54%, p = 0.01) and inflammatory monocytes (72.38 ± 1.49% vs 59.52 ± 2.44%, p = 0.001). In contrast, tumors with lower Ktrans values (below 0.1/min) exhibited greater numbers of patrolling monocytes (75.65 ± 4.14% vs 63 ± 6.94%, p = 0.05). In the tumors with lower Ktrans values, all three types of tumor associated cells, including patrolling monocytes, inflammatory monocytes, and microglia cells possessed a higher proportion of cells at pro-inflammatory status (41.77 ± 6.13% vs 25.06 ± 6.72%, p = 0.05; 27.50 ± 2.11% vs 20.62 ± 1.87%, p = 0.03; and 55.80 ± 9.88% vs 31.12 ± 7.31%, p = 0.05), inflammatory monocytes showed fewer anti-inflammatory cells (1.25 ± 0.62% vs 3.16 ± 3.56%, p = 0.04). Taken together, differences in Ktrans values were associated with differential immune cell phenotypes and polarizations. Ktrans mapping may therefore represent a novel approach for defining the immune status of GBM.

Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition

OBJECTIVE

To investigate the relationship between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) measurements and the potential composition of rectal carcinoma.

METHODS

Twenty-four patients provided informed consent for this study. DCE-MRI was performed before total mesorectal excision. Quantitative parameters were calculated based on a modified Tofts model. Whole-mount immunohistochemistry and Masson staining sections were generated and digitized at histological resolution. The percentage of tissue components area was measured. Pearson correlation analysis was used to evaluate the correlations between pathological parameters and DCE-MRI parameters.

RESULTS

On the World Health Organization (WHO) grading scale, there were significant differences in extracellular extravascular space (Ktrans) (F = 9.890, P = 0.001), mean transit time (MTT) (F = 9.890, P = 0.038), CDX-2 (F = 4.935, P = 0.018), and Ki-67 (F = 4.131, P = 0.031) among G1, G2, and G3. ECV showed significant differences in extramural venous invasion (t =  - 2.113, P = 0.046). Ktrans was strongly positively correlated with CD34 (r = 0.708, P = 0.000) and moderately positively correlated with vimentin (r = 0.450, P = 0.027). Interstitial volume (Ve) was moderately positively correlated with Masson's (r = 0.548, P = 0.006) and vimentin (r = 0.417, P = 0.043). There was a moderate negative correlation between Ve and CDX-2 (r =  - 0.441, P = 0.031). The rate constant from extracellular extravascular space to blood plasma (Kep) showed a strong positive correlation with CD34 expression (r = 0.622, P = 0.001). ECV showed a moderate negative correlation with CDX-2 (r =  - 0.472, P = 0.020) and a moderate positive correlation with collagen fibers (r = 0.558, P = 0.005).

CONCLUSION

The dynamic contrast-enhanced MRI-derived parameters measured in rectal cancer were significantly correlated with the proportion of histological components. This may serve as an optimal imaging biomarker to identify tumor tissue components.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study

OBJECTIVES

There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS.

METHODS

This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis.

RESULTS

Ultimately, 35 participants (21 women, 52 ± 12 years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7 months. K^trans (0.036 min^-1 vs. 0.057 min^-1, p = .008) and initial area under the time-concentration curve within 90 s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. K^trans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. K^trans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction.

CONCLUSION

DCE-MRI (particularly K^trans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS.

KEY POINTS

•Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.

Neurofibromatosis type 2 versus sporadic vestibular schwannoma: The utility of MR diffusion and dynamic contrast-enhanced imaging

BACKGROUND AND PURPOSE

The goal of this study was to assess the utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish sporadic vestibular schwannomas (VSs) from those related to neurofibromatosis type 2 (NF2).

METHODS

We retrospectively reviewed 265 patients pathologically diagnosed with VSs between January 2015 and October 2020 in a single institution. There were 28 patients (male: 19, female: 9; age 11-67 years) including 23 sporadic and five NF2-related VSs, who had pretreatment DWI and DCE-MRI. Normalized mean apparent diffusion coefficient (nADCmean) and DCE-MRI parameters along with tumor characteristics were compared between sporadic and NF2-related VSs as appropriate. The diagnostic performances were calculated based on the receiver operating characteristic curve analysis for the values that showed significant differences. To identify significant modalities, multivariate logistic regression analysis was performed using nADCmean and the combination of statistically significant DCE-MRI parameters.

RESULTS

NADCmean, fractional volume of extracellular space (Ve), and forward volume transfer constant (Ktrans) were significantly different between sporadic and NF2-related VSs (nADCmean: median 1.62 vs. 1.16, P = .002; Ve: median 0.40 vs. 0.66, P = .007; Ktrans: median 0.17 vs. 0.33, P = .007), whereas fractional plasma volume (Vp), reverse reflux rate constant (Kep), and tumor characteristics were not. The diagnostic performances of nADCmean, Ve, and Ktrans were 0.93, 0.90, and 0.90 area under the curves with cutoffs of 1.46, 0.51, and 0.29, respectively. nADCmean and the combination of Ve and Ktrans were both chosen as significant differentiators by multivariate logistic regression analysis (P = .027).

CONCLUSIONS

DWI and DCE-MRI are both promising modalities to distinguish sporadic and NF2-related VSs.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

MR diffusion and dynamic-contrast enhanced imaging to distinguish meningioma, paraganglioma, and schwannoma in the cerebellopontine angle and jugular foramen

BACKGROUND AND PURPOSE

Differentiation of meningiomas, paragangliomas, and schwannomas in the cerebellopontine angle and jugular foramen remains challenging when conventional MRI findings are inconclusive. This study aimed to assess the clinical utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) findings for tumor type differentiation and to identify the most significant diagnostic parameters.

