Multi-pool chemical exchange saturation transfer MRI in glioma grading, molecular subtyping and evaluating tumor proliferation.

PURPOSE: To evaluate the performance of multi-pool Chemical exchange saturation transfer (CEST) MRI in prediction of glioma grade, isocitrate dehydrogenase (IDH) mutation, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss and Ki-67 labeling index (LI), based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5). METHODS: 95 patients with adult-type diffuse gliomas were analyzed. The amide, direct water saturation (DS), nuclear Overhauser enhancement (NOE), semi-solid magnetization transfer (MT) and amine signals were derived using Lorentzian fitting, and asymmetry-based amide proton transfer-weighted (APTwasym) signal was calculated. The mean value of tumor region was measured and intergroup differences were estimated using student-t test. The receiver operating curve (ROC) and area under the curve (AUC) analysis were used to evaluate the diagnostic performance of signals and their combinations. Spearman correlation analysis was performed to evaluate tumor proliferation. RESULTS: The amide and DS signals were significantly higher in high-grade gliomas compared to low-grade gliomas, as well as in IDH-wildtype gliomas compared to IDH-mutant gliomas (all p < 0.001). The DS, MT and amine signals showed significantly differences between ATRX loss and retention in grade 2/3 IDH-mutant gliomas (all p < 0.05). The combination of signals showed the highest AUC in prediction of grade (0.857), IDH mutation (0.814) and ATRX loss (0.769). Additionally, the amide and DS signals were positively correlated with Ki-67 LI (both p < 0.001). CONCLUSION: Multi-pool CEST MRI demonstrated good potential to predict glioma grade, IDH mutation, ATRX loss and Ki-67 LI.

The impact of paramagnetic rim lesions on cortical thickness and gray to white matter contrast in relapsing-remitting multiple sclerosis.

Background: Paramagnetic rim lesions (PRLs) on susceptibility magnetic resonance sequences have been suggested as an imaging marker of disease progression in multiple sclerosis. This retrospective cross-sectional study aimed to investigate the impact of PRLs on cortical thickness and gray matter (GM) to white matter (WM) contrast in relapsing-remitting multiple sclerosis (RRMS). Methods: A total of 82 RRMS patients (40 patients with at least 1 PRL and 42 patients without PRL) and 43 healthy controls (HC) were included in this study. The T1-weighted images (T1WI) were processed with the FreeSurfer pipeline. GM to WM signal intensity ratio (GWR) was obtained from T1WI by dividing the GM signal intensity by the WM signal intensity for each vertex. Group differences in cortical thickness and GWR were tested on reconstructed cortical surface. Results: Compared to HC, patients with PRL had thinner mean cortical thickness (P<0.001), higher mean GWR (P=0.001), and lower brain structure volumes (cortex volume, P=0.001; WM volume, P<0.001; deep GM volume, P<0.001). Vertex-based analysis found significant cortical thinning in several regions and increased GWR in a wider range of regions in patients with PRL. The two types of clusters had both overlapping regions and independent regions. However, in patients without PRL, only a few regions showed significant cortical thickness changes. Correlation analysis found that in patients with PRL, only PRL volume showed a significant negative correlation with mean cortical thickness (P=0.048), and PRL volume and count, non-PRL count, and total lesion volume were significantly and positively correlated with mean GWR (P<0.05). Conclusions: There were significant changes in cortical thickness, GWR, and brain structure volume in RRMS patients with PRL that may contribute to further understanding of the pathological mechanisms underlying neurological tissue damage.

Diffusion along the perivascular space as a potential biomarker for glioma grading and isocitrate dehydrogenase 1 mutation status prediction.

