Predicting Glioma Grade Research Articles

Glioma Grade and Molecular Markers: Comparing Machine-Learning Approaches Using VASARI (Visually AcceSAble Rembrandt Images) Radiological Assessment.

This study aimed to leverage Visually AcceSAble Rembrandt Images (VASARI) radiological features, extracted from magnetic resonance imaging (MRI) scans, and machine-learning techniques to predict glioma grade, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) methylation. A retrospective evaluation was undertaken, analyzing MRI and molecular data from 107 glioma patients treated at a tertiary hospital. Patients underwent MRI scans using established protocols and were evaluated based on VASARI criteria. Tissue samples were assessed for glioma grade and underwent molecular testing for IDH mutations and MGMT methylation. Four machine learning models, namely, Random Forest, Elastic-Net, multivariate adaptive regression spline (MARS), and eXtreme Gradient Boosting (XGBoost), were trained on 27 VASARI features using fivefold internal cross-validation. The models' predictive performances were assessed using the area under the curve (AUC), sensitivity, and specificity. For glioma grade prediction, XGBoost exhibited the highest AUC (0.978), sensitivity (0.879), and specificity (0.964), with f6 (proportion of non-enhancing) and f12 (definition of enhancing margin) as the most important predictors. In predicting IDH mutation status, XGBoost achieved an AUC of 0.806, sensitivity of 0.364, and specificity of 0.880, with f1 (tumor location), f12, and f30 (perpendicular diameter to f29) as primary predictors. For MGMT methylation, XGBoost displayed an AUC of 0.580, sensitivity of 0.372, and specificity of 0.759, highlighting f29 (longest diameter) as the key predictor. This study underscores the robust potential of combining VASARI radiological features with machine learning models in predicting glioma grade, IDH mutation status, and MGMT methylation. The best and most balanced performance was achieved using the XGBoost model. While the prediction of glioma grade showed promising results, the sensitivity in discerning IDH mutations and MGMT methylation still leaves room for improvement. Follow-up studies with larger datasets and more advanced artificial intelligence techniques can further refine our understanding and management of gliomas.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.

Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis.

Mutations in isocitrate dehydrogenase (IDH) represent an independent predictor of better survival in patients with gliomas. We aimed to assess grade and IDH mutation status in patients with untreated gliomas, by evaluating the respective value of 18F-FET PET/CT via dynamic and texture analyses. A total of 73 patients (male: 48, median age: 47) who underwent an 18F-FET PET/CT for initial glioma evaluation were retrospectively included. IDH status was available in 61 patients (20 patients with WHO grade 2 gliomas, 41 with grade 3-4 gliomas). Time-activity curve type and 20 parameters obtained from static analysis using LIFEx© v6.30 software were recorded. Respective performance was assessed using receiver operating characteristic curve analysis and stepwise multivariate regression analysis adjusted for patients' age and sex. The time-activity curve type and texture parameters derived from the static parameters showed satisfactory-to-good performance in predicting glioma grade and IDH status. Both time-activity curve type (stepwise OR: 101.6 (95% CI: 5.76-1791), p = 0.002) and NGLDM coarseness (stepwise OR: 2.08 × 1043 (95% CI: 2.76 × 1012-1.57 × 1074), p = 0.006) were independent predictors of glioma grade. No independent predictor of IDH status was found. Dynamic and texture analyses of 18F-FET PET/CT have limited predictive value for IDH status when adjusted for confounding factors. However, they both help predict glioma grade.

Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI

Predicting glioma grade plays a pivotal role in treatment and prognosis. However, several current methods for grading depend on the characteristics of the whole tumor. Predicting grade by analyzing tumor subregions has not been thoroughly investigated, which aims to improve the prediction performance. To predict glioma grade via analysis of tumor heterogeneity with features extracted from tumor subregions, it is mainly divided into four magnetic resonance imaging (MRI) sequences, including T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), pre-gadolinium T1-weighted (T1), and post-gadolinium T1-weighted methods. This study included the data of 97 patients with glioblastomas and 42 patients with low-grade gliomas before surgery. Three subregions, including enhanced tumor (ET), non-enhanced tumor, and peritumoral edema, were obtained based on segmentation labels generated by the GLISTRBoost algorithm. One hundred radiomic features were extracted from each subregion. Feature selection was performed using the cross-validated recursive feature elimination with a support vector machine (SVM) algorithm. SVM classifiers with grid search were established to predict glioma grade based on unparametric and multiparametric MRI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the classifiers, and the performance of the subregions was compared with the results of the whole tumor. In uniparametric analysis, the features from the ET subregion yielded a higher AUC value of 0.8697, 0.8474, and 0.8474 than those of the whole tumor of FLAIR, T1, and T2. In multiparametric analysis, the ET subregion achieved the best performance (AUC = 0.8755), which was higher than the uniparametric results. Radiomic features from the tumor subregion can potentially be used as clinical markers to improve the predictive accuracy of glioma grades.