METHODS

This retrospective study included 57 patients with pathologically confirmed meningiomas, paragangliomas, and schwannomas, diagnosed between January 2018 and August 2021. DWI and DCE-MRI were obtained before surgery. The apparent diffusion coefficient (ADC) and DCE-MRI parameters were calculated. The Kruskal-Wallis H test and post hoc test with Bonferroni correction and receiver operating characteristic curve were used for statistical analysis.

RESULTS

There were 20 meningiomas (6 men; 62.3 ± 17.8 years), 23 paragangliomas (3 men; 51.6 ± 17.0 years), and 14 schwannomas (7 men; 37.7 ± 20.0 years). V_p showed a significant difference in each comparison (p < .001, <.001, and <.001, respectively), V_e showed significant differences both in meningiomas and paragangliomas, and paragangliomas and schwannomas (p < .001 and .017, respectively), and K_trans showed significant differences both in meningiomas and paragangliomas, and meningiomas and schwannomas (p = .0018 and <.001, respectively), though there was no significant difference in ADC. V_p diagnostic performance values for each pair of tumors were area under the curve of 0.89-1.00, with cutoff values of 0.14-0.27.

CONCLUSION

DCE-MRI can provide promising parameters to differentiate meningiomas, paragangliomas, and schwannomas in the cerebellopontine angle and jugular foramen.

Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm³; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm³; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (K^trans, V_p, and V_e). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95^th percentile K^trans and V_e from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading

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LRD medical image fusion algorithm can be used for glioma grading.

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We can use the LRD fusion algorithm with MRI image for glioma grading.

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Fusing of DWI (b50) and T₁ enhancement (T₁Gd) by LRD, have highest diagnostic value for glioma grading.

Background

Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.

Procedures

In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.

Findings

We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.

Conclusions

This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

Objective

To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma.

Materials and Methods

One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the “radiomics risk score” groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates.

Results

16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively).

Conclusion

We developed and validated the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI

PURPOSE

This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation.

METHODS

Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features.

RESULTS

Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma.

CONCLUSION

We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.

Effects of artery input function on dynamic contrast-enhanced MRI for determining grades of gliomas

OBJECTIVE

To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas.

METHODS

49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as K^trans (volume transfer constant) and V_e (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The K^trans and V_e were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant.

RESULTS

All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (K^trans and V_e) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the K^trans and V_e from any AIFs could distinguish between low and high grade, however, only K^trans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor.

CONCLUSION

Both quantitative parameters K^trans and V_e calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only K^trans can distinguish grades II and III.

ADVANCES IN KNOWLEDGE

We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters K^trans and V_e calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.

Prognostic Value of Dynamic Contrast-Enhanced MRI-Derived Pharmacokinetic Variables in Glioblastoma Patients: Analysis of Contrast-Enhancing Lesions and Non-Enhancing T2 High-Signal Intensity Lesions

OBJECTIVE

To evaluate pharmacokinetic variables from contrast-enhancing lesions (CELs) and non-enhancing T2 high signal intensity lesions (NE-T2HSILs) on dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in glioblastoma (GBM) patients.

MATERIALS AND METHODS

Sixty-four GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. We analyzed the pharmacokinetic variables of the volume transfer constant (Ktrans) and volume fraction of extravascular extracellular space within the CEL and NE-T2HSIL of the entire tumor. Univariate and multivariate Cox regression analyses were performed using preoperative clinical characteristics, pharmacokinetic variables of DCE MR imaging, and postoperative molecular biomarkers to predict PFS.

RESULTS

The increased mean Ktrans of the CEL, increased 95th percentile Ktrans of the CELs, and absence of methylated O⁶-methylguanine-DNA methyltransferase promoter were relevant adverse variables for PFS in the univariate analysis (p = 0.041, p = 0.032, and p = 0.083, respectively). The Kaplan-Meier survival curves demonstrated that PFS was significantly shorter in patients with a mean Ktrans of the CEL > 0.068 and 95th percentile Ktrans of the CEL>0.223 (log-rank p = 0.038 and p = 0.041, respectively). However, only mean Ktrans of the CEL was significantly associated with PFS (p = 0.024; hazard ratio, 553.08; 95% confidence interval, 2.27-134756.74) in the multivariate Cox proportional hazard analysis. None of the pharmacokinetic variables from NE-T2HSILs were significantly related to PFS.

CONCLUSION

Among the pharmacokinetic variables extracted from CELs and NE-T2HSILs on preoperative DCE MR imaging, the mean Ktrans of CELs exhibits potential as a useful imaging predictor of PFS in GBM patients.

Comparison of Dynamic Contrast-Enhancement Parameters between Gadobutrol and Gadoterate Meglumine in Posttreatment Glioma: A Prospective Intraindividual Study

BACKGROUND AND PURPOSE

Differences in molecular properties between one-molar and half-molar gadolinium-based contrast agents are thought to affect parameters obtained from dynamic contrast-enhanced imaging. The aim of our study was to investigate differences in dynamic contrast-enhanced parameters between one-molar nonionic gadobutrol and half-molar ionic gadoterate meglumine in patients with posttreatment glioma.

MATERIALS AND METHODS

This prospective study enrolled 32 patients who underwent 2 20-minute dynamic contrast-enhanced examinations, one with gadobutrol and one with gadoterate meglumine. The model-free parameter of area under the signal intensity curve from 30 to 1100 seconds and the Tofts model-based pharmacokinetic parameters were calculated and compared intraindividually using paired t tests. Patients were further divided into progression (n = 12) and stable (n = 20) groups, which were compared using Student t tests.