Background: The diffusion tensor image analysis along the perivascular space (DTI-ALPS) may have the potential to reflect glymphatic dysfunction in patients with glioma. The study aimed to determine the correlation of DTI-ALPS with glioma grade and isocitrate dehydrogenase 1 (IDH1) genotype and to then compare the ALPS index with other diffusion metrics. Methods: In this study, 81 patients with glioma and 31 healthy controls underwent magnetic resonance imaging (MRI) examination. The ALPS-index, fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) were calculated. Comparisons were made between the left and right hemispheres and between patients and controls. IDH1 status was compared after age adjustment. The diagnostic performance of each metric was assessed via receiver operating characteristic (ROC) analysis. Results: In patients with glioma, the ALPS-index of the hemisphere ipsilateral to glioma was significantly lower than that of the hemisphere contralateral to glioma (1.417±0.177 vs. 1.478±0.165; P=0.002), and the bilateral ALPS-index values in patients were significantly decreased compared with those in healthy controls. The ALPS-index was significantly higher in lower-grade gliomas (LrGGs) than that in glioblastomas (GBMs) (1.495±0.151 vs. 1.320±0.159; P<0.001) and was significantly lower in IDH1-wild-type LrGGs than in IDH1-mutant LrGGs (1.400±0.185 vs. 1.530±0.123; P=0.036). FA, MD, and MK also showed significant differences between LrGGs and GBMs and between IDH1-mutant and IDH1-wild-type LrGGs (P<0.05). Furthermore, the combination of the ALPS-index with FA, MD, or MK, exhibited superior discrimination ability compared to each metric used alone. The ALPS-index combined with MD had the highest area under the curve (AUC) of 0.854 as compared to that of 0.614-0.807 for a single metric in glioma grading, while for IDH1 mutation prediction, this combination had the highest AUC of 0.861 as compared to that of 0.707-0.778 for a single metric. Conclusions: The reduced ALPS-index may reflect tumor-induced glymphatic system impairment, and the ALPS-index may be able to complement conventional diffusion metrics in glioma grading and IDH1 genotyping.

Cuproptosis-related classification and personalized treatment in lower-grade gliomas to prompt precise oncology.

BACKGROUND: Cuproptosis is implicated in regulating tricarboxylic acid cycle and associated with tumor therapeutic sensitivity, patient outcomes and tumorigenesis. However, the classification and prognostic effect of cuproptosis-associated genes (CAGs), the relationship between cuproptosis and tumor microenvironment (TME) and the treatment of lower-grade glioma (LrGG) remain enigmatic. METHODS: The genetic and transcriptional alterations, prognostic value and classification related to cuproptosis were systematically analyzed. Subtypes of cuproptosis and cuproptosis score (Cuscore) were constructed and further confirmed by two external cohorts. The relationships between cuproptosis and TME, prognosis, and treatment response were also evaluated. RESULTS: Four clusters were identified based on cuproptosis-associated genes. The associations between cuproptosis-associated clusters and clinical features, prognosis, immune cell infiltration, and chemotherapy sensitivity were observed. The Cuscore is an independent prognostic indicator in LrGG patients. The nomogram is constructed according to Cuscore and clinical characteristics, and has good predictive ability and calibration. Patients with high Cuscore had a worse prognosis and advanced performance. A higher Cuscore also indicated a higher stromal score, abundant immune infiltration, and increased tumor mutation burden. A high Cuscore was remarkably related to immune checkpoint inhibitors, immunotherapy response and immune phenotype. CONCLUSIONS: This study demonstrates the clinical effect of CAGs, and suggests that cuproptosis could be a potential therapeutic target in LrGG.

Collateral circulation predicts 3-month functional outcomes of subacute ischemic stroke patients: A study combining arterial spin labeling and MR angiography.

OBJECTIVE: Collateral circulation could help preserve the blood supply and protect penumbra in ischemic stroke (IS), critical for late-window therapeutic decisions and clinical outcomes. In this study, we aimed to investigate the prognostic value of two collateral indexes measured by arterial spin labeling (ASL) and MR angiography (MRA) in subacute IS patients. MATERIALS AND METHODS: Fifty-five subacute IS patients with large artery atherosclerosis were retrospectively collected. Arterial transit artifact (ATA) on ASL and good circulation (GC) on MRA were ranked as markers of leptomeningeal collaterals and fast collaterals, respectively. Volume and relative cerebral blood flow (rCBF) of infarct and hypoperfusion area were calculated. Stroke severity was determined by baseline- and discharge- National Institute of Hospital Stroke Scale (NIHSS). Functional independence (FI) was defined as 3-month modified Ranking Scale ≤2. Univariate analyses and multivariable logistic regression analyses were conducted to identify the independent predictors of FI. RESULTS: Thirty-eight patients (69.1 %) presented ATA and 29 (52.7 %) patients presented GC. Univariate analyses showed that baseline-NIHSS, discharge-NIHSS, rCBF of infarct, presence of ATA and GC were associated with FI (P < 0.05). After multivariable adjustment, ATA (adjusted Odds Ratio [OR]: 13.785, 95 % CI: 2.608-72.870, P = 0.002) and GC (adjusted OR: 8.317, 95 % CI: 1.629-42.454, P = 0.011) remained independent predictors of FI. Besides, patients with both ATA and GC had the highest frequencies of FI while patients with neither of them showed the lowest (94.7 % vs 14.3 %, P < 0.001), indicating a positive synergistic effect between ATA and GC. CONCLUSION: The combination of ASL and MRA simultaneously reflects leptomeningeal collaterals and fast collaterals, providing a useful method to predict functional outcomes of subacute IS patients.

Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study.

BACKGROUND: Noninvasive determination of Notch signaling is important for prognostic evaluation and therapeutic intervention in glioma. PURPOSE: To predict Notch signaling using multiparametric (mp) MRI radiomics and correlate with biological characteristics in gliomas. STUDY TYPE: Retrospective. POPULATION: A total of 63 patients for model construction and 47 patients from two public databases for external testing. FIELD STRENGTH/SEQUENCE: A 1.5 T and 3.0 T, T1-weighted imaging (T1WI), T2WI, T2 fluid attenuated inversion recovery (FLAIR), contrast-enhanced (CE)-T1WI. ASSESSMENT: Radiomic features were extracted from CE-T1WI, T1WI, T2WI, and T2FLAIR and imaging signatures were selected using a least absolute shrinkage and selection operator. Diagnostic performance was compared between single modality and a combined mpMRI radiomics model. A radiomic-clinical nomogram was constructed incorporating the mpMRI radiomic signature and Karnofsky Performance score. The performance was validated in the test set. The radiomic signatures were correlated with immunohistochemistry (IHC) analysis of downstream Notch pathway components. STATISTICAL TESTS: Receiver operating characteristic curve, decision curve analysis (DCA), Pearson correlation, and Hosmer-Lemeshow test. A P value < 0.05 was considered statistically significant. RESULTS: The radiomic signature derived from the combination of all sequences numerically showed highest area under the curve (AUC) in both training and external test sets (AUCs of 0.857 and 0.823). The radiomics nomogram that incorporated the mpMRI radiomic signature and KPS status resulted in AUCs of 0.891 and 0.859 in the training and test sets. The calibration curves showed good agreement between prediction and observation in both sets (P= 0.279 and 0.170, respectively). DCA confirmed the clinical usefulness of the nomogram. IHC identified Notch pathway inactivation and the expression levels of Hes1 correlated with higher combined radiomic scores (r = -0.711) in Notch1 mutant tumors. DATA CONCLUSION: The mpMRI-based radiomics nomogram may reflect the intratumor heterogeneity associated with downstream biofunction that predicts Notch signaling in a noninvasive manner. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

A Comparative Study Between Tumor Blood Vessels and Dynamic Contrast-enhanced MRI for Identifying Isocitrate Dehydrogenase Gene 1 (IDH1) Mutation Status in Glioma.

OBJECTIVE: Isocitrate dehydrogenase gene (IDH) mutations are associated with tumor angiogenesis and therefore play an important role in glioma management. This study compared the performance of tumor blood vessels counted from contrast-enhanced 3D brain volume (3D-BRAVO) sequence and dynamic contrast-enhanced (DCE) MRI in differentiating IDH1 status in gliomas. METHODS: Forty-four glioma patients [16 with IDH1 mutant-type (IDH1-MT), 28 with IDH1 wild-type (IDH1-WT)] were retrospectively analyzed. A blood vessel entering a tumor was defined as an intratumoral vessel; a blood vessel adjacent to the edge of a tumor was defined as a peritumoral vessel. Combined vessels were defined as the sum of the intratumoral and peritumoral vessels. DCE-derived metrics of tumor were normalized to the contralateral normal-appearing white matter. RESULTS: Intratumoral, peritumoral, and combined tumor blood vessels were all significantly different between IDH1-MT and IDH1-WT gliomas, and the range of area under curves (AUCs) was 0.816-0.855. For DCE-derived parameters, cerebral blood volume, cerebral blood flow, mean transit time, and volume transfer constant were significantly different between IDH1-MT and IDH1-WT gliomas, and the range of AUCs was 0.703-0.756. Combined vessels possessed the best performance for identifying IDH1 mutations in gliomas (AUC: 0.855, sensitivity: 0.857, specificity: 0.812, P<0.001). CONCLUSION: The number of tumor blood vessels has comparable diagnostic performance with DCE-derived parameters for differentiating IDH1 mutations and can serve as a potential imaging biomarker to reflect IDH1 mutations in gliomas.

