Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status.

OBJECTIVES: To evaluate the glioma grade, Ki-67 expression, and IDH-1 mutation status using mean apparent propagator (MAP) MRI. METHODS: Forty enrolled glioma patients underwent structural and diffusion MRI. The diffusion metric values including fractional anisotropy (FA), mean diffusivity (MD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP) in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The TP/NAWM ratios of diffusion metric values were correlated with tumor grades, Ki-67, and IDH-1 mutation statuses, and the diagnostic performance was assessed. RESULTS: QIV were significantly higher, whereas RTAP and RTOP were significantly lower in low-grade gliomas (LGGs) than those in high-grade gliomas (HGGs); QIV and MD were significantly higher, whereas RTAP and RTOP were significantly lower in lower-grade gliomas (grade II and III) than those in grade IV gliomas (p < 0.05 for all). RTAP performed best in grading gliomas. MSD, QIV, and MD were significantly higher, whereas RTAP, RTOP, RTPP, and FA were significantly lower in the IDH-1 mutant gliomas than those in the IDH-1 wild-type ones both for all gliomas and lower-grade gliomas (p < 0.05 for all). RTAP performed best in all gliomas, while QIV performed best in lower-grade gliomas. Additionally, RTAP, RTOP, and FA correlated positively, whereas MSD, QIV, and MD correlated negatively with Ki-67 (p < 0.05 for all). CONCLUSIONS: MAP-MRI is a potent approach in evaluating the microstructural changes in gliomas with different grades, cellular proliferation, and IDH-1 mutation statuses. KEY POINTS: • MAP-MRI, a newly developed diffusion technique, accurately reveals microstructure-related features in the complex white matter by recovering important microstructural tissue parameters. • MAP-MRI is a potent approach in evaluating the glioma grade, IDH-1 mutation status, and Ki-67 expression. • Compared with DTI, MAP-MRI seems to demonstrate higher diagnostic performance.

Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients.

OBJECTIVES: To comprehensively and noninvasively risk-stratify glioma grade, isocitrate dehydrogenase (IDH) genotype, and 1p/19q codeletion status using multi-contrast Z-spectral magnetic resonance imaging (MRI). METHODS: One hundred and thirteen patients with glioma were retrospectively included. Multiple contrasts contributing to Z-spectra, including direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), amide proton transfer (APT) effect, aliphatic nuclear Overhauser effect, and the 2-ppm chemical exchange saturation transfer peak (CEST@2ppm), were fitted with five individual Lorentzian functions. Z-spectral contrasts were compared according to the three most important risk stratifications: tumor grade, IDH genotype, and 1p/19q codeletion status. We further investigated the differentiation of 1p/19q codeletion status within IDH mutant gliomas. The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic (ROC) analyses. RESULTS: DSW was significantly different within grade, IDH genotypes, and 1p/19q codeletion status. APT was significantly different with grade and IDH mutation, but not with 1p/19q subtypes. CEST@2ppm was only significantly different with 1p/19q codeletion subtypes. DSW and CEST@2ppm were the two Z-spectral contrasts able to differentiate 1p/19q codeletion subtypes within IDH mutant gliomas. For differentiating glioma grades using ROC analyses, DSW achieved the largest AUC. For differentiating IDH genotypes, DSW and APT achieved comparable AUCs. DSW was the best metric for differentiating 1p/19q codeletion status within all patients and within the IDH mutant patients. Combining all Z-spectral contrasts improved sensitivity and specificity for all risk stratifications. CONCLUSIONS: Multi-parametric Z-spectral MRI serves as a useful, comprehensive, and noninvasive imaging technique for glioma stratification in clinical patients. KEY POINTS: • Multiple contrasts contributing to Z-spectra were separately fitted with Lorentzian functions. • Z-spectral contrasts were compared within the three most important and common tumor risk stratifications for gliomas: tumor grade, IDH genotype, and 1p/19q codeletion status. • The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic analyses, which found Z-spectral MRI to be a useful and comprehensive imaging biomarker for glioma stratification.

Comparison of amide proton transfer imaging and magnetization transfer imaging in revealing glioma grades and proliferative activities: a histogram analysis.