RESULTS

Gadobutrol and gadoterate meglumine did not show any significant differences in the area under the signal intensity curve or pharmacokinetic parameters of K ^trans, V_e, V_p, or K_ep (all P > .05). Gadobutrol showed a significantly higher mean wash-in rate (0.83 ± 0.64 versus 0.29 ± 0.63, P = .013) and a significantly lower mean washout rate (0.001 ± 0.0001 versus 0.002 ± 0.002, P = .02) than gadoterate meglumine. Trends toward higher area under the curve, K ^trans, V_e, V_p, wash-in, and washout rates and lower K_ep were observed in the progression group in comparison with the treatment-related-change group, regardless of the contrast agent used.

CONCLUSIONS

Model-free and pharmacokinetic parameters did not show any significant differences between the 2 gadolinium-based contrast agents, except for a higher wash-in rate with gadobutrol and a higher washout rate with gadoterate meglumine, supporting the interchangeable use of gadolinium-based contrast agents for dynamic contrast-enhanced imaging in patients with posttreatment glioma.

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIF_DSC) and AIF measured at DCE MRI (AIF_DCE). The model was trained to translate AIF_DCE into AIF_DSC, and after training, outputted neural-network-generated AIF (AIF_{generated DSC}) with input AIF_DCE. By using the three different AIFs, volume transfer constant (K^trans), fractional volume of extravascular extracellular space (V_e), and vascular plasma space (V_p) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs. Results The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIF_DCE (mean K^trans, 0.88 [95% confidence interval {CI}: 0.81, 0.93] vs 0.72 [95% CI: 0.63, 0.79], P = .04; mean V_e, 0.87 [95% CI: 0.79, 0.92] vs 0.70 [95% CI: 0.61, 0.77], P = .049, respectively). K^trans and V_e showed higher intraclass correlation coefficients for AIF_{generated DSC} than for AIF_DCE (0.91 vs 0.38, P < .001; and 0.86 vs 0.60, P < .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIF_{generated DSC} than AIF_DCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. © RSNA, 2020 Online supplemental material is available for this article.

Feasibility of using dynamic contrast-enhanced MRI for differentiating thymic carcinoma from thymic lymphoma based on semi-quantitative and quantitative models

AIM

To evaluate the value of using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived parameters to differentiate thymic carcinoma and thymic lymphoma based on semi-quantitative and quantitative models.

MATERIALS AND METHODS

Twenty-nine pathologically confirmed anterior mediastinum tumours in 29 patients were enrolled in this retrospective study, including 15 thymic carcinoma and 14 lymphoma patients. All the patients underwent pre-treatment mediastinum DCE-MRI. Both semi-quantitative and quantitative parameters were calculated and the volume transfer constant K^trans, the flux rate constant between extravascular extracellular space and plasma k_ep, the extravascular extracellular volume fraction v_e were obtained based on a modified Tofts model. DCE-MRI derived parameters were compared between thymic carcinoma and thymic lymphoma groups.

RESULTS

Thymic carcinoma had significantly lower k_ep (p=0.040) and higher v_e (p=0.018) than thymic lymphoma; however, there were no significant differences on K^trans and semi-quantitative parameters between the two groups. v_e had the highest area under the curve (cut-off value, 0.282; area under the curve, 0.748; sensitivity, 71.4%; specificity, 80%). The combination of k_ep and v_e could increase the diagnostic performance significantly (area under the curve, 0.752; sensitivity, 57.1%; specificity, 93.3%).

CONCLUSION

DCE-MRI derived parameters may have value in the differentiating thymic carcinoma and thymic lymphoma.

DCE-MRI perfusion predicts pseudoprogression in metastatic melanoma treated with immunotherapy

PURPOSE

It can be challenging to differentiate pseudoprogression from progression. We assessed the ability of dynamic contrast enhanced T1 MRI (DCE-MRI) perfusion to identify pseudoprogression in melanoma brain metastases.

METHODS

Patients with melanoma brain metastases who underwent immunotherapy and DCE-MRI were identified. Enhancing lesions ≥  5mm in diameter on DCE-MRI and that were new or increased in size between a week from beginning the treatment, and a month after completing the treatment were included in the analysis. The 90th percentiles of rVp and rKtrans and the presence or absence of hemorrhage were recorded. Histopathology served as the reference standard for pseudoprogression. If not available, pseudoprogression was defined as neurological and radiographic stability or improvement without any new treatment for ≥ 2 months.

RESULTS

Forty-four patients were identified; 64% received ipilimumab monotherapy for a median duration of 9 weeks (range, 1-138). Sixty-four lesions in 44 patients were included in the study. Of these, nine lesions in eight patients were determined to be pseudoprogression and seven lesions were previously irradiated. Forty-four progression lesions and eight pseudoprogression lesions were hemorrhagic. Median lesion volume for pseudoprogression and progression were not significantly different, at 2.3 cm³ and 3.2 cm³, respectively (p = 0.82). The rVp⁹⁰ was smaller in pseudoprogression versus progression, at 2.2 and 5.3, respectively (p = 0.02), and remained significant after false discovery rate adjustment (p = 0.04).

CONCLUSIONS

Pseudoprogression exhibited significantly lower rVp⁹⁰ on DCE-MRI compared with progression. This knowledge can be useful for managing growing lesions in patients with melanoma brain metastases who are receiving immunotherapy.

Dynamic Contrast-Enhanced MR Imaging of Nonenhancing T2 High-Signal-Intensity Lesions in Baseline and Posttreatment Glioblastoma: Temporal Change and Prognostic Value

BACKGROUND AND PURPOSE

The prognostic value of dynamic contrast-enhanced MR imaging on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma has not been thoroughly elucidated to date. We evaluated the temporal change and prognostic value for progression-free survival of dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters on nonenhancing T2 high-signal-intensity lesions in patients with glioblastoma before and after standard treatment, including gross total surgical resection.