Tractometry-Based Estimation of Corticospinal Tract Injury to Assess Initial Impairment and Predict Functional Outcomes in Ischemic Stroke Patients.

BACKGROUND: Corticospinal tract (CST) injury has been shown to exert a major influence on functional recovery after ischemic stroke. PURPOSE: To evaluate the prognostic value of CST injury estimated using a recent developed tractometry-based method. STUDY TYPE: Prospective. POPULATION: Forty-eight patients with CST damage induced by stroke lesion who underwent brain magnetic resonance imaging within 7 days from onset. SEQUENCE: Diffusion-weighted imaging (b = 1000 seconds/mm2 ) and diffusion kurtosis imaging (DKI) spin-echo echo-planar sequence with three b-values (0, 1250, and 2500 seconds/mm2 ) at 3.0 T. ASSESSMENT: A recently developed approach that combines tract segmentation and orientation mapping was used for CST-specific tractography and tractometry. CST injury was estimated using the proposed method with diffusion metrics extracted from DKI sequence and with the first principal component (PC1) of the metrics. We also calculated the weighted lesion load (wLL) for comparison. Clinical evaluation included the National Institutes of Health Stroke Score in the acute phase and the modified Rankin scale at 3 months post-stroke. The correlations between CST injury and initial motor impairment, as well as the prognostic values of CST injury for functional outcomes were evaluated. STATISTICAL TESTS: Pearson correlation and logistic regression. Area under the receiver operating characteristic curve. P < 0.05 was considered statistically significant. RESULTS: CST injury calculated with diffusion metrics except fractional anisotropy all showed significant correlations with initial motor impairment. PC1 achieved the largest correlation coefficient (R = 0.65) compared with wLL and other diffusion metrics. In addition to wLL, DKI_AK, AFD_total, and PC1 maximum all showed predictive values for functional outcomes. DATA CONCLUSION: Structural injury to CST is important for the assessment of the extent of injury and the prediction of functional outcome. The method proposed in our study could provide an imaging indicator to quantify the CST injury after ischemic stroke. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Characterization of orthotopic xenograft tumor of glioma stem cells (GSCs) on MRI, PET and immunohistochemical staining.

Introduction: The orthotopic xenograft tumors of human glioma stem cells (GSCs) is a recent glioma model with genotype and phenotypic characteristics close to human gliomas. This study aimed to explore the imaging and immunohistochemical characteristics of GSCs gliomas. Methods: The rats underwent MRI and 18F-FDG PET scan in 6th-8th weeks after GSCs implantation. The MRI morphologic, DWI and PET features of the tumor lesions were assessed. In addition, the immunohistochemical features of the tumor tissues were further analyzed. Results: Twenty-five tumor lesions were identified in 20 tumor-bearing rats. On structural MRI, the average tumor size was 30.04±17.31mm2, and the intensity was inhomogeneous in 76.00% (19/25) of the lesions. The proportion of the lesions mainly presented as solid, cystic and patchy tumor were 60.00% (15/25), 16.00% (4/25) and 24.00% (6/25), respectively. The boundary was unclear in 88.00% (22/25), and peritumoral mass effect was observed in 92.00% (23/25) of the lesions. On DWI, 80.00% (20/25) of the lesions showed increased intensity. Of the 14 lesions in the 11 rats underwent PET scan, 57.14% (8/14) showed increased FDG uptake. On immunohistochemical staining, the expression of Ki-67 was strong in all the lesions (51.67%±11.82%). Positive EGFR and VEGF expression were observed in 64.71% (11/17) and 52.94% (9/17) of the rats, whereas MGMT and HIF-1α showed negative expression in all the lesions. Discussion: GSC gliomas showed significant heterogeneity and invasiveness on imaging, and exhibited strong expression of Ki-67, partial expression of EGFR and VEGF, and weak expression of MGMT and HIF-1α on immunohistochemical staining.

