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Objectives: To evaluate the added value of MR dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI)-derived tumour microvascular and oxygenation information with cerebral blood volume (CBV) to distinguish pseudoprogression from true progression (TP) in post-treatment glioblastoma. Methods: This retrospective single-institution study included patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and a newly developed or enlarging measurable contrast-enhancing mass within 12 weeks after concurrent chemoradiotherapy. CBV, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) were obtained from DSC-PWI. Predictors were selected using univariable logistic regression, and performance was measured with adjusted diagnostic odds with tumour volume and area under the curve (AUC) of receiver operating characteristics analysis. Results: A total of 103 patients were included (mean age, 59.6 years; 59 women), with 67 cases of TP and 36 cases of pseudoprogression. Pseudoprogression exhibited higher CTH (4.0 vs. 3.4, p =.019) and higher OEF (12.7 vs. 10.7, p =.014) than TP, but a similar CBV (1.48 vs. 1.53, p =.13) and CMRO2 (7.7 vs. 7.3s, p =.598). Independent of tumour volume, both high CTH (adjusted odds ratio [OR] 1.52; 95% confidence interval [CI]: 1.11–2.09, p =.009) and high OEF (adjusted OR 1.17; 95% CI:1.03–1.33, p =.016) were predictors of pseudoprogression. The combination of CTH, OEF, and CBV yielded higher diagnostic performance (AUC 0.71) than CBV alone (AUC 0.65). Conclusion: High intratumoural capillary transit heterogeneity and high oxygen extraction fraction derived from DSC-PWI have enhanced the diagnostic value of CBV in pseudoprogression of post-treatment IDH-wild type glioblastoma. Clinical relevance statement: In the early post-treatment stage of glioblastoma, pseudoprogression exhibited both high oxygen extraction fraction and high capillary transit heterogeneity and these dynamic susceptibility contrast-perfusion weighted imaging derived parameters have added value in cerebral blood volume-based noninvasive differentiation of pseudoprogression from true progression. Key Points: • Capillary transit time heterogeneity and oxygen extraction fraction can be measured noninvasively through processing of dynamic susceptibility contrast imaging. • Pseudoprogression exhibited higher capillary transit time heterogeneity and higher oxygen extraction fraction than true progression. • A combination of cerebral blood volume, capillary transit time heterogeneity, and oxygen extraction fraction yielded the highest diagnostic performance (area under the curve 0.71). [ABSTRACT FROM AUTHOR]

Objectives: The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. Methods: This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. Results: SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94–0.99; p =.011) and definition 2 (HR: 0.96, 0.94–0.98, p <.001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94–0.99, p =.008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735–0.738 vs. 0.687–0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). Conclusion: SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. Key Points: • Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry. [ABSTRACT FROM AUTHOR]

Objectives: Anti-angiogenic therapy may not benefit all patients with recurrent glioblastomas, and imaging biomarker predicting treatment response to anti-angiogenic therapy is currently limited. We aimed to develop and validate vascular habitats based on perfusion and vessel size to predict time to progression (TTP) in patients with recurrent glioblastomas treated with bevacizumab. Methods: Sixty-nine patients with recurrent glioblastomas treated with bevacizumab who underwent pretreatment MRI with dynamic susceptibility contrast imaging and vessel architectural imaging were enrolled. Vascular habitats were constructed using vessel size index (VSI) and relative cerebral blood volume (rCBV). Associations with vascular habitats and TTP were analyzed using Cox proportional hazard regression analysis. In a prospectively enrolled validation cohort consisting of 15 patients (ClinicalTrials.gov identifier; NCT04143425), stratification of TTP was demonstrated by the Kaplan-Meier method (log-rank test) using vascular habitats. Results: Three vascular habitats consisting of high, intermediate, and low angiogenic habitats were identified with rCBV and VSI. Both high angiogenic and intermediate angiogenic habitats were significantly associated with a shorter TTP (hazard ratio [HR], 2.78 and 1.82, respectively; largest p =.003) and so was rCBV (HR, 2.15; p =.02). Concordance probability index of vascular habitat combining high and intermediate angiogenic habitats was 0.74. Vascular habitats stratified patients as good or poor responder in a prospective cohort (p =.059). Conclusions: Perfusion- and vessel size-derived vascular habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy and aided patient stratification in a prospective validation cohort. Trial registration: ClinicalTrials.gov identifier: NCT04143425 Key Points: • High and intermediate angiogenic habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy. • Vascular habitats combining high and intermediate angiogenic habitats aided patient stratification for anti-angiogenic therapy in recurrent glioblastomas. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified). Methods: Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data. Results: Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812). Conclusion: Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis. Key Points: • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance. [ABSTRACT FROM AUTHOR]