MATERIALS AND METHODS

This retrospective study included 33 patients who were newly diagnosed with glioblastoma and treated with gross total surgical resection followed by concurrent chemoradiation therapy and adjuvant chemotherapy with temozolomide in a single institution. All patients underwent dynamic contrast-enhanced MR imaging before surgery as a baseline and after completion of maximal surgical resection and concurrent chemoradiation therapy. On the whole nonenhancing T2 high-signal-intensity lesion, dynamic contrast-enhanced MR imaging-derived pharmacokinetic parameters (volume transfer constant [K ^trans], volume of extravascular extracellular space [v _e]_, and blood plasma volume [v_p ]) were calculated. The Cox proportional hazards regression model analysis was performed to determine the histogram features or percentage changes of pharmacokinetic parameters related to progression-free survival.

RESULTS

Baseline median K ^trans, baseline first quartile K ^trans, and posttreatment median K ^trans were significant independent variables, as determined by univariate analysis (P < .05). By multivariate Cox regression analysis including methylation status of O⁶-methylguanine-DNA methyltransferase, baseline median K ^trans was determined to be the significant independent variable and was negatively related to progression-free survival (hazard ratio = 1.48, P = .003).

CONCLUSIONS

Baseline median K ^trans from nonenhancing T2 high-signal-intensity lesions could be a potential prognostic imaging biomarker in patients undergoing gross total surgical resection followed by standard therapy for glioblastoma.

Prognostic Predictions for Patients with Glioblastoma after Standard Treatment: Application of Contrast Leakage Information from DSC-MRI within Nonenhancing FLAIR High-Signal-Intensity Lesions

BACKGROUND AND PURPOSE

Attempts have been made to quantify the microvascular leakiness of glioblastomas and use it as an imaging biomarker to predict the prognosis of the tumor. The purpose of our study was to evaluate whether the extraction fraction value from DSC-MR imaging within nonenhancing FLAIR hyperintense lesions was a better prognostic imaging biomarker than dynamic contrast-enhanced MR imaging parameters for patients with glioblastoma.

MATERIALS AND METHODS

A total of 102 patients with glioblastoma who received a preoperative dynamic contrast-enhanced MR imaging and DSC-MR imaging were included in this retrospective study. Patients were classified into the progression (n = 87) or nonprogression (n = 15) groups at 24 months after surgery. We extracted the means and 95th percentile values for the contrast leakage information parameters from both modalities within the nonenhancing FLAIR high-signal-intensity lesions.

RESULTS

The extraction fraction 95th percentile value was higher in the progression-free survival group of >24 months than at ≤24 months. The median progression-free survival of the group with an extraction fraction 95th percentile value of >13.32 was 17 months, whereas that of the group of ≤13.32 was 12 months. In addition, it was an independent predictor variable for progression-free survival in the patients regardless of their ages and genetic information.

CONCLUSIONS

The extraction fraction 95th percentile value was the only independent parameter for prognostic prediction in patients with glioblastoma among the contrast leakage information, which has no statistically significant correlations with the DCE-MR imaging parameters.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Purpose

This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival.

Patients and methods

A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival.

Results

Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of V_e were demonstrated to have higher accuracies (the accuracies for Extended Tofts_V_e^mean and Extended Tofts_V_e^median were 68.33% and 71.67%, respectively, while those for the Incremental_V_e^mean and Incremental_V_e^75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_V_e histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_K_trans^95th (AUC = 0.9265) and Extended Tofts_V_e^95th (AUC = 0.9154) performed better than their corresponding means (Patlak_K_trans^mean: AUC = 0.9118 and Extended Tofts_V_e^mean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up.

Conclusion

DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading.

Trial registration

NCT 02622620.

MR Perfusion and MR Spectroscopy of Brain Neoplasms

Advances in imaging techniques, such as MR perfusion and spectroscopy, are increasingly indispensable in the management and treatment plans of brain neoplasms: from diagnosing, molecular/genetic typing and grading neoplasms, augmenting biopsy results and improving accuracy, to ultimately directing and monitoring treatment and response. New developments in treatment methods have resulted in new diagnostic challenges for conventional MR imaging, such as pseudoprogression, where MR perfusion has the widest current application. MR spectroscopy is showing increasing promise in noninvasively determining genetic subtypes and, potentially, susceptibility to molecular targeted therapies.

Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis

Background

T1‐weighted dynamic contrast‐enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE‐MRI in discriminating between low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs), between tumor recurrence and treatment‐related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs.

Methods

We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE‐MRI for the aforementioned entities. Meta‐analysis was conducted with the use of a random effects model.

Results

Twenty‐seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE‐MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment‐related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment‐related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively.

Conclusions

Dynamic contrast‐enhanced‐Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE‐MRI shows high diagnostic accuracy in discriminating between HGGs and their low‐grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment‐related changes as well as PCNSLs and HGGs.

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading

BACKGROUND

The application of conventional magnetic resonance imaging (MRI) in glioma grading is limited and non-specific.

PURPOSE

To investigate the application values of MRI, texture analysis (TA) of dynamic contrast-enhanced MRI (DCE-MRI) and intratumoral susceptibility signal (ITSS) on susceptibility weighted imaging (SWI), alone and in combination, for glioma grading.

MATERIAL AND METHODS

Fifty-two patients with pathologically confirmed gliomas who underwent DCE-MRI and SWI were enrolled in this retrospective study. Conventional MRIs were evaluated by the VASARI scoring system. TA of DCE-MRI-derived parameters and the degree of ITSS were compared between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). The diagnostic ability of each parameter and their combination for glioma grading were analyzed.