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging.

Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

Application of Cluster Analysis of Time Evolution for Magnetic Resonance Imaging -Derived Oxygen Extraction Fraction Mapping: A Promising Strategy for the Genetic Profile Prediction and Grading of Glioma.

Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling that genetic aberrations associated with magnetic resonance imaging (MRI) phenotypes may aid in the diagnosis and treatment of glioma. Objective: To explore the predictability of MRI-based oxygen extraction fraction (OEF) mapping using cluster analysis of time evolution (CAT) for genetic profiling and glioma grading. Methods: Ninety-one patients with histopathologically confirmed glioma were examined with CAT for quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude-based OEF mapping and dynamic contrast-enhanced (DCE) MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis [volume transfer constant, cerebral blood volume (CBV), and cerebral blood flow], were investigated to predict IDH mutation, O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) vs. lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using receiver operating characteristic (ROC) analysis. Results: IDH1-mutated LGGs exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all p < 0.01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated GBM, but only OEF retained significance (p = 0.01). Relative prevalence of RTK alterations was associated with increased OEF (p = 0.003) and perfusion values (p < 0.05). ROC analysis suggested OEF achieved best performance for IDH mutation detection [area under the curve (AUC) = 0.828]. None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in glioma grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes. Conclusion: CAT for MRI-based OEF mapping is a promising technology for oxygen measurement and along with perfusion MRI can predict genetic profiles and tumor grade in a non-invasive and clinically relevant manner. Clinical Impact: Physiological imaging provides an in vivo portrait of genetic alterations in glioma and offers a potential strategy for non-invasively selecting patients for individualized therapies.

The Spatiotemporal Evolution of MRI-Derived Oxygen Extraction Fraction and Perfusion in Ischemic Stroke.

PURPOSE: This study aimed to assess the spatiotemporal evolution of oxygen extraction fraction (OEF) in ischemic stroke with a newly developed cluster analysis of time evolution (CAT) for a combined quantitative susceptibility mapping and quantitative blood oxygen level-dependent model (QSM + qBOLD, QQ). METHOD: One hundred and fifteen patients in different ischemic stroke phases were retrospectively collected for measurement of OEF of the infarcted area defined on diffusion-weighted imaging (DWI). Clinical severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Of the 115 patients, 11 underwent two longitudinal MRI scans, namely, three-dimensional (3D) multi-echo gradient recalled echo (mGRE) and 3D pseudo-continuous arterial spin labeling (pCASL), to evaluate the reversal region (RR) of the initial diffusion lesion (IDL) that did not overlap with the final infarct (FI). The temporal evolution of OEF and the cerebral blood flow (CBF) in the IDL, the RR, and the FI were assessed. RESULTS: Compared to the contralateral mirror area, the OEF of the infarcted region was decreased regardless of stroke phases (p < 0.05) and showed a declining tendency from the acute to the chronic phase (p = 0.022). Five of the 11 patients with longitudinal scans showed reversal of the IDL. Relative oxygen extraction fraction (rOEF, compared to the contralateral mirror area) of the RR increased from the first to the second MRI (p = 0.044). CBF was about 1.5-fold higher in the IDL than in the contralateral mirror area in the first MRI. Two patients showed penumbra according to the enlarged FI volume. The rOEF of the penumbra fluctuated around 1.0 at earlier scan times and then decreased, while the CBF decreased continuously. CONCLUSION: The spatiotemporal evolution of OEF and perfusion in ischemic lesions is heterogeneous, and the CAT-based QQ method is feasible to capture cerebral oxygen metabolic information.

Survival prediction of high-grade glioma patients with diffusion kurtosis imaging.