Objectives: Tissue conductivity measurements made with electrical properties tomography (EPT) can be used to define temporal changes in tissue habitats on longitudinal multiparametric MRI. We aimed to demonstrate the added insights for identifying tumor habitats obtained by including EPT with diffusion- and perfusion-weighted MRI, and to evaluate the use of these tumor habitats for determining tumor treatment response in post-treatment glioblastoma. Methods: Tumor habitats were developed from EPT, diffusion-weighted, and perfusion-weighted MRI in 60 patients with glioblastoma who underwent concurrent chemoradiotherapy. Voxels from EPT, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps were clustered into habitats, and each habitat was serially examined to assess its temporal change. The usefulness of temporal changes in tumor habitats for diagnosing tumor progression and treatment-related change was investigated using logistic regression. The performance of significant predictors was measured using the area under the curve (AUC) from receiver-operating-characteristics analysis with 1000-fold bootstrapping. Results: Five tumor habitats were identified, and of these, the hypervascular cellular habitat (odds ratio [OR] 5.45; 95% CI, 1.75–31.42; p =.02), hypovascular low conductivity habitat (OR 2.00; 95% CI, 1.45–3.05; p <.001), and hypovascular intermediate habitat (OR 1.57; 95% CI, 1.18–2.30; p =.006) were predictive of tumor progression. Low EPT and low CBV reflected a unique hypovascular low conductivity habitat that showed the highest diagnostic performance (AUC 0.86; 95% CI, 0.76–0.96). The combined habitats showed high performance (AUC 0.90; 95% CI, 0.82–0.98) in the differentiation of tumor progression from treatment-related change. Conclusion: EPT reveals low conductivity habitats that can improve the diagnosis of tumor progression in post-treatment glioblastoma. Key Points: • Electrical properties tomography (EPT) demonstrated lower conductivity in tumor progression than in treatment-related change. • EPT allowed identification of a unique hypovascular low conductivity habitat when combined with cerebral blood volume mapping. • Tumor habitats with a hypovascular low conductivity habitat, hypervascular cellular habitat, and hypovascular intermediate habitat yielded high diagnostic performance for diagnosing tumor progression. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop multiparametric physiologic MRI-based spatial habitats and to evaluate whether temporal changes in these habitats help to distinguish tumor progression from treatment-related change in post-treatment glioblastoma. Methods: This retrospective, single-institution study included patients with glioblastoma treated by concurrent chemoradiotherapy who had newly developed or enlarging, measurable contrast-enhancing mass. Contrast-enhancing mass was divided into three spatial habitats by K-means clustering of voxel-wise ADC and CBV values. Temporal changes of these habitats between two consecutive examinations prior to the diagnosis of tumor progression or treatment-related change were assessed. Predictors were selected using logistic regression and the performance was measured with an area under the receiver operating characteristics curve (AUC). Spatiotemporal habitats were further analyzed for correlation with the site of tumor progression. Results: There were 75 patients (mean, 58 years; range, 26−81 years; 43 men) with 48 cases of tumor progression and 39 cases of treatment-related change including 12 patient overlaps at different time points. Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. Increase in the hypervascular cellular (OR 4.55, p =.002) and hypovascular cellular habitat (OR 1.22, p <.001) was predictive of tumor progression. Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% [21/25] of cases with tumor progression. Conclusions: Temporal changes in spatial habitats derived from multiparametric physiologic MRI provided diagnostic value in distinguishing tumor progression from treatment-related change and predicted site of tumor progression in post-treatment glioblastoma. Key Points: • In post-treatment glioblastoma, three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified, and an increase in the hypervascular cellular (OR 4.55, p =.002) and hypovascular cellular habitat (OR 1.22, p <.001) was predictive of tumor progression. • Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). • An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% (21/25) of cases with tumor progression. [ABSTRACT FROM AUTHOR]