RESULTS

Significant statistical differences in VASARI features were observed between LGGs and HGGs ( P < 0.05), of which the enhancement quality had the highest area under the curve (AUC) (0.873) with 93.3% sensitivity and 80% specificity. The TA of DCE-MRI derived parameters were significantly different between LGGs and HGGs ( P < 0.05), of which the uniformity of K^trans had the highest AUC (0.917) with 93.3% sensitivity and 90% specificity. The degree of ITSS was significantly different between LGGs and HGGs ( P < 0.001). The AUC of the ITSS was 0.925 with 93.3% sensitivity and 90% specificity. The best discriminative power was obtained from a combination of enhancement quality, K^trans- uniformity, and ITSS, resulting in 96.7% sensitivity, 100.0% specificity, and AUC of 0.993.

CONCLUSION

Combining conventional MRI, TA of DCE-MRI, and ITSS on SWI may help to improve the differentiation between LGGs and HGGs.

Dynamic Contrast-Enhanced T1-Weighted Perfusion Magnetic Resonance Imaging Identifies Glioblastoma Immunohistochemical Biomarkers via Tumoral and Peritumoral Approach: A Pilot Study

OBJECTIVE

We aimed to evaluate the usefulness of dynamic contrast-enhanced T1-weighted perfusion magnetic resonance imaging (DCE-pMRI) to predict certain immunohistochemical (IHC) biomarkers of glioblastoma (GB) in this pilot study.

METHODS

We retrospectively reviewed 36 patients (male/female, 25:11; mean age, 53 years; age range, 29-85 years) who had pretreatment DCE-pMRI with IHC analysis of their excised GBs. Regions of interest of the enhancing tumor (ER) and nonenhancing peritumoral region (NER) were used to calculate DCE-pMRI parameters of volume transfer constant, back flux constant, volume of the extravascular extracellular space, initial area under enhancement curve, and maximum slope. IHC biomarkers including Ki-67 labeling index, epidermal growth factor receptor (EGFR), oligodendrocyte transcription factor 2 (OLIG2), isocitrate dehydrogenase 1 (IDH1), and p53 mutation status were determined. The imaging metrics of GB with IHC markers were compared using the Kruskal-Wallis test and Spearman correlation analysis.

RESULTS

Among 30 patients with available IDH1 status, 14 patients (46.6%) had IDH1 mutation. EGFR amplification was present in 24/36 (66.6%) patients. Mean Ki-67 labeling index was 29% (range, 1.5%-80%). p53 mutation was present in 20/36 GBs (55%), whereas OLIG2 expression was positive in 29/36 GBs (80.5%). Various DCE-pMRI parameters gathered from the ER and NER were significantly correlated with IDH1 mutation, EGFR amplification, and OLIG2 expression (P < 0.05). Ki-67 labeling index showed a strong positive correlation with initial area under enhancement curve (r = 0.619; P < 0.001).

CONCLUSIONS

DCE-pMRI could determine surrogate IHC biomarkers in GB via tumoral and peritumoral approach, potential targets for individualized treatment protocols.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging for differentiating malignant from benign orbital lymphproliferative disorders

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for assessing orbital lymphoproliferative disorders (OLPDs). However, only the mean values of quantitative parameters were obtained in previous studies and tumor heterogeneity was ignored.

PURPOSE

To assess the value of DCE-MRI derived histogram parameters in differentiating malignant from benign OLPDs.

MATERIAL AND METHODS

Forty-eight OLPDs patients (25 malignant and 23 benign) who had undergone DCE-MRI for pre-treatment evaluation were retrospectively included. Histogram parameters of K^trans, k_ep, and v_e were calculated and compared between two groups using the independent sample's t-test. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of malignant OLPDs.

RESULTS

Tenth k_ep, mean k_ep, median k_ep, and 90th k_ep were significantly higher in the malignant OLPD group than in the benign OLPD group. Tenth v_e was significantly lower in the malignant OLPD group than in the benign OLPD group. Ninetieth k_ep was the only independent predictor of malignant OLPDs ( P = 0.019), with an area under ROC curve of 0.828, a sensitivity of 92.00%, and a specificity of 78.26% at a cut-off value of 1.057 min^-1.

CONCLUSION

Histogram analysis of DCE-MRI derived parameters may help to differentiate malignant from benign OLPDs. The 90th k_ep hold the potential as an independent predictor for malignant OLPDs.

Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma

BACKGROUND

Accurate glioma grading plays an important role in patient treatment.

PURPOSE

To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM).

STUDY TYPE

Retrospective.

POPULATION

In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007.

FIELD STRENGTH/SEQUENCE

3.0T MRI/ T₁ WI, T₂ fluid-attenuated inversion recovery, contrast enhanced T₁ , arterial spinal labeling, diffusion-weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm² ), and dynamic contrast-enhanced.

ASSESSMENT

Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF-SVM) combined with attribute selection using SVM-recursive feature elimination (SVM-RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10-fold crossvalidation. The model performance was further tested using the remaining 20% data.

STATISTICAL TESTS

Ten-fold crossvalidation was used to validate the model performance.

RESULTS

Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM-RFE was able to reduce attribute redundancy as well as improve RBF-SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray-level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM-RFE to provide comparable classifying efficacy.

DATA CONCLUSION

When using image textures-based SVM classification of gliomas, the GLSZM model in combination with gray-level 64 and attribute selection may be an optimized solution.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263-1274.

Pretreatment dynamic contrast-enhanced MRI biomarkers correlate with progression-free survival in primary central nervous system lymphoma

PURPOSE

Prediction of clinical outcomes in patients with primary central nervous system lymphoma (PCNSL) is important for optimization of treatment planning. Quantitative imaging biomarkers for PCNSL have not yet been established. This study evaluated the prognostic value of pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging for progression-free survival (PFS) in patients with PCNSL.

METHODS

Pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging were retrospectively analyzed in 18 immunocompetent patients with PCNSL. Volumes of interest encompassing the tumors were assessed for measurements of blood plasma volume (Vp), volume transfer constant (K^trans), and apparent diffusion coefficient. Patients were divided into short and long PFS groups based on median PFS. Imaging and clinical variables were correlated with PFS.