PURPOSE: To evaluate the prognostic value of diffusion kurtosis imaging (DKI) for survival prediction of patients with high-grade glioma (HGG). MATERIALS AND METHODS: DKI was performed for fifty-eight patients with pathologically proven HGG by using a 3-T scanner. The mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) values in the solid part of the tumor were measured and normalized. Univariate Cox regression analysis was used to evaluate the association between overall survival (OS) and sex, age, Karnofsky performance status (KPS), tumor grade, Ki-67 labeling index (LI), extent of resection, use of chemoradiotherapy, MK, MD, and FA. Multivariate Cox regression analysis including sex, age, KPS, extent of resection, use of chemoradiotherapy, MK, MD, and FA was subsequently performed. Spearman's correlation coefficient for OS and the area under receiver operating characteristic curve (AUC) for predicting 2-year survival were calculated for each DKI parameter and further compared. RESULTS: In univariate Cox regression analyses, OS was significantly associated with the tumor grade, Ki-67 LI, extent of resection, use of chemoradiotherapy, MK, and MD (P < 0.05 for all). Multivariate Cox regression analyses indicated that MK, MD (hazard ratio = 1.582 and 0.828, respectively, for each 0.1 increase in the normalized value), extent of resection and use of chemoradiotherapy were independent predictors of OS. The absolute value of the correlation coefficient for OS and AUC for predicting 2-year survival by MK (rho = -0.565, AUC = 0.841) were higher than those by MD (rho = 0.492, AUC = 0.772), but the difference was not significant. CONCLUSION: DKI is a promising tool to predict the survival of HGG patients. MK and MD are independent predictors. MK is potentially better associated with OS than MD, which may lead to a more accurate evaluation of HGG patient survival.

Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.

PURPOSE: To explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation. METHODS: 220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm3 isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC. RESULTS: In univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T1C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II-III, 0.997 for grades II-IV, and 0.881 for grades III-IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936. CONCLUSION: Multicontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis. KEY POINTS: • Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI. • Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour. • Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.

Amid proton transfer (APT) and magnetization transfer (MT) MRI contrasts provide complimentary assessment of brain tumors similarly to proton magnetic resonance spectroscopy imaging (MRSI).

OBJECTIVES: Using MRSI as comparison, we aimed to explore the difference between amide proton transfer (APT) MRI and conventional semi-solid magnetization transfer ratio (MTR) MRI, and to investigate if molecular APT and structural MTR can provide complimentary information in assessing brain tumors. METHODS: Seventeen brain tumor patients and 17 age- and gender-matched volunteers were included and scanned with anatomical MRI, APT and MT-weighted MRI, and MRSI. Multi-voxel choline (Cho) and N-acetylaspartic acid (NAA) signals were quantified from MRSI and compared with MTR and MTRasym(3.5ppm) contrasts averaged from corresponding voxels. Correlations between contrasts were explored voxel-by-voxel by pooling values from all voxels into Pearson's correlation analysis. Differences in correlation coefficients were tested with the Z-test (set at p<0.05). RESULTS: APT and MT provide good contrast and quantitative parameters in tumor imaging, as do the metabolite (Cho and NAA) maps. MTRasym(3.5ppm) significantly correlated with MTR (R=-0.61, p<0.0001), Cho (R=0.568, p<0.0001) and NAA (R=-0.619, p<0.0001) in tumors, and MTR also significantly correlated with Cho (R=-0.346, p<0.0001) and NAA (R=0.624, p<0.0001). In healthy volunteers, MTRasym(3.5ppm) was non-significantly correlated with MTR (R=-0.049, p=0.239), Cho (R=0.030, p=0.478) and NAA (R=-0.083, p=0.046). Significant correlations were found among MTR with Cho (R=0.199, p<0.0001) and NAA (R=0.263, p<0.0001) in the group of healthy volunteers with lower correlation R values than those in tumor patients. CONCLUSIONS: APT and MT could provide independent and supplementary information for the comprehensive assessment of molecular and structural changes due to brain tumor cancerogenesis. KEY POINTS: • MTR asym(3.5ppm) positively correlated with Cho while negatively with NAA in tumors. • MTR positively correlated with NAA while negatively with Cho in tumors. • Combining APT/MT provides molecular and structural information similarly to MRSI.

Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas.

