Incidence of high grade gliomas presenting as radiographically non-enhancing lesions: experience in 111 surgically treated non-enhancing gliomas with tissue diagnosis

Dynamic glucose enhanced imaging using direct water saturation

PURPOSE

Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based linewidth (LW) broadening of the direct water saturation (DS) curve of the water saturation spectrum (Z-spectrum) during and after glucose infusion (DS-DGE MRI).

METHODS

To estimate the glucose-infusion-induced LW changes (ΔLW), Bloch-McConnell simulations were performed for normoglycemia and hyperglycemia in blood, gray matter (GM), white matter (WM), CSF, and malignant tumor tissue. Whole-brain DS-DGE imaging was implemented at 3 T using dynamic Z-spectral acquisitions (1.2 s per offset frequency, 38 s per spectrum) and assessed on four brain tumor patients using infusion of 35 g of D-glucose. To assess ΔLW, a deep learning-based Lorentzian fitting approach was used on voxel-based DS spectra acquired before, during, and post-infusion. Area-under-the-curve (AUC) images, obtained from the dynamic ΔLW time curves, were compared qualitatively to perfusion-weighted imaging parametric maps.

RESULTS

In simulations, ΔLW was 1.3%, 0.30%, 0.29/0.34%, 7.5%, and 13% in arterial blood, venous blood, GM/WM, malignant tumor tissue, and CSF, respectively. In vivo, ΔLW was approximately 1% in GM/WM, 5% to 20% for different tumor types, and 40% in CSF. The resulting DS-DGE AUC maps clearly outlined lesion areas.

CONCLUSIONS

DS-DGE MRI is highly promising for assessing D-glucose uptake. Initial results in brain tumor patients show high-quality AUC maps of glucose-induced line broadening and DGE-based lesion enhancement similar and/or complementary to perfusion-weighted imaging.

Preoperative prediction of diffuse glioma type and grade in adults: a gadolinium-free MRI-based decision tree

OBJECTIVES

To develop a gadolinium-free MRI-based diagnosis prediction decision tree (DPDT) for adult-type diffuse gliomas and to assess the added value of gadolinium-based contrast agent (GBCA) enhanced images.

MATERIALS AND METHODS

This study included preoperative grade 2-4 adult-type diffuse gliomas (World Health Organization 2021) scanned between 2010 and 2021. The DPDT, incorporating eleven GBCA-free MRI features, was developed using 18% of the dataset based on consensus readings. Diagnosis predictions involved grade (grade 2 vs. grade 3/4) and molecular status (isocitrate dehydrogenase (IDH) and 1p/19q). GBCA-free diagnosis was predicted using DPDT, while GBCA-enhanced diagnosis included post-contrast images. The accuracy of these predictions was assessed by three raters with varying experience levels in neuroradiology using the test dataset. Agreement analyses were applied to evaluate the prediction performance/reproducibility.

RESULTS

The test dataset included 303 patients (age (SD): 56.7 (14.2) years, female/male: 114/189, low-grade/high-grade: 54/249, IDH-mutant/wildtype: 82/221, 1p/19q-codeleted/intact: 34/269). Per-rater GBCA-free predictions achieved ≥ 0.85 (95%-CI: 0.80-0.88) accuracy for grade and ≥ 0.75 (95%-CI: 0.70-0.80) for molecular status, while GBCA-enhanced predictions reached ≥ 0.87 (95%-CI: 0.82-0.90) and ≥ 0.77 (95%-CI: 0.71-0.81), respectively. No accuracy difference was observed between GBCA-free and GBCA-enhanced predictions. Group inter-rater agreement was moderate for GBCA-free (0.56 (95%-CI: 0.46-0.66)) and substantial for GBCA-enhanced grade prediction (0.68 (95%-CI: 0.58-0.78), p = 0.008), while substantial for both GBCA-free (0.75 (95%-CI: 0.69-0.80) and GBCA-enhanced (0.77 (95%-CI: 0.71-0.82), p = 0.51) molecular status predictions.