PURPOSE: Comprehensive understanding glioma metabolic characters is of great help for patient management. We aimed to compare amide proton transfer imaging (APTw) and magnetization transfer imaging (MT) in predicting glioma malignancy and reflecting tumor proliferation. METHODS: Thirty low-grade gliomas (LGGs) and 39 high-grade gliomas (HGGs) were prospectively included, of which 58 samples Ki-67 levels were quantified. Anatomical MRI, APTw, and MT were scanned, and magnetization transfer ratio (MTR) and asymmetric magnetic transfer ratio at 3.5 ppm (MTRasym(3.5ppm)) were calculated. ROIs were semi-automatically drawn with ImageJ, from which histogram features, including 5th, 25th, 50th, mean, 70th, 90th, and 95th percentiles were extracted. The independent t test was used to test differences in LGGs and HGGs, and correlations between histogram features and tumor grades, Ki-67 were revealed by Spearman's rank or Pearson's correlation analysis. RESULTS: The maximum correlation coefficient (R) values of APTw were 0.526 (p < 0.001) with tumor grades and 0.397 (p < 0.001) with Ki-67 at 90th percentiles, while only 5th and 25th percentiles of MT significantly correlated with tumor grades. Moreover, APTw features were significantly different in LGGs and HGGs, except 5th percentile. The most significantly different feature was 95th percentile, providing the excellent AUC of 0.808. However, the best feature in MTR was 5th percentiles with AUC of 0.703. Combing 5th and 95th of APTw achieved highest AUC Of 0.837. CONCLUSIONS: Both APTw and MT provide quantitative information for tumor metabolite imaging. However, APTw supplys more specific information in reflecting glioma biological behaviors than MT, and well differentiates glioma malignancy.

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.

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.

Role of Histogram Features on Arterial Spin Labeling Perfusion Magnetic Resonance Imaging in Identifying Isocitrate Dehydrogenase Genotypes and Glioma Malignancies.

AIM: To explore the use of histogram features on noninvasive arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) in differentiating isocitrate dehydrogenase mutant-type (IDH-mut) from isocitrate dehydrogenase wild-type (IDH-wt) gliomas, and lower-grade gliomas (LGGs) from glioblastomas. MATERIAL AND METHODS: This retrospective study included 131 patients who underwent ASL MRI and anatomic MRI. Cerebral blood flow (CBF) maps were calculated, from which 10 histogram features describing the CBF distribution were extracted within the tumor region. Correlation analysis was performed to determine the correlations between histogram features as well as tumor grades and IDH genotypes. The independent t-test and Fisher's exact test were used to determine differences in the extracted histogram features, age at diagnosis, and sex in different glioma subtypes. Multivariate binary logistic regression analysis was performed, and diagnostic performances were evaluated with the receiver operating characteristic curves. RESULTS: CBF histogram features were significantly correlated with tumor grades and IDH genotypes. These features can effectively differentiate LGGs from glioblastomas, and IDH-mut from IDH-wt gliomas. The area under the receiving operating characteristic curve of the model calculated using combined CBF 30th percentile and age at diagnosis in differentiating LGGs from glioblastomas was 0.73. Integrating age at diagnosis and CBF 10th percentile could be more effective in differentiating IDH-mut from IDH-wt gliomas. Furthermore, the combined model had a better area under the receiving operating characteristic curve at 0.856 (sensitivity: 84.4%, specificity: 82.9%). CONCLUSION: The histogram features on ASL were significantly correlated with tumor grade and IDH genotypes. Moreover, the use of these features could effectively differentiate glioma subtypes. The combined application of age at diagnosis and perfusion histogram features resulted in a more comprehensive identification of tumor subtypes. Therefore, ASL can be a noninvasive tool for the pre-surgical evaluation of gliomas.

A Study on Prognosis of Diffuse Glioma Based on Clinical Factors and Magnetic Resonance Imaging Radiomics.

OBJECTIVE: To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma. METHODS: Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort: validation cohort=7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) was also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a "Prognosis Calculator for Diffuse Glioma" was constructed based on the nomogram. RESULTS: The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year area under the curve of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year area under the curve was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The "Prognosis Calculator for Diffuse Glioma" was available at https://github.com/HDCurry/prognosis. CONCLUSIONS: Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The "Prognosis Calculator for Diffuse Glioma" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies.

Noninvasive Characterization of Metabolic Changes in Ischemic Stroke Using Z-spectrum-fitted Multiparametric Chemical Exchange Saturation Transfer-weighted Magnetic Resonance Imaging.