PURPOSE: To assess the feasibility of using diffusion-weighted imaging (DWI) with the stretched-exponential model (SEM) for glioma grading and determining the correlations among parameters and proliferating cell nuclear antigen and Ki-67 expression. MATERIALS AND METHODS: Mono-exponential model-DWI (MEM-DWI) and SEM-DWI were performed in 104 patients with pathologically proven gliomas. The patients were divided into the training set (n = 72) and test set (n = 32). Apparent diffusion coefficient (ADC), solid tumor distributed diffusion coefficient (DDC), and whole tumor α values were measured. These parameters were applied as cut-off values to determine the predictive accuracy. Proliferating cell nuclear antigen and Ki-67 expression correlated with all parameters. RESULTS: Significant differences between low-grade gliomas (LGG) and high-grade gliomas (HGG) were observed for all parameters (P < 0.05), and significant differences in the ability of DDC to distinguish between any two glioma grades (P < 0.05) were also evident. DDC showed the highest sensitivity and specificity for glioma grading and was negatively correlated with Ki-67 and proliferating cell nuclear antigen expression. DDC also showed greater predictive accuracy than ADC and α. CONCLUSION: SEM-DWI offers a better approach for glioma grading than MEM-DWI, and DDC may be a better imaging biomarker for grading and evaluating the proliferative activity of brain gliomas.

Intravoxel incoherent motion diffusion-weighted imaging analysis of diffusion and microperfusion in grading gliomas and comparison with arterial spin labeling for evaluation of tumor perfusion.

PURPOSE: To determine the utility of intravoxel incoherent motion (IVIM) imaging in grading gliomas and compare IVIM perfusion metrics with arterial spin labeling (ASL)-derived cerebral blood flow (CBF). MATERIALS AND METHODS: Fifty-two patients with pathologically confirmed gliomas underwent IVIM and ASL imaging at 3.0T. IVIM perfusion-related diffusivity (D*), perfusion fraction (f), product of f and D*(f×D*), true diffusivity (D), and apparent diffusion coefficient (ADC) were obtained to distinguish glioma grades. The CBF derived from pseudocontinuous ASL within the solid tumor was compared and correlated with IVIM perfusion metrics for grading of gliomas. Values were also normalized to the contralateral normal-appearing white matter. Receiver-operating characteristic was performed to determine diagnostic efficiency. The reliability was estimated with intraclass coefficient, coefficient of variance, and Bland-Altman plots. RESULTS: IVIM perfusion metrics and CBF were significantly higher in the high-grade than the low-grade gliomas (P < 0.001), ADC and D were significantly lower in the high-grade than the low-grade gliomas (P < 0.001). f×D* differed significantly between grades II through IV (P < 0.05 for all). The other metrics showed significant difference between grade II and grade III (P < 0.05 for all). Area under the curve (AUC) was largest for f×D* in distinguishing high-grade from low-grade gliomas (AUC = 0.979, P < 0.001) and between grade II and grade III (AUC = 0.957, P < 0.001). f×D* improved diagnostic performance of CBF in grading gliomas and showed strong correlation with CBF (r = 0.696, P < 0.001). CONCLUSION: IVIM-derived metrics are promising biomarkers in preoperative grading gliomas. IVIM imaging may be an additive method to ASL and ADC for evaluating tumor perfusion and diffusion. J. Magn. Reson. Imaging 2016;44:620-632.

Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation.

Conventional diffusion imaging techniques are not sufficiently accurate for evaluating glioma grade and cellular proliferation, which are critical for guiding glioma treatment. Diffusion kurtosis imaging (DKI), an advanced non-Gaussian diffusion imaging technique, has shown potential in grading glioma; however, its applications in this tumor have not been fully elucidated. In this study, DKI and diffusion weighted imaging (DWI) were performed on 74 consecutive patients with histopathologically confirmed glioma. The kurtosis and conventional diffusion metric values of the tumor were semi-automatically obtained. The relationships of these metrics with the glioma grade and Ki-67 expression were evaluated. The diagnostic efficiency of these metrics in grading was further compared. It was demonstrated that compared with the conventional diffusion metrics, the kurtosis metrics were more promising imaging markers in distinguishing high-grade from low-grade gliomas and distinguishing among grade II, III and IV gliomas; the kurtosis metrics also showed great potential in the prediction of Ki-67 expression. To our best knowledge, we are the first to reveal the ability of DKI to assess the cellular proliferation of gliomas, and to employ the semi-automatic method for the accurate measurement of gliomas. These results could have a significant impact on the diagnosis and subsequent therapy of glioma.