Background: Updated response assessment in neuro-oncology (RANO) does not consider peritumoral non-enhancing lesion (NEL) and baseline (residual) contrast enhancement (CE) volume. The objective of this study is to explore helpful imaging characteristics to refine RANO for assessing early treatment response (pseudoprogression and time-to-progression [TTP]) in patients with IDH wild-type glioblastoma.Methods: This retrospective study enrolled 86 patients with IDH wild-type glioblastoma who underwent consecutive MRI examinations before and after concurrent chemoradiotherapy (CCRT). NEL was classified as edema- or tumor-dominant type on pre-CCRT MRI. CE evolution was categorized into 4 patterns based on post-operative residual CE (measurable vs. non-measurable) and CE volume change (same criteria with RANO) during CCRT. Multivariable logistic regression, including clinical parameters, NEL type, and CE evolution pattern, was used to analyze pseudoprogression rate. TTP and OS according to NEL type and CE evolution pattern was analyzed by the Kaplan-Meier method.Results: Pseudoprogression rate was significantly lower (chi-square test, P = .047) and TTP was significantly shorter (hazard ratio [HR] = 2.03, P = .005) for tumor-dominant type than edema-dominant type of NEL. NEL type was the only predictive marker of pseudoprogression on multivariate analysis (odds ratio = 0.26, P = .046). Among CE evolution patterns, TTP and OS was shortest in patients with residual CE compared with those exhibiting new CE (HR = 4.33, P < 0.001 and HR = 3.71, P = .009, respectively). In edema-dominant NEL type, both TTP and OS was stratified by CE evolution pattern (log-rank, P = .001), whereas it was not in tumor-dominant NEL.Conclusions: NEL type improves prediction of pseudoprogression and, together with CE evolution pattern, further stratifies TTP and OS in patients with IDH wild-type glioblastoma and may become a helpful biomarker for refining RANO. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model. [ABSTRACT FROM AUTHOR]

Objectives: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients.Methods: A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients.Results: Reproducibility was excellent for ADC and CBV features (ICC, 0.82-0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64-0.99] vs. AUC, 0.81 [0.60-1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61-0.95] vs. AUC, 0.65 [0.46-0.84], p = 0.23).Conclusion: DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis.Key Points: • Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers. • DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma. [ABSTRACT FROM AUTHOR]

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN.. [ABSTRACT FROM AUTHOR]

We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability. [ABSTRACT FROM AUTHOR]

Background: Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.Purpose: To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.Study Type: Systematic review and meta-analysis.Subjects: Ovid-MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."Field Strength/sequence: Patients underwent at least one MRI sequence including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced imaging (DSC), dynamic contrast-enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.Assessment: Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool.Statistical Tests: Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta-regression was performed.Results: Twenty-two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87-93%) and specificity of 89% (95% CI, 85-93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90-0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89-97%] and specificity of 91% [95% CI, 86-96%]). Heterogeneity was only detected in specificity (I2 = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.Data Conclusion: MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.Level Of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:560-572. [ABSTRACT FROM AUTHOR]