CONCLUSION

The proposed GBCA-free diagnosis prediction decision tree performed well, with GBCA-enhanced images adding little to the preoperative diagnostic accuracy of adult-type diffuse gliomas.

KEY POINTS

Question Given health and environmental concerns, is there a gadolinium-free imaging protocol to preoperatively evaluate gliomas comparable to the gadolinium-enhanced standard practice? Findings The proposed gadolinium-free diagnosis prediction decision tree for adult-type diffuse gliomas performed well, and gadolinium-enhanced MRI demonstrated only limited improvement in diagnostic accuracy. Clinical relevance Even inexperienced raters effectively classified adult-type diffuse gliomas using the gadolinium-free diagnosis prediction decision tree, which, until further validation, can be used alongside gadolinium-enhanced images to respect standard practice, despite this study showing that gadolinium-enhanced images hardly improved diagnostic accuracy.

Establishment and validation of a nomogram for predicting IDH-wildtype glioblastomas in nonenhancing adult-type diffuse gliomas

Background

Approximately 8.94%-44.44% of nonenhancing adult-type diffuse gliomas are identified as glioblastomas. Our purpose is to develop a nomogram that can predict glioblastomas from nonenhancing adult-type diffuse gliomas.

Methods

Nonenhancing adult-type diffuse gliomas were collected from Beijing Tiantan Hospital and TCIA public database. Univariate and multivariate logistic regression were performed to screen features on the training set. The features with P < .05 in multivariate logistic regression were used to establish the prediction model. The testing and validation sets were used to test the model.

Results

A total of 557 and 67 nonenhancing adult-type diffuse gliomas were collected from Beijing Tiantan Hospital and TCIA, respectively. The T2-FLAIR mismatch sign exhibited 100% specificity but low sensitivity (<30%) in ruling out glioblastoma. Age, tumor location, rADC(kurtosis), and rADC(median) were identified as independent predictors and employed for developing the prediction model. The AUC of the model was 0.901, 0.861, and 0.945 in the training, testing, and validation set, respectively. The best cutoff value of nomoscore was 138.5, which achieved sensitivity of 0.935, 0.714, and 0.895, specificity of 0.777, 0.782, and 0.8775 in the training, testing, and validation sets, respectively. Survival analysis shown that patients with nomoscore above 138.5 had significantly poorer survival time than those with scores below 138.5.

Conclusions

Positive T2-FLAIR mismatch sign can effectively rule out glioblastoma in nonenhancing adult-type diffuse gliomas with high specificity. Nonenhancing adult-type diffuse gliomas with nomoscore above 138.5 are highly suspicious for glioblastoma or nonglioblastoma with a poor prognosis.

Mechanical properties of pediatric low-grade gliomas in children with and without neurofibromatosis type 1

INTRODUCTION

Prognoses for pediatric brain tumors are suboptimal, as even in low-grade tumors, management techniques can lead to damage in the developing brain. Therefore, advanced neuroimaging methods are critical for developing optimal management plans and improving patient care. Magnetic resonance elastography (MRE) has allowed for the characterization of adult gliomas by their mechanical properties, which are uniquely sensitive to the complex interplay of cellularity, vasculature, and interstitium. However, pediatric tumors differ in behavior and cytoarchitecture, and their mechanical properties have never been assessed.

METHODS

Here, we conduct the first study of pediatric brain tumor mechanical properties by using MRE to measure tissue stiffness and damping ratio in low grade gliomas (LGGs). We additionally measure the mechanical properties of non-neoplastic focal abnormal signal intensities (FASIs) in children with neurofibromatosis type 1 (NF1).

RESULTS

23 patients age 4-17 years who had MR imaging results consistent with a primary LGG or with NF1 were included in this study. We found that pediatric gliomas are on an average 10.9% softer (p = 0.010) with a 17.3% lower (p = 0.009) viscosity than reference tissue. Softness of tumors appeared consistent across tumor subtypes and unrelated to tumor size or contrast-enhancement. In NF1 we found that, unlike gliomas, FASIs are stiffer, though not significantly, than reference tissue by an average of 10.4% and have a 16.7% lower damping ratio.