OBJECTIVE: This study aimed to noninvasively characterize the metabolic alterations in ischemic brain tissues using Z-spectrum-fitted multiparametric chemical exchange saturation transfer-weighted magnetic resonance imaging (CEST-MRI). METHODS: Three sets of Z-spectrum data with saturation power (B1) values of 1.5, 2.5, and 3.5 µT, respectively, were acquired from 17 patients with ischemic stroke. Multiple contrasts contributing to the Z-spectrum, including fitted amide proton transfer (APTfitted), +2 ppm peak (CEST@2ppm), concomitantly fitted APTfitted and CEST@2ppm (APT&CEST@2ppm), semisolid magnetization transfer contrast (MT), aliphatic nuclear Overhauser effect (NOE), and direct saturation of water (DSW), were fitted with 4 and 5 Lorentzian functions, respectively. The CEST metrics were compared between ischemic lesions and contralateral normal white matter (CNWM), and the correlation between the CEST metrics and the apparent diffusion coefficient (ADC) was assessed. The differences in the Z-spectrum metrics under varied B1 values were also investigated. RESULTS: Ischemic lesions showed increased APTfitted, CEST@2ppm, APT&CEST@2ppm, NOE, and DSW as well as decreased MT. APT&CEST@2ppm, MT, and DSW showed a significant correlation with ADC [APT&CEST@2ppm at the 3 B1 values: R=0.584/0.467/0.551; MT at the 3 B1 values: R=-0.717/-0.695/-0.762 (4-parameter fitting), R=-0.734/-0.711/-0.785 (5-parameter fitting); DSW of 4-/5-parameter fitting: R=0.794/0.811 (2.5 µT), R=0.800/0.790 (3.5 µT)]. However, the asymmetric analysis of amide proton transfer (APTasym) could not differentiate the lesions from CNWM and showed no correlation with ADC. Furthermore, the Z-spectrum contrasts varied with B1. CONCLUSION: The Z-spectrum-fitted multiparametric CEST-MRI can comprehensively detect metabolic alterations in ischemic brain tissues.

T2-FLAIR, DWI and DKI radiomics satisfactorily predicts histological grade and Ki-67 proliferation index in gliomas.

OBJECTIVE: To build highly predictive performance models for glioma stratification with radiomics features from non-invasive MRI. METHODS: T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) imaging, diffusion-weighted MRI and diffusion kurtosis imaging were retrospectively collected for 139 glioma cases (2 with grade I, 67 with grade II, 36 with grade III and 34 with grade IV disease). Multi-parameter maps, including the apparent diffusion coefficient (ADC), mean diffusion coefficient (Dmean), fractional anisotropy (FA), and mean kurtosis (MK), were co-registered to T2-FLAIR, and 431 radiomics features for each were extracted within manually segmented tumour regions. Partial correlation analysis revealed correlations between radiomics features and glioma stratification factors (tumour grades and Ki-67 LI). Predictive models were built with adjusted-imbalanced logistic regression. RESULTS: MK radiomics features were more closely correlated with glioma stratification than other modalities analysed. The maximum R in MK was 0.52 for tumour grade and 0.56 for Ki-67 index (compared with 0.36 and 0.34 in FA, and 0.43 and 0.37 in ADC, and 0.48 and 0.42 in T2-FLAIR). The best predictive models for grade II vs. III, grade II vs. IV, low-grade vs. high-grade gliomas and positive vs. highly positive Ki-67 LI were obtained by combining multi-parameter MR radiomics features with AUCs of 0.858, 0.966, 0.853 and 0.870, respectively. However, for grade III vs. IV gliomas, the model obtained from MK radiomics features achieved the highest AUC (0.947), with excellent sensitivity and specificity. CONCLUSION: Compared with the other modalities, MK showed closer correlations with tumour grade and Ki-67 LI. Combined radiomics models integrating multi-parameter MRI allow for the generation of highly predictive models for glioma stratification.

Predicting cancer malignancy and proliferation in glioma patients: intra-subject inter-metabolite correlation analyses using MRI and MRSI contrast scans.

BACKGROUND: The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation. METHODS: A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, RAPT vs . MTC, RAPT vs . tCho/tCr, and RMTC vs . tNAA/tCr. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves. RESULTS: Compared with the NCs, gliomas showed stronger inter-metabolic correlations. RAPT vs . MTC was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in RMTC vs . tNAA/tCr among the three groups. RAPT vs . MTC and RAPT vs . tCho/tCr correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only RAPT vs . MTC was mildly correlated with Ki-67 (R=-0.33, P=0.02). RAPT vs . MTC and RAPT vs . tCho/tCr achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%). CONCLUSIONS: This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis.

Associations Between Diffusion Dynamics and Functional Outcome in Acute and Early Subacute Ischemic Stroke.