RESULTS

Median PFS was 19.6 months. Lower Vp_mean and K^trans_mean values increased risk for rapid progression (< 19.6 months). Receiver operating characteristic curve analysis demonstrated an optimal Vp_mean cutoff value of 2.29 (area under the curve [AUC] = 0.74, sensitivity and specificity = 0.78, p = 0.023) for separating patients with short and long PFS. The optimal K^trans_mean cutoff was 0.08 (AUC = 0.74, sensitivity = 0.67, specificity = 0.78, p = 0.025). Kaplan-Meier survival analysis with log-rank test demonstrated significantly (p = 0.015) increased risk of rapid progression for patients with Vp_mean < 2.29. Vp_mean was significantly (p = 0.03) associated with PFS on univariate Cox analysis. Apparent diffusion coefficient values and clinical factors did not influence PFS.

CONCLUSIONS

Pretreatment Vp and K^trans derived from dynamic contrast-enhanced MRI may be novel prognostic quantitative imaging biomarkers of progression-free survival in patients with PCNSL. These data should be prospectively validated in larger patient cohorts.

Differentiation of grade II and III oligodendrogliomas from grade II and III astrocytomas: a histogram analysis of perfusion parameters derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI

Background Since oligodendroglial tumors are sensitive to chemotherapy and have a better prognosis, the differentiation of oligodendroglial tumors (OT) from astrocytic tumors (AT) is important. Purpose To investigate the perfusion and permeability parameters that differentiate grade II and III OT from AT, using dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI). Material and Methods We retrospectively reviewed the DCE and DSC MRIs of 39 patients with OT (OTs, n = 19; grade II, n = 12 and grade III, n = 7) and AT (ATs, n = 20; grade II, n = 7 and grade III, n = 13). Glioblastomas were not included. Various histogram parameters of relative cerebral blood volume, volume transfer constant (Ktrans), flux rate constant (Kep), plasma volume fraction (Vp), and extravascular extracellular volume fraction (Ve) from DSC and DCE MRI, were compared between the two groups. Univariable and multivariable logistic regression were used to distinguish OT from AT. Receiver operating characteristic (ROC) curve analysis was performed. Results On the results of DCE MRI, most of the histogram parameters of Ktrans, Kep, and Ve showed tendencies toward higher values in OT than AT. Multivariable logistic regression revealed that the 50th Kep and 95th Ktrans were the most significant parameters predictive of OT, with an odds ratio of 3.7 and 2.5, respectively ( P = 0.004 and 0.03). The area under the curve from the ROC curve analysis for the 50th Kep and the 95th Ktrans were 0.81 and 0.80, respectively. Conclusion The DCE MRI-derived parameters of Ktrans and Kep could facilitate differentiation of OT from AT.

Imaging biomarkers from multiparametric magnetic resonance imaging are associated with survival outcomes in patients with brain metastases from breast cancer

OBJECTIVES

The aim of this study is to investigate the correlation of survival outcomes with imaging biomarkers from multiparametric magnetic resonance imaging (MRI) in patients with brain metastases from breast cancer (BMBC).

METHODS

This study was approved by the institutional review board. Twenty-two patients with BMBC who underwent treatment involving bevacizumab on day 1, etoposide on days 2-4, and cisplatin on day 2 in 21-day cycles were prospectively enrolled for a phase II study. Three brain MRIs were performed: before the treatment, on day 1, and on day 21. Eight imaging biomarkers were derived from dynamic contrast-enhanced MRI (Peak, IAUC₆₀, K^trans, k_ep, v_e), diffusion-weighted imaging [apparent diffusion coefficient (ADC)], and MR spectroscopy (choline/N-acetylaspartate and choline/creatine ratios). The relative changes (Δ) in these biomarkers were correlated with the central nervous system (CNS)-specific progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier and Cox proportional hazard models.

RESULTS

There were no significant differences in the survival outcomes as per the changes in the biomarkers on day 1. On day 21, those with a low ΔK^trans (p = 0.024) or ΔADC (p = 0.053) reduction had shorter CNS-specific PFS; further, those with a low ΔPeak (p = 0.012) or ΔIAUC₆₀ (p = 0.04) reduction had shorter OS compared with those with high reductions. In multivariate analyses, ΔK^trans and ΔPeak were independent prognostic factors for CNS-specific PFS and OS, respectively, after controlling for age, size, hormone receptors, and performance status.

CONCLUSIONS

Multiparametric MRI may help predict the survival outcomes in patients with BMBC.

KEY POINTS

• Decreased angiogenesis after chemotherapy on day 21 indicated good survival outcome. • ΔK ^trans was an independent prognostic factors for CNS-specific PFS. • ΔPeak was an independent prognostic factors for OS. • Multiparametric MRI helps clinicians to assess patients with BMBC. • High-risk patients may benefit from more intensive follow-up or treatment strategies.

Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading

BACKGROUND AND OBJECTIVE

To investigate the feasibility of histogram analysis of the T2*-based permeability parameter volume transfer constant (K^trans) for glioma grading and to explore the diagnostic performance of the histogram analysis of K^trans and blood plasma volume (v_p).

METHODS

We recruited 31 and 11 patients with high- and low-grade gliomas, respectively. The histogram parameters of K^trans and v_p, derived from the first-pass pharmacokinetic modeling based on the T2* dynamic susceptibility-weighted contrast-enhanced perfusion-weighted magnetic resonance imaging (T2* DSC-PW-MRI) from the entire tumor volume, were evaluated for differentiating glioma grades.

RESULTS

Histogram parameters of K^trans and v_p showed significant differences between high- and low-grade gliomas and exhibited significant correlations with tumor grades. The mean K^trans derived from the T2* DSC-PW-MRI had the highest sensitivity and specificity for differentiating high-grade gliomas from low-grade gliomas compared with other histogram parameters of K^trans and v_p.