Objective: To determine if perfusion in surgical cavity wall enhancement (SCWE) obtained in early post-treatment MR imaging can stratify time-to-progression (TTP) in glioblastoma. Materials and methods: This study enrolled 60 glioblastoma patients with more than 5-mm-thick SCWEs as detected on contrast-enhanced MR imaging after concurrent chemoradiation therapy. Two independent readers categorized the shape and perfusion state of SCWEs as nodular or non-nodular and as having positive or negative perfusion compared with the contralateral grey matter on arterial spin labeling (ASL). The perfusion fraction on ASL within the contrast-enhancing lesion was calculated. The independent predictability of TTP was analyzed using the Kaplan-Meier method and Cox proportional hazards modelling. Results: The perfusion fraction was higher in the non-progression group, significantly for reader 2 (P = 0.03) and borderline significantly for reader 1 (P = 0.08). A positive perfusion state and (P = 0.02) a higher perfusion fraction of the SCWE were found to become an independent predictor of longer TTP (P = 0.001 for reader 1 and P < 0.001 for reader 2). The contrast enhancement pattern did not become a TTP predictor. Conclusion: Assessment of perfusion in early post-treatment MR imaging can stratify TTP in patients with glioblastoma for adjuvant temozolomide therapy. Positive perfusion in SCWEs can become a predictor of a longer TTP. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to determine whether diffusion and perfusion imaging parameters demonstrate different diagnostic values for predicting pseudoprogression between glioblastoma subgroups stratified by O6-mythylguanine-DNA methyltransferase (MGMT) promoter methylation status.Methods: We enrolled seventy-five glioblastoma patients that had presented with enlarged contrast-enhanced lesions on magnetic resonance imaging (MRI) one month after completing concurrent chemoradiotherapy and undergoing MGMT promoter methylation testing. The imaging parameters included 10 or 90 % histogram cutoffs of apparent diffusion coefficient (ADC10), normalized cerebral blood volume (nCBV90), and initial area under the time signal-intensity curve (IAUC90). The results of the areas under the receiver operating characteristic curve (AUCs) with cross-validation were compared between MGMT methylation and unmethylation groups.Results: MR imaging parameters demonstrated a trend toward higher accuracy in the MGMT promoter methylation group than in the unmethylation group (cross-validated AUCs = 0.70-0.95 and 0.56-0.87, respectively). The combination of MGMT methylation status with imaging parameters improved the AUCs from 0.70 to 0.75-0.90 for both readers in comparison with MGMT methylation status alone. The probability of pseudoprogression was highest (95.7 %) when nCBV90 was below 4.02 in the MGMT promoter methylation group.Conclusions: MR imaging parameters could be stronger predictors of pseudoprogression in glioblastoma patients with the methylated MGMT promoter than in patients with the unmethylated MGMT promoter.Key Points: • The glioblastoma subgroup was stratified according to MGMT promoter methylation status. • Diagnostic values of diffusion and perfusion parameters for predicting pseudoprogression were compared. • Imaging parameters showed higher diagnostic accuracy in the MGMT promoter methylation group. • Imaging parameters were independent to MGMT promoter methylation status for predicting pseudoprogression. • Imaging biomarkers might demonstrate different diagnostic values according to MGMT promoter methylation. [ABSTRACT FROM AUTHOR]

Objectives: To determine the added value of amide proton transfer (APT) imaging to conventional and perfusion MRI for differentiating tumour progression (TP) from the treatment-related effect (TE) in patients with post-treatment glioblastomas.Methods: Sixty-five consecutive patients with enlarging contrast-enhancing lesions following concurrent chemoradiotherapy were assessed using contrast-enhanced T1-weighted MRI (CE-T1WI), 90th percentile histogram parameters of normalized cerebral blood volume (nCBV90) and APT asymmetry value (APT90). Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUC) and cross validations.Results: There were statistically significant differences in the mean APT90 between the TP and the TE groups (3.87-4.01 % vs. 1.38-1.41 %; P < .001). Compared with CE-T1WI alone, the addition of APT90 to CE-T1WI significantly improved cross-validated AUC from 0.58-0.74 to 0.89-0.91 for differentiating TP from TE. The combination of CE-T1WI, nCBV90 and APT90 resulted in greater diagnostic accuracy for differentiating TP from TE than the combination of CE-T1WI and nCBV90 (cross-validated AUC, 0.95-0.97 vs. 0.84-0.91). The inter-reader agreement between the expert and trainee was excellent for the measurements of APT90 (intraclass correlation coefficient, 0.94).Conclusion: Adding APT imaging to conventional and perfusion MRI improves the diagnostic performance for differentiating TP from TE.Key Points: • APT imaging could provide a reliable distinction between TP and TE • Adding APT imaging to CE-T1WI improved the diagnostic accuracy versus CE-T1WI alone • Multimodal imaging using CE-T1WI, perfusion and APT imaging led to accurate diagnosis • The inter-reader agreement of APT histogram parameters was excellent. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to evaluate the accuracy of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) perfusion MR imaging for distinguishing tumor recurrence from post-treatment effect as alternatives to dynamic-susceptibility contrast-enhanced (DSC) perfusion MR imaging when the DSC image is uninterpretable. Materials and Methods: This retrospective study was approved by our institutional review board. Seventy one post-treatment glioblastoma patients who showed enlarged contrast-enhancing lesions on follow-up MR images after concurrent chemoradiotherapy and uninterpretable DSC images for corresponding enhancing lesions, underwent additional DWI and DCE MR imaging. The primary outcome was the frequency of interpretable DWI and DCE MR cases in these 71 patients. The secondary outcome was the area under the receiver operating characteristic curve (AUC) of DWI and DCE imaging parameters for distinguishing tumor recurrence from post-treatment effect in selected patients with interpretable DWI and DCE images. The imaging parameters were quantified as 10% cumulative histogram cutoff of apparent diffusion coefficient (ADC10) and 90% cumulative histogram cutoff of initial area under the time signal intensity curve (IAUC90). The AUCs were cross-validated by using leave-one-out method. Results: Of the 71 patients, the uninterpretable DSC images were associated with treatment-related hemorrhage within the corresponding enhancing lesions (n = 54, 76.1%) and a near skull base location (n = 17, 23.9%). The frequencies of interpretable DWI and DCE image were 51 (71.8%) and 59 (83.1%) of the 71 cases with uninterpretable DSC images, respectively. Of the 45 selected patients with interpretable DWI and DCE images, the combination of DWI with DCE imaging showed a superior diagnostic performance than DWI or DCE imaging alone for differentiating tumor recurrence from post-treatment effect (cross-validated AUC: 0.78 versus 0.55 and 0.73 for reader 1; cross-validated AUC: 0.78 versus 0.53 and 0.75 for reader 2, respectively). Cross-validated accuracy of the single and combined imaging parameters also showed the highest for the combination of DWI with DCE MR imaging (72.9% for reader 1; 72.5% for reader 2) and the lowest for DWI alone (54.0% for reader 1; 56.4% for reader 2). Inter-reader agreement for DCE imaging was higher than that for DWI (intraclass correlation coefficient: 0.95 versus 0.87). Conclusion: DCE MR imaging could be a superior and more reproducible imaging biomarker than DWI for differentiating tumor recurrence from post-treatment effect in patients with post-treatment glioblastoma when DSC MR images are not interpretable. [ABSTRACT FROM AUTHOR]