CONCLUSIONS

Measuring tumor mechanical properties patterning and heterogeneity has potential to aid in prediction of biological behavior and inform management strategies for pediatric patients.

Dose-Painting Proton Radiotherapy Guided by Functional MRI in Non-enhancing High-Grade Gliomas

AIMS

This study aimed to demonstrate the feasibility and evaluate the dosimetric effect and clinical impact of dose-painting proton radiotherapy (PRT) guided by functional MRI in non-enhancing high-grade gliomas (NE-HGGs).

MATERIALS AND METHODS

The 3D-ASL and T2 FLAIR MR images of ten patients with NE-HGGs before radiotherapy were studied retrospectively. The hyperintensity on T2 FLAIR was used to generate the planning target volume (PTV), and the high-perfusion volume on 3D-ASL (PTV-ASL) was used to generate the simultaneous integrated boost (SIB) volume. Each patient received pencil beam scanning PRT and photon intensity-modulated radiotherapy (IMRT). There were five plans in each modality: (1) Uniform plans (IMRT60 vs. PRT60): 60Gy in 30 fractions to the PTV. (2)-(5) SIB plans (IMRT72, 84, 96, 108 vs. PRT72, 84, 96, 108): Uniform plan plus additional dose boost to PTV-ASL in 30 fractions to 72, 84, 96, 108 Gy. The dosimetric differences between various plans were compared. The clinical effects of target volume and organs at risk (OARs) were assessed using biological models for both tumor control probability (TCP) and normal tissue complication probability (NTCP).

RESULTS

Compared with the IMRT plan, the D2 and D50 of the PRT plans with the same prescription dose increased by 1.27-4.12% and 0.64-2.01%, respectively; the R30 decreased by > 32%; the dose of brainstem and chiasma decreased by > 27% and >32%; and the dose of normal brain tissue (Br-PTV), optic nerves, eyeballs, lens, cochlea, spinal cord, and hippocampus decreased by > 50% (P < 0.05). The maximum necessary dose was 96GyE to achieve >98% TCP for PRT, and it was 84Gy to achieve >91% TCP for IMRT. The average NTCP of Br-PTV was 1.30% and 1.90% for PRT and IMRT at the maximum dose escalation, respectively. The NTCP values of the remaining OARs approached zero in all PRT plans.

CONCLUSION

The functional MRI-guided dose escalation using PRT is feasible while sparing the OARs constraints and demonstrates a potential clinical benefit by improving TCP with no or minimal increase in NCTP for tissues outside the PTV. This retrospective study suggested that the use of PRT-based SIB guided by functional MRI may represent a strategy to provide benefits for patients with NE-HGGs.

MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition

Background

The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas.

Methods

Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set.

Results

The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas.

Conclusions

Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification

BACKGROUND

The 2021 World Health Organization (WHO) classification considers a histological low grade glioma with specific molecular characteristics as molecular glioblastoma (mGBM). Accurate identification of mGBM will aid in risk stratification of glioma patients.

PURPOSE

To explore the value of machine learning models based on magnetic resonance imaging (MRI) radiomics features in predicting mGBM.

MATERIAL AND METHODS

In total, 166 patients histologically diagnosed as low-grade diffuse glioma (WHO II and III) were included in the study. Fifty-three cases were reclassified as mGBM based on molecular status. Four dimensionality reduction methods including distance correlation (DC), gradient boosted decision tree (GBDT), least absolute shrinkage and selection operator (LASSO) and minimal redundancy maximal relevance (MRMR) were used to select the optimal signatures. Six machine learning algorithms including support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), logistic regression (LR), K-nearest neighbour (KNN) and decision tree (DT) were used to develop the classifiers. The relative SD was used to evaluate the stability of the models, and the area under the curve values in the independent test group were used to evaluate their performances.