CONCLUSIONS

Histogram analysis of T2*-based pharmacokinetic imaging is useful for cerebral glioma grading. The histogram parameters of the entire tumor K^trans measurement can provide increased accuracy with additional information regarding microvascular permeability changes for identifying high-grade brain tumors.

Application of a Simplified Method for Estimating Perfusion Derived from Diffusion-Weighted MR Imaging in Glioma Grading

Purpose: To evaluate the feasibility of a simplified method based on diffusion-weighted imaging (DWI) acquired with three b-values to measure tissue perfusion linked to microcirculation, to validate it against from perfusion-related parameters derived from intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging, and to investigate its utility to differentiate low- from high-grade gliomas. Materials and Methods: The prospective study was approved by the local institutional review board and written informed consent was obtained from all patients. From May 2016 and May 2017, 50 patients confirmed with glioma were assessed with multi-b-value DWI and DCE MR imaging at 3.0 T. Besides conventional apparent diffusion coefficient (ADC_0,1000) map, perfusion-related parametric maps for IVIM-derived perfusion fraction (f) and pseudodiffusion coefficient (D*), DCE MR imaging-derived pharmacokinetic metrics, including K^trans, v_e and v_p, as well as a metric named simplified perfusion fraction (SPF), were generated. Correlation between perfusion-related parameters was analyzed by using the Spearman rank correlation. All imaging parameters were compared between the low-grade (n = 19) and high-grade (n = 31) groups by using the Mann-Whitney U test. The diagnostic performance for tumor grading was evaluated with receiver operating characteristic (ROC) analysis. Results: SPF showed strong correlation with IVIM-derived f and D* (ρ = 0.732 and 0.716, respectively; both P < 0.001). Compared with f, SPF was more correlated with DCE MR imaging-derived K^trans (ρ = 0.607; P < 0.001) and v_p (ρ = 0.397; P = 0.004). Among all parameters, SPF achieved the highest accuracy for differentiating low- from high-grade gliomas, with an area under the ROC curve value of 0.942, which was significantly higher than that of ADC_0,1000 (P = 0.004). By using SPF as a discriminative index, the diagnostic sensitivity and specificity were 87.1% and 94.7%, respectively, at the optimal cut-off value of 19.26%. Conclusion: The simplified method to measure tissue perfusion based on DWI by using three b-values may be helpful to differentiate low- from high-grade gliomas. SPF may serve as a valuable alternative to measure tumor perfusion in gliomas in a noninvasive, convenient and efficient way.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIF_DSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIF_DCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIF_DSC and AIF_DCE were obtained from each image. Volume transfer constant (K^trans), volume of vascular plasma space (v_p), and volume of extravascular extracellular space (v_e) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIF_DSC-driven mean K^trans and v_e were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIF_DCE (area under the ROC curve: mean K^trans, 0.796 vs 0.645, P = .038; mean v_e, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.737 vs 0.095; v_p, 0.848 vs 0.728; v_e, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIF_DSC than AIF_DCE. Conclusion AIF_DSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIF_DCE. ^© RSNA, 2017 Online supplemental material is available for this article.

Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T

Background

Dynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B₁ inhomogeneity (B₁inh). The purpose of the study was to evaluate the effect of B₁inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B₁inh correction of perfusion parameters (PPs) on tumor grading.

Methods

An MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B₁-mapping, T₁-mapping and DCE-MRI were acquired. Relative B₁ maps (B_1rel) were generated using the saturated-double-angle method. T₁-maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal–intensity time (S(t)) curve to concentration–time (C(t)) curve followed by tracer kinetic analysis (K^trans, Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B₁inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B₁ inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain K^trans of glioma patients at different B_1rel values and see whether grading is affected or not.

Results

Current study shows that B_1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B_1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B₁inh.

Conclusions

B₁inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B₁inh correction during DCE-MRI data analysis.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1349-7) contains supplementary material, which is available to authorized users.

Advanced and amplified BOLD fluctuations in high-grade gliomas

BACKGROUND

Glioma grade along with patient's age and general health are used for treatment planning and prognosis.

PURPOSE

To characterize and quantify the spontaneous blood oxygen level-dependent (BOLD) fluctuations in gliomas using measures based on T2*-weighted signal time-series and to distinguish between high- and low-grade gliomas.

STUDY TYPE

Retrospective.

SUBJECTS

Twenty-one patients with high-grade and 13 patients with low-grade gliomas confirmed on histology were investigated.

FIELD STRENGTH/SEQUENCE

Dynamic T2*-weighted (multislice single-shot echo-planar-imaging) magnetic resonance imaging (MRI) was performed on a 3T system with an 8-element receive-only head coil to measure the BOLD fluctuations. In addition, a dynamic T₁ -weighted (3D fast field echo) dynamic contrast-enhanced (DCE) perfusion scan was performed.

ASSESSMENT

Three BOLD measures were determined: the temporal shift (TS), amplitude of low frequency fluctuations (ALFF), and regional homogeneity (ReHo). DCE perfusion-based cerebral blood volume (CBV) and time-to-peak (TTP) maps were concurrently evaluated for comparison.

STATISTICAL TESTS

An analysis-of-variance test was first used. When the test appeared significant, post-hoc analysis was performed using analysis-of-covariance with age as covariate. Logistic regression and receiver-operator characteristic curve analysis were also performed.

RESULTS

TS was significantly advanced in high-grade gliomas compared to the contralateral cortex (P = 0.01) and low-grade gliomas (P = 0.009). In high-grade gliomas, ALFF and CBV were significantly higher than the contralateral cortex (P = 0.041 and P = 0.008, respectively) and low-grade gliomas (P = 0.036 and P = 0.01, respectively). ReHo and TTP did not show significant differences between high- and low-grade gliomas (P = 0.46 and P = 0.42, respectively). The area-under-curve was above 0.7 only for the TS, ALFF, and CBV measures.