An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics.

ObjectivesTo develop and validate an efficient and automatically computational approach for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients using an integration of state-of-the-art convolutional neural network (CNN) and radiomics.MethodThis retrospective study reviewed 470 preoperative MR images of glioma from BraTs public dataset (n=269) and Jinling hospital (n=201). A fully automated pipeline incorporating tumor segmentation and grading was developed, which can avoid variability and subjectivity of manual segmentations. First, an integrated approach by fusing CNN features and radiomics features was employed to stratify glioma grades. Then, a deep-radiomics signature based on the integrated approach for predicting survival of LGG patients was developed and subsequently validated in an independent cohort.ResultsThe performance of tumor segmentation achieved a Dice coefficient of 0.81. The intraclass correlation coefficients (ICCs) of the radiomics features between the segmentation network and physicians were all over 0.75. The performance of glioma grading based on integrated approach achieved the area under the curve (AUC) of 0.958, showing the effectiveness of the integrated approach. The multivariable Cox regression results demonstrated that the deep-radiomics signature remained an independent prognostic factor and the integrated nomogram showed significantly better performance than the clinical nomogram in predicting overall survival of LGG patients (C-index: 0.865 vs. 0.796, P=0.005).ConclusionThe proposed integrated approach can be noninvasively and efficiently applied in prediction of gliomas grade and survival. Moreover, our fully automated pipeline successfully achieved computerized segmentation instead of manual segmentation, which shows the potential to be a reproducible approach in clinical practice.

Serum microRNA-4297 is a sex-specific predictive biomarker of glioma grade and prognosis.

BackgroundGliomas account for nearly 80% of brain cancers, tending to occur more frequently in men with adverse outcomes. Emerging microRNAs have been positioned as promising predictors for glioma's histological grade and prognosis. However, there have been few studies concerning the sex-biased impacts on the clinical approach for the potential microRNA-4297 (miR-4297).MethodsWe utilized GSE139031micro-RNAs profiling to analyze serum miR-4297 expression in glioma. A total of 114 newly diagnosed glioma patients at the First Affiliated Hospital of Fujian Medical University from January 2017 to February 2021 were recruited and prospectively followed up. The association of miR-4297 levels with glioma grade and prognosis was investigated. Luciferase reporter gene assays and genotype analyses were carried out to explore the potential mechanism of sexually dimorphic miR-4297 in glioma.ResultsSerum miR-4297 levels were notably down-regulated in glioma. Besides, serum miR-4297 levels were positively associated with the high grades, which were exclusively present for females. The positive correlations of miR-4297 with O6-methylguanine-DNA methyltransferase (MGMT) protein and mean platelet volume were also observed in females. IDH-mutant females had decreased miR-4297. Median PFS time for females with miR-4297 ≥ 1.392 was distinctly shorter than those with miR-4297 <1.392 (12.3 months vs. 42.89 months, p = 0.0289). Based on multivariate logistic regression, miR-4297-based equation model was established as FHGRS. AU-ROC analysis revealed FHGRS exhibited a robust performance in predicting high-grade glioma in females (p < 0.001), whereas there was no such relationship in males. Furthermore, the MGMT-3'UTR variant rs7896488 in the specific binding region of miR-4297 was correlated with prognosis.ConclusionOur study uncovers sex-dependent characterization of serum miR-4297 in predicting glioma grade and the relapse risk for female patients, which underscores the clinical benefits of sex-specific analysis in non-coding RNA research.

RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas.

Simple SummaryResting-state functional magnetic resonance imaging (rs-fMRI), a popular neuroimaging technique, can provide rich information about functional processes in the brain with a large array of imaging parameters and is suitable for exploring the pathophysiological essence of gliomas. In this study, by applying omics analysis strategy to rs-fMRI with exhaustive regional parameters, we proposed a novel approach, named Regional Parameter of Resting-state fMRI-omics (RP-Rs-fMRIomics), and further evaluated the diagnosis performance of the method on brain gliomas. We found that the RP-Rs-fMRIomics, featuring entire investigation and high interpretability, presented superior performance in prediction of tumor grade, IDH genotype and prognosis of brain gliomas. This RP-Rs-fMRIomics not only contributed a new imaging method for brain glioma research, but also expanded the clinical application of rs-fMRI.Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.

Diagnostic Accuracy of Magnetic Resonance Spectroscopy in Predicting the Grade of Glioma Keeping Histopathology as the Gold Standard.