RESULTS

NN_DC was determined as the optimal classifier due to the highest area under the curve of 0.891 in the test group. The classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value of NN_DC were 0.915, 0.842, 0.950, 0.889 and 0.927, respectively.

CONCLUSION

Machine learning models can predict mGBM non-invasively, which may help to develop personalized treatment strategies for neurosurgeons and provide an effective tool for accurate stratification in clinical trials.

Risk Estimation in Non-Enhancing Glioma: Introducing a Clinical Score

The preoperative grading of non-enhancing glioma (NEG) remains challenging. Herein, we analyzed clinical and magnetic resonance imaging (MRI) features to predict malignancy in NEG according to the 2021 WHO classification and developed a clinical score, facilitating risk estimation. A discovery cohort (2012-2017, n = 72) was analyzed for MRI and clinical features (T2/FLAIR mismatch sign, subventricular zone (SVZ) involvement, tumor volume, growth rate, age, Pignatti score, and symptoms). Despite a "low-grade" appearance on MRI, 81% of patients were classified as WHO grade 3 or 4. Malignancy was then stratified by: (1) WHO grade (WHO grade 2 vs. WHO grade 3 + 4) and (2) molecular criteria (IDH^mut WHO grade 2 + 3 vs. IDH^wt glioblastoma + IDH^mut astrocytoma WHO grade 4). Age, Pignatti score, SVZ involvement, and T2/FLAIR mismatch sign predicted malignancy only when considering molecular criteria, including IDH mutation and CDKN2A/B deletion status. A multivariate regression confirmed age and T2/FLAIR mismatch sign as independent predictors (p = 0.0009; p = 0.011). A "risk estimation in non-enhancing glioma" (RENEG) score was derived and tested in a validation cohort (2018-2019, n = 40), yielding a higher predictive value than the Pignatti score or the T2/FLAIR mismatch sign (AUC of receiver operating characteristics = 0.89). The prevalence of malignant glioma was high in this series of NEGs, supporting an upfront diagnosis and treatment approach. A clinical score with robust test performance was developed that identifies patients at risk for malignancy.

Therapy Defining at Initial Diagnosis of Primary Brain Tumor-The Role of 18F-FET PET/CT and MRI

Primary malignant brain tumors are heterogeneous and infrequent neoplasms. Their classification, therapeutic regimen and prognosis have undergone significant development requiring the innovation of an imaging diagnostic. The performance of enhanced magnetic resonance imaging depends on blood-brain barrier function. Several studies have demonstrated the advantages of static and dynamic amino acid PET/CT providing accurate metabolic status in the neurooncological setting. The aim of our single-center retrospective study was to test the primary diagnostic role of amino acid PET/CT compared to enhanced MRI. Emphasis was placed on cases prior to intervention, therefore, a certain natural bias was inevitable. In our analysis for newly found brain tumors ¹⁸F-FET PET/CT outperformed contrast MRI and PWI in terms of sensitivity and negative predictive value (100% vs. 52.9% and 36.36%; 100% vs. 38.46% and 41.67%), in terms of positive predictive value their performance was roughly the same (84.21 % vs. 90% and 100%), whereas regarding specificity contrast MRI and PWI were superior (40% vs. 83.33% and 100%). Based on these results the superiority of ¹⁸F-FET PET/CT seems to present incremental value during the initial diagnosis. In the case of non-enhancing tumors, it should always be suggested as a therapy-determining test.

Three-dimensional arterial spin labeling-guided dose painting radiotherapy for non-enhancing low-grade gliomas

PURPOSE

To investigate the feasibility and dosimetric characteristics of dose painting for non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL).