PURPOSE: The current study aimed to investigate the associations between diffusion dynamics of ischemic lesions and clinical functional outcome of acute and early subacute stroke. MATERIAL AND METHODS: A total of 80 patients with first ever infarcts in the territory of the middle cerebral artery underwent multi-b-values diffusion-weighted imaging and diffusion kurtosis imaging. Multiple diffusion parameters were generated in postprocessing using different diffusion models. Long-term functional outcome was evaluated with modified Rankin scale (mRS) at 6 months post-stroke. Good functional outcome was defined as mRS score ≤ 2 and poor functional outcome was defined as mRS score ≥ 3. Univariate analysis was used to compare the diffusion parameters and clinical features between patients with poor and good functional outcome. Significant parameters were further analyzed for correlations with functional outcome using partial correlation. RESULTS: In univariate analyses, standard-b-values apparent diffusion coefficient (ADCst) ratio and fractional anisotropy (FA) ratio of acute stroke, ADCst ratio and mean kurtosis (MK) ratio of early subacute stroke were statistically different between patients with poor outcome and good outcome (P < 0.05). When the potential confounding factor of lesion volume was controlled, only FA ratio of acute stroke, ADCst ratio and MK ratio of early subacute stroke remained correlated with the functional outcome (P < 0.05). CONCLUSION: Diffusion dynamics are correlated with the clinical functional outcome of ischemic stroke. This correlation is independent of the effect of lesion volume and is specific to the time period between symptom onset and imaging. More effort is needed to further investigate the predictive value of diffusion-weighted imaging.

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.

Application of Neurite Orientation Dispersion and Density Imaging in Assessing Glioma Grades and Cellular Proliferation.

OBJECTIVE: To explore the performance of neurite orientation dispersion and density imaging (NODDI) in grading gliomas and to evaluate the cellular proliferation. METHODS: NODDI and diffusion-weighted imaging were performed on 79 patients with histopathologically proven gliomas. Parameter maps of intracellular volume fraction (ICVF), orientation dispersion index (ODI), and apparent diffusion coefficient (ADC) were calculated. Regions of interest were placed in the most solid part of the tumor. These metrics were normalized to the contralateral normal-appearing white matter and correlated with Ki-67 expression. RESULTS: ICVF and ODI increased as tumor grades increased, whereas ADC decreased with the increase of tumor grades. Significant differences in normalized ICVF and ODI were observed between low-grade gliomas and high-grade gliomas (ICVF: 0.208 ± 0.104 vs. 0.718 ± 0.234; ODI: 0.952 ± 0.428 vs. 1.767 ± 0.636, P < 0.001, respectively) and between grades II and III (ICVF: 0.208 ± 0.104 vs. 0.603 ± 0.253; ODI: 0.952 ± 0.428 vs. 1.762 ± 0.542, P < 0.001, respectively). Normalized ICVF was also significantly different between grades III and IV (0.603 ± 0.253 vs. 0.803 ± 0.182, P = 0.004). Ki-67 labeling index was positively correlated with normalized ICVF and ODI (r = 0.755 and 0.572, P < 0.001, respectively), and negatively correlated with normalized ADC (r = -0.709, P < 0.001). CONCLUSIONS: NODDI is a promising method in grading gliomas and predicting cellular proliferation. These results may be of great significance for the clinical diagnosis and treatment of gliomas.

MicroRNA 217 inhibits cell proliferation and enhances chemosensitivity to doxorubicin in acute myeloid leukemia by targeting KRAS.

Acute myeloid leukemia (AML) is a heterogeneous malignant disorder derived from the myeloid hematopoietic cells that accounts for ~80% of all adult acute leukemia. Numerous studies have shown that drug resistance not only exists against conventional chemotherapeutic drugs, but also limits the efficacy of new biological agents. Therefore, it is important to identify the mechanisms behind chemoresistance and seek therapeutic strategies to enhance efficacy in AML chemotherapy. MicroRNA (miR)-217 has been recognized as a tumor suppressor that is downregulated in various types of cancer, however the mechanisms behind the expression and function of miR-217 in AML have not yet been recognized. The expression of miR-217 was determined by quantitative polymerase chain reaction (qPCR). Following transfection with miR-217 mimics, an MTT assay, chemosensitivity assay, cell apoptosis assay and western blot analysis were performed in AML cell lines. Functional assays were also performed to explore the effects of endogenous Kirsten rat sarcoma viral oncogene homolog (KRAS) in AML. The results revealed that miR-217 was downregulated in patients with AML. Overexpression of miR-217 inhibited cellular proliferation and enhanced cell chemosensitivity to doxorubicin by the cell apoptosis pathway in AML cells. A dual-luciferase reporter assay demonstrated that KRAS was a direct target gene of miR-217 in vitro. qPCR and western blot analysis revealed that miR-217 negatively regulated KRAS protein expression, but had no impact on KRAS mRNA expression. Knockdown of KRAS expression markedly suppressed AML cellular proliferation, and enhanced cell chemosensitivity to doxorubicin via the cell apoptosis pathway. These findings indicate that miR-217 functions as a tumor suppressor in AML by directly targeting KRAS. Therefore, miR-217-based therapeutic strategies may provide a novel strategy for the enhancement of efficacy in the treatment of AML.