Background and Purpose: Brain tumor cellularity has been assessed by using apparent diffusion coefficient (ADC). However, the ADC value might be influenced by both perfusion and true molecular diffusion, and the perfusion effect on ADC can limit the reliability of ADC in the characterization of tumor cellularity, especially, in hypervascular brain tumors. In contrast, the IVIM technique estimates parameter values for diffusion and perfusion effects separately. The purpose of our study was to compare ADC and IVIM for differentiating among glioblastoma, metastatic tumor, and primary CNS lymphoma (PCNSL) focusing on diffusion-related parameter. Materials and Methods: We retrospectively reviewed the data of 128 patients with pathologically confirmed glioblastoma (n = 55), metastasis (n = 31), and PCNSL (n = 42) prior to any treatment. Two neuroradiologists independently calculated the maximum IVIM-f (fmax) and minimum IVIM-D (Dmin) by using 16 different b-values with a bi-exponential fitting of diffusion signal decay, minimum ADC (ADCmin) by using 0 and 1000 b-values with a mono-exponential fitting and maximum normalized cerebral blood volume (nCBVmax). The differences in fmax, Dmin, nCBVmax, and ADCmin among the three tumor pathologies were determined by one-way ANOVA with multiple comparisons. The fmax and Dmin were correlated to the corresponding nCBV and ADC using partial correlation analysis, respectively. Results: Using a mono-exponential fitting of diffusion signal decay, the mean ADCmin was significantly lower in PCNSL than in glioblastoma and metastasis. However, using a bi-exponential fitting, the mean Dmin did not significantly differ in the three groups. The mean fmax significantly increased in the glioblastomas (reader 1, 0.103; reader 2, 0.109) and the metastasis (reader 1, 0.105; reader 2, 0.107), compared to the primary CNS lymphomas (reader 1, 0.025; reader 2, 0.023) (P < .001 for each). The correlation between fmax and the corresponding nCBV was highest in glioblastoma group, and the correlation between Dmin and the corresponding ADC was highest in primary CNS lymphomas group. Conclusion: Unlike ADC value derived from a mono-exponential fitting of diffusion signal, diffusion-related parametric value derived from a bi-exponential fitting with separation of perfusion effect doesn’t differ among glioblastoma, metastasis, and PCNSL. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s00330-021-08065-8 [ABSTRACT FROM AUTHOR]