MATERIALS AND METHODS

Eighteen patients with NE-LGGs were enrolled. 3D-ASL, T2 fluid-attenuated inversion recovery (T2 Flair) and contrast-enhanced T1-weighted magnetic resonance images were obtained. The gross tumor volume (GTV) was delineated on the T2 Flair. The hyper-perfusion region of the GTV (GTV-ASL) was determined by 3D-ASL, and the GTV-SUB was obtained by subtracting the GTV-ASL from the GTV. The clinical target volume (CTV) was created by iso-tropically expanding the GTV by 1 cm. The planning target volume (PTV), PTV-ASL were obtained by expanding the external margins of the CTV, GTV-ASL, respectively. PTV-SUB was generated by subtracting PTV-ASL from PTV. Three plans were generated for each patient: a conventional plan (plan 1) without dose escalation delivering 95-110% of 45-60 Gy in 1.8-2 Gy fractions to the PTV and two dose-painting plans (plan 2 and plan 3) with dose escalating by 10-20% (range, 50-72 Gy) to the PTV-ASL based on plan 1. The plan 3 was obtained from plan 2 without the maximum dose constraint. The dosimetric differences among the three plans were compared.

RESULTS

The volume ratio of the PTV-ASL to the PTV was (23.49 ± 11.94)% (Z = - 3.724, P = 0.000). Compared with plan 1, D_2%, D_98% and D_mean of PTV-ASL increased by 14.67%,16.17% and 14.31% in plan2 and 19.84%,15.52% and 14.27% in plan3, respectively (P < 0.05); the D_2% of the PTV and PTV-SUB increased by 11.89% and 8.34% in plan 2, 15.89% and 8.49% in plan 3, respectively (P < 0.05). The PTV coverages were comparable among the three plans (P > 0.05). In plan 2 and plan 3, the conformity indexes decreased by 18.60% and 12.79%; while the homogeneity index increased by 1.43 and 2 times (P < 0.05). Compared with plan 1, the D_0.1 cc of brain stem and D_max of optic chiasma were slightly increased in plan 2 and plan 3, and the absolute doses met the dose constraint. The doses of the other organs at risk (OARs) were similar among the three plans (P > 0.05).

CONCLUSION

The dose delivered to hyper-perfusion volume derived from 3D-ASL can increased by 10-20% while respecting the constraints to the OARs for NE-LGGs, which provides a basis for future individualized and precise radiotherapy, especially if the contrast agent cannot be injected or when contrast enhancement is uncertain.

Clinical applications and prospects of PET imaging in patients with IDH-mutant gliomas

PET imaging using radiolabeled amino acids in addition to MRI has become a valuable diagnostic tool in the clinical management of patients with brain tumors. This review provides a comprehensive overview of PET studies in glioma patients with a mutation in the isocitrate dehydrogenase gene (IDH). A considerable fraction of these tumors typically show no contrast enhancement on MRI, especially when classified as grade 2 according to the World Health Organization classification of Central Nervous System tumors. Major diagnostic challenges in this situation are differential diagnosis, target definition for diagnostic biopsies, delineation of glioma extent for treatment planning, differentiation of treatment-related changes from tumor progression, and the evaluation of response to alkylating agents. The main focus of this review is the role of amino acid PET in this setting. Furthermore, in light of clinical trials using IDH inhibitors targeting the mutated IDH enzyme for treating patients with IDH-mutant gliomas, we also aim to give an outlook on PET probes specifically targeting the IDH mutation, which appear potentially helpful for response assessment.

Acute progression of cerebral amyloid angiopathy-related inflammation diagnosed by biopsy in an elderly patient: A case report

Background:

Cerebral amyloid angiopathy-related inflammation (CAA-I) presents with slowly progressive nonspecific neurological symptoms, such as headache, cognitive function disorder, and seizures. Pathologically, the deposition of amyloid-β proteins at the cortical vascular wall is a characteristic and definitive finding. Differential diagnoses include infectious encephalitis, neurosarcoidosis, primary central nervous system lymphoma, and glioma. Here, we report a case of CAA-I showing acute progression, suggesting a glioma without enhancement, in which a radiological diagnosis was difficult using standard magnetic resonance imaging.