miR-223 decreases cell proliferation and enhances cell apoptosis in acute myeloid leukemia via targeting FBXW7.

The expression of microRNA-223 (miR-233) has been investigated in various types of cancer. However, to the best of our knowledge, the expression and function of miR-223 in acute myeloid leukemia (AML) remains to be elucidated. The expression of miR-223 was measured by reverse transcription-quantitative polymerase chain reaction. Following transfection with miR-223, cell viability assays, cell apoptosis assays, western blot analysis and luciferase assays were conducted in AML cell lines. In the present study, it was initially observed that miR-223 was downregulated in AML patients compared with healthy subjects. It was also demonstrated that miR-223 inhibited cell proliferation and enhanced cell apoptosis in AML cell lines. Additionally, the present study provided evidence that miR-223 may directly target F-box and WD repeat domain containing 7 in AML. The identification of candidate target genes of miR-223 may provide an understanding of the potential mechanisms underlying the development of AML. In conclusion, the results of the present study have therapeutic implications and may be exploited for further treatment of AML.

JARID2 inhibits leukemia cell proliferation by regulating CCND1 expression.

It has recently been shown that JARID2 contributes to the malignant character of solid tumors, such as epithelial-mesenchymal transition in lung and colon cancer cell lines, but its role in leukemia progression is unexplored. In this study, we explored the effect and underlying molecular mechanism of JARID2 on leukemia cell proliferation. Real-time PCR and Western assay were carried out to detect JARID2 and CCND1 expression. Cell number and cell cycle change were detected using hemocytometer and flow cytometry, and a ChIP assay was utilized to investigate JARID2 and H3K27me3 enrichment on the CCND1 promoter. JARID2 is down-regulated in B-chronic lymphocytic leukemia (B-CLL) and acute monocytic leukemia (AMOL), and knockdown of JARID2 promotes leukemia cell proliferation via acceleration of the G1/S transition. Conversely, ectopic expression of JARID2 inhibits these malignant phenotypes. Mechanistic studies show that JARID2 negatively regulates CCND1 expression by increasing H3K27 trimethylation on the CCND1 promoter. Our findings indicate that JARID2 is a negative regulator of leukemia cell proliferation, and functions as potential tumor suppressor in leukemia.

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.

Methylenetetrahydrofolate reductase polymorphisms and susceptibility to acute lymphoblastic leukemia in a Chinese population: a meta-analysis.

BACKGROUND: Although many epidemiologic studies have investigated the methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and their association with acute lymphoblastic leukemia (ALL), definitive conclusions cannot be drawn. To clarify the effects of MTHFR polymorphisms on the risk of ALL, a meta-analysis was performed in a Chinese population. MATERIALS AND METHODS: A computerized literature search was carried out in PubMed, the Chinese Biomedicine (CBM) database, China National Knowledge Infrastructure (CNKI) platform, and the Wanfang database (Chinese) to collect relevant articles. RESULTS: A total of 11 articles including 1,738 ALL cases and 2,438 controls were included in this meta-analysis. Overall, a significantly decreased association was found between the MTHFR C677T polymorphism and ALL risk when all studies in Chinese populations were pooled into the meta-analysis. In subgroup analyses stratified by age, ethnicity, and source of controls, the same results were observed in children, in population-based studies, and in people with no stated ethnicity. However, a significantly increased association was also found for MTHFR C677T in hospital-based studies, and for MTHFR A1298C in people with no stated ethnicity. CONCLUSION: Our results suggest that the MTHFR C677T and A1298C polymorphisms may be potential biomarkers for ALL risk in Chinese populations, and studies with a larger sample size and wider population spectrum are required before definitive conclusions can be drawn.