DATA CONCLUSION

Advanced and amplified hemodynamic fluctuations manifest in high-grade gliomas, but not in low-grade gliomas, and can be assessed using BOLD measures. Preliminary results showed that quantification of spontaneous fluctuations has potential for hemodynamic characterization of gliomas and distinguishing between high- and low-grade gliomas.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:1616-1625.

Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI using a model consistency constraint

PURPOSE

To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data.

METHODS

The proposed method poses the tracer-kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI.

RESULTS

In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF.

CONCLUSION

The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med 79:2804-2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging

The objective of the present study was to quantitatively analyze the permeability of tumor entity and peritumor edema in glioma grading, using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In the present retrospective study, 80 patients underwent T1-weighted DCE-MRI examination at 3.0 T and the pathological results (including astrocytoma and oligodendroglioma) were obtained between January 2012 and June 2015. All cases were surgically validated as grade I–IV gliomas. The original DCE-MRI data were analyzed using dual compartment modified Tofts model. The forward volume transfer constant (K^trans), backflux rate (k_ep) and fractional volume (v_e) were calculated with the region of interest selected on the highest permeability area of the tumor entity and peritumor edema. Analysis of variance with the Bonferroni correction was used to compare the values of K^trans, k_ep, and v_e of the tumor entity and peritumor edema in different glioma grades. The results of the present study revealed that the K^trans, k_ep, and v_e values in each stage were associated with the pathological grading (r=0.951, 0.804 and 0.766, respectively). There were significant differences identified between different tumor grades in K^trans, k_ep, with the exception being between grades II and III in k_ep. In addition, there was a significant difference revealed between grade I/II and grade III/IV in v_e. Receiver operator characteristics curve analysis was used to evaluate the diagnosis accuracies of permeability parameters. K^trans was demonstrated to exhibit the highest sensitivity and specificity for evaluating the tumor grade. With the threshold values of 0.160, 0.420 and 0.935 in K^trans on tumor, glioma grades I vs. II, II vs III and III vs. IV may be differentiated with sensitivities of 0.900, 0.950 and 0.950, and specificities of 0.950, 0.950 and 0.850, respectively. Furthermore, associations were observed between the K^trans, k_ep and v_e values of peritumor edema and the pathological grading in glioma (K^trans r=0.438, P<0.001; K_ep r=0.385, P<0.001; V_e r=0.397, P<0.001, respectively). K^trans values in peritumoral edema revealed significant differences between low-grade and high-grade glioma. The sensitivity and specificity for K^trans of peritumor edema were 0.975 and 0.950, with a threshold value of 0.007. Therefore, the DCE-MRI parameters of K^trans of tumor entity and peritumor edema in gliomas may be used to accurately differentiate glioma grades.

Differential diagnosis of oligodendroglial and astrocytic tumors using imaging results: the added value of perfusion MR imaging

PURPOSE

The purposes of the present study are to assess whether different characteristics of oligodendrogliomas and astrocytic tumors are visible on MR imaging and to determine the added value of perfusion imaging in conventional MR imaging when differentiating oligodendrogliomas from astrocytic tumors.

METHODS

We retrospectively studied 22 oligodendroglioma and 54 astrocytic tumor patients, including glioblastoma multiforme (GBM). The morphological tumor characteristics were evaluated using MR imaging. The rCBV, K ^trans, and V _e values were recorded. All imaging and clinical values were compared. The ability to discriminate between the two entities was evaluated using receiver operating characteristic curve analyses. Separate comparison analysis between oligodendroglioma and astrocytic tumors excluding GBM was also performed.

RESULTS

The presence of calcification, higher cortex involvement ratio, and lower V _e value were more representative of oligodendrogliomas than astrocytic tumors (P = <0.001, 0.038, and <0.001, respectively). The area under the curve (AUC) value of a combination of calcification and cortex involvement ratio was 0.796. The combination of all three parameters, including V _e, further increased the diagnostic performance (AUC = 0.881). Comparison test of the two AUC areas revealed significant difference (P = 0.0474). The presence of calcification and higher cortex involvement ratio were the only findings suggestive of oligodendrogliomas than astrocytic tumors with exclusion of GBMs (P = 0.014 and <0.001, respectively).

CONCLUSION

Cortex involvement ratio and the presence of calcification with V _e values were diagnostically accurate in identifying oligodendrogliomas. The V _e value calculated from dynamic contrast-enhanced MR imaging could be a supportive tool for differentiating between oligodendrogliomas and astrocytic tumors including GBMs.

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis

Purpose:

We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them.

Methods:

Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (K^trans) for transfer from plasma to the extravascular extracellular space. K^trans and cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of K^trans and cCBV were investigated. The differences in K^trans, cCBV, and K^trans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver operating characteristic (ROC) analysis of K^trans, cCBV, and K^trans/cCBV ratio was performed.

Results:

The 30th percentile (C30) in K^trans and 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed significantly lower C80 cCBV, significantly higher C30 K^trans, and significantly higher C30 K^trans/C80 cCBV than those of HGG. In ROC analysis, C30 K^trans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 K^trans or C80 cCBV.

Conclusion:

The combination of K^trans by DCE-MRI and cCBV by DSC-MRI was found to reveal the characteristics of vascularity and permeability of a lesion more precisely than either K^trans or cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.

Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases

Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast-enhanced (DCE)-MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (Ktrans ) derived from DCE-MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty-seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE-MRI. Regions of interest were manually drawn around the metastases to calculate Vpmean and Kmeantrans. The Mann-Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vpmean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vpmean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut-off value of 3.02 for Vpmean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE-MRI parameter Vpmean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.

Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges

Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.