Case Description:

An 80-year-old woman was admitted due to transient abnormal behavior. Her initial imaging findings were similar to those of a glioma. She presented with rapid progression of the left hemiplegia and disturbance of consciousness for 6 days after admission and underwent emergent biopsy with a targeted small craniotomy under general anesthesia despite her old age. Intraoperative macroscopic findings followed by a pathological study revealed CAA-I as the definitive diagnosis. Steroid pulse therapy with methylprednisolone followed by oral prednisolone markedly improved both the clinical symptoms and imaging findings.

Conclusion:

Differential diagnosis between CAA-I and nonenhancing gliomas may be difficult using standard imaging studies in cases presenting with acute progression. A pathological diagnosis under minimally invasive small craniotomy may be an option, even for elderly patients.

Noninvasive differentiation of molecular subtypes of adult non-enhancing glioma using MRI perfusion and diffusion parameters

Background

Nonenhancing glioma typically have a favorable outcome, but approximately 19–44% have a highly aggressive course due to a glioblastoma genetic profile. The aim of this retrospective study is to use physiological MRI parameters of both perfusion and diffusion to distinguish the molecular profiles of glioma without enhancement at presentation.

Methods

Ninety-nine patients with nonenhancing glioma were included, in whom molecular status (including 1p/19q codeletion status and IDH mutation) and preoperative MRI (T2w/FLAIR, dynamic susceptibility-weighted, and diffusion-weighted imaging) were available. Tumors were segmented semiautomatically using ITK-SNAP to derive whole tumor histograms of relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC). Tumors were divided into three clinically relevant molecular profiles: IDH mutation (IDHmt) with (n = 40) or without (n = 41) 1p/19q codeletion, and (n = 18) IDH-wildtype (IDHwt). ANOVA, Kruskal-Wallis, and Chi-Square analyses were performed using SPSS.

Results

rCBV (mean, median, 75^th and 85^th percentile) and ADC (mean, median, 15^th and 25^th percentile) showed significant differences across molecular profiles (P < .01). Posthoc analyses revealed that IDHwt and IDHmt 1p/19q codeleted tumors showed significantly higher rCBV compared to IDHmt 1p/19q intact tumors: mean rCBV (mean, SD) 1.46 (0.59) and 1.35 (0.39) versus 1.08 (0.31), P < .05. Also, IDHwt tumors showed significantly lower ADC compared to IDHmt 1p/19q codeleted and IDHmt 1p/19q intact tumors: mean ADC (mean, SD) 1.13 (0.23) versus 1.27 (0.15) and 1.45 (0.20), P < .001).

Conclusions

A combination of low ADC and high rCBV, reflecting high cellularity and high perfusion respectively, separates IDHwt from in particular IDHmt 1p/19q intact glioma.

The diagnostic value of contrast enhancement on MRI in diffuse and anaplastic gliomas

PURPOSE

We evaluated differentiations in gadolinium contrast enhancement (CE) between low-grade WHO °II and high-grade WHO °III gliomas in conventional MRI, which have been repeatedly questioned.

METHODS

Ninety-nine patients, who underwent first resection of WHO°II and °III gliomas, were retrospectively retrieved from a prospective database. The quantitative metric volume of Gd-CE in T1-weighted pre-operative MRI was measured using volumetric segmentation.

RESULTS

The OR to detect CE in anaplastic gliomas was seven times higher than that in diffuse gliomas (CI95% 2.8-17.2, p<0.0001). No CE was seen in 50% (8/16) of focal anaplastic and in 28% (10/36) of entirely anaplastic gliomas. CE was present in 21% (10/47) of diffuse gliomas. Anaplasia correlated with a larger CE volume (r=0.49, p<0.0001) and provided additional 4 cm³ of CE volume compared to entirely diffuse tumors. The OR to have CE was 3.6 times for IDH1 wild-type tumors (CI95% 1.3-10.2, p=0.05) and 4.8 for tumors with ATRX expression (CI95% 1.3-17.2, p=0.05). In all sub-groups, at least a quarter of cases showed no CE at all and there were cases with present CE.

CONCLUSION

CE is associated with higher odds of unfavorable prognostic features like anaplasia, wild-type IDH1 and retained ATRX. There was no CE in one-fourth of anaplastic gliomas and half of gliomas with focal anaplasia.

