Application of diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging for ganglioglioma in adults: Comparison study with oligodendroglioma

Nomograms Based on MRI Radiomics for Differential Diagnosis and Predicting BRAFV600E Expression in Pleomorphic Xanthoastrocytoma and Ganglioglioma

RATIONALE AND OBJECTIVES

This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression.

MATERIALS AND METHODS

This study included 265 patients histologically diagnosed with PXA (n = 113) and GG (n = 152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms.

RESULTS

Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms.

CONCLUSION

Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors

The fifth edition of the World Health Organization classification of central nervous system tumors published in 2021 reflects the current transitional state between traditional classification system based on histopathology and the state-of-the-art molecular diagnostics. This Part 3 Review focuses on the molecular diagnostics and imaging findings of glioneuronal and neuronal tumors. Histological and molecular features in glioneuronal and neuronal tumors often overlap with pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas (discussed in the Part 2 Review). Due to this overlap, in several tumor types of glioneuronal and neuronal tumors the diagnosis may be inconclusive with histopathology and genetic alterations, and imaging features may be helpful to distinguish difficult cases. Thus, it is crucial for radiologists to understand the underlying molecular diagnostics as well as imaging findings for application on clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Imaging of Neuronal and Mixed Glioneuronal Tumors

Neuronal and mixed glioneuronal tumors represent a group of neoplasms with varying degrees of neural and glial elements. Their age of presentation varies, but they are most commonly seen in children and young adults. With the exception of anaplastic ganglioglioma and other atypical variants, most lesions are low grade; however, they can have significant morbidity because of seizures, mass effect, or difficult to treat hydrocephalus. Although many tumors show overlapping clinical and imaging features, some have relatively distinctive imaging characteristics that may aid in narrowing the differential diagnosis. In this review, we discuss relevant clinical and pathologic characteristics of these tumors and provide an overview of conventional and advanced imaging features that provide clues as to the diagnosis.

Differentiation of Glioblastoma and Solitary Brain Metastasis by Gradient of Relative Cerebral Blood Volume in the Peritumoral Brain Zone Derived from Dynamic Susceptibility Contrast Perfusion Magnetic Resonance Imaging

OBJECTIVE

The purpose of our study was to evaluate the efficacy of the relative cerebral blood volume (rCBV) gradient in the peritumoral brain zone (PBZ)-the difference in the rCBV values from the area closest to the enhancing lesion to the area closest to the healthy white matter-in differentiating glioblastoma (GB) from solitary brain metastasis (MET).

METHODS

A 3.0-T magnetic resonance imaging (MRI) machine was used to perform dynamic susceptibility contrast perfusion MRI (DSC-MRI) on 43 patients with a solitary brain tumor (24 GB, 19 MET). The rCBV ratios were acquired by DSC-MRI data in 3 regions of the PBZ (near the enhancing tumor, G1; intermediate distance from the enhancing tumor, G2; far from the enhancing tumor, G3). The maximum rCBV ratios in the PBZ (rCBVp) and the enhancing tumor were also calculated, respectively. The perfusion parameters were evaluated using the nonparametric Mann-Whitney test. The sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve were identified.

RESULTS

The rCBVp ratios and rCBV gradient in the PBZ were significantly higher in GB compared with MET (P < 0.05 for both rCBVp ratios and rCBV gradient). The threshold values of 0.50 or greater for rCBVp ratios provide sensitivity and specificity of 57.69% and 79.17%, respectively, for differentiation of GB from MET. Compared with rCBVp ratios, rCBV gradient had higher sensitivity (94.44%) and specificity (91.67%) using the threshold value of greater than 0.06.

CONCLUSIONS

The parameter of rCBV gradient derived from DSC-MRI in the PBZ seems to be the most efficient parameter to differentiate GB from METs.

Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload

BACKGROUND AND PURPOSE

Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli.

MATERIALS AND METHODS

Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization.

RESULTS

Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration.

CONCLUSION

The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.

Evaluation of the apparent diffusion coefficient in patients with recurrent glioblastoma under treatment with bevacizumab with radiographic pseudoresponse

BACKGROUND

Response Assessment in Neuro-Oncology Criteria (RANO), are used to asses response to first-line treatment of glioblastoma (GBM). Differentiation between response and pseudoresponse under treatment with Bevacizumab (BVZ) remains challenging. This study evaluates ADC changes in patients with radiographic pseudoresponse under treatment with (BVZ).

METHODS

Patients (n=40) with recurrent GBM under-treatment with BVZ underwent MRI before, two and four months after treatment with BVZ. In patients with radiological pseudoresponse (n=11), ADC analyses were performed. Areas with decreasing T1 contrast enhancement (CE) and FLAIR signal decrease were manually selected and compared to size and position matched healthy contralateral brain parenchyma.

RESULTS

Histogram based ADC (10^-6×mm²/s) of these patients decreased significantly (P<0.005) from baseline MRI (T1-CE, FLAIR: 1124.9±160.3, 1098.4±226.2, respectively) to 2months (781.3±110.7, 783.3±103.3) and remained stable during 4months (777.0±138.5, 784.4±155.4, all mean±1 SD), despite progressive disease. Mean ADC values of the healthy contralateral brain tissue remained stable (P>0.05) (ADC values: baseline: 786.2±110.7, 2months: 781.1±76.2, 4months: 804.1±86.2).

CONCLUSION

Treatment of GBM with BVZ leads to a decrease of ADC values in areas of pre-treatment T1-CE/FLAIR signal hyperintensity to levels of comparable with normal brain tissue. ADC values remained stable, even when progressive tumor growth was reported.

In vivo assessment of tumor heterogeneity in WHO 2016 glioma grades using diffusion kurtosis imaging: Diagnostic performance and improvement of feasibility in routine clinical practice

PURPOSE

To assess the diagnostic performance of normalized and non-normalized diffusion kurtosis imaging (DKI) metrics extracted from different tumor volume data for grading glioma according to the integrated approach of the revised 2016 WHO classification.

MATERIALS AND METHODS

Sixty patients with histopathologically confirmed glioma, who provided written informed consent, were retrospectively assessed between 01/2013 and 08/2016 from a prospective trial approved by the local institutional review board. Mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were assessed by two blinded physicians from four different volumes of interest (VOI): whole solid tumor including (VOI_tu-ed) and excluding perifocal edema (VOI_tu), infiltrative zone (VOI_ed), and single slice of solid tumor core (VOI_slice). Intra-class correlation coefficient (ICC) was calculated to assess inter-rater agreement. One-way ANOVA was used to compare MK between 2016 CNS WHO tumor grades. Friedman's test compared MK and MD of each VOI. Spearman's correlation coefficient was used to correlate MK with 2016 CNS WHO tumor grades. ROC analysis was performed on MK for significant results.

RESULTS

The MK assessment showed excellent inter-rater agreement for each VOI (ICC, 0.906-0.955). MK was significantly lower in IDH_mutant astrocytoma (0.40±0.07), than in 1p/19q-confirmed oligodendroglioma (0.54±0.10, P=0.001) or IDH_wild-type glioblastoma (0.68±0.13, P<0.001). MK and 2016 WHO tumor grades were strongly and positively correlated (VOI_tu-ed, r=0.684; VOI_tu, r=0.734; VOI_ed, r=0.625; VOI_slice, r=0.698; P<0.001).

CONCLUSIONS

Non-normalized MK values obtained from VOI_tu and VOI_slice showed the best reproducibility and highest diagnostic performance for stratifying glioma according to the integrated approach of the recent 2016 WHO classification.