Can High b Value Diffusion Be a Surrogate Marker for PET-A MR/PET Study in Neurooncology Set Up

Purpose: Hybrid whole-body magnetic resonance/positron emission tomography (MR/PET) systems are new diagnostic tools enabling the simultaneous acquisition of morphologic and multiparametric functional data, which allow for a diversified characterization of oncological diseases. This study aimed to compare the diagnostic ability of MRI with the diffusion-weighted image (DWI), and simultaneous integrated positron emission tomography MR/PET to detect malignant lesions and elucidate the utility and limitations of these imaging modalities in preoperative and postoperative follow up in cancer patients. Material and Methods: A total of 45 patients undergoing simultaneous MR/PET for CNS ICSOL in our institution between January 2016 and July 2020 were considered in this study. Post-processing was done in Siemens syngo software to generate a b2000 image. This image was then inverted to grayscale and compared with the NAC image of PET. Results: The lesion-based sensitivity, specificity, positive predictive value, and negative predictive value for DWI were 92.3, 83.3, 97.3, and 62.5%, respectively (at 95% CI and p was 0.000). The lesion-based sensitivity, specificity, positive predictive value, and negative predictive value for PET were 97.4, 71.4, 94.9, and 83.3%, respectively (at 95% CI and p was 0.000). The lesion-based sensitivity and specificity of DWI were comparable with those of PET. Conclusions: Although DWI and FDG-PET reflect different tissue properties, there is an association between the measures of both methods in CNS tumors probably because of the coupling of cellularity with tumor metabolism as seen on FDG and other PET tracers. Our study shows that DWI acts as a surrogate biomarker for FDG PET and other tracers in tumors. The method of DWI image generation is simple, radiation-free, and cost-effective in a clinical setup. The simultaneous DWI-PET study provides evidence and confirms the role of DWI in surveillance imaging of tumors.

DCE-MRI in Glioma, Infiltration Zone and Healthy Brain to Assess Angiogenesis: A Biopsy Study

Purpose

To explore the focal predictability of vascular growth factor expression and neovascularization using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in glioma.

Methods

120 brain biopsies were taken in vital tumor, infiltration zone and normal brain tissue of 30 glioma patients: 17 IDH(isocitrate dehydrogenase)-wildtype glioblastoma (GBM), 1 IDH-wildtype astrocytoma °III (together prognostic group 1), 3 IDH-mutated GBM (group 2), 3 anaplastic astrocytomas IDH-mutated (group 3), 4 anaplastic oligodendrogliomas and 2 low-grade oligodendrogliomas (together prognostic group 4). A mixed linear model evaluated the predictabilities of microvessel density (MVD), vascular area ratio (VAR), mean vessel size (MVS), vascular endothelial growth factor and receptors (VEGF-A, VEGFR‑2) and vascular endothelial-protein tyrosine phosphatase (VE-PTP) expression from Tofts model kinetic and model-free curve parameters.

Results

All kinetic parameters were associated with VEGF‑A (all p < 0.001) expression. K^trans, k_ep and v_e were associated with VAR (p = 0.006, 0.004 and 0.01, respectively) and MVS (p = 0.0001, 0.02 and 0.003, respectively) but not MVD (p = 0.84, 0.74 and 0.73, respectively). Prognostic groups differed in K^trans (p = 0.007) and v_e (p = 0.004) values measured in the infiltration zone. Despite significant differences of VAR, MVS, VEGF‑A, VEGFR‑2, and VE-PTP in vital tumor tissue and the infiltration zone (p = 0.0001 for all), there was no significant difference between kinetic parameters measured in these zones.

Conclusion

The DCE-MRI kinetic parameters show correlations with microvascular parameters in vital tissue and also reveal blood-brain barrier abnormalities in the infiltration zones adequate to differentiate glioma prognostic groups.

Supplementary Information

The online version of this article (10.1007/s00062-021-01015-3) contains supplementary material, which is available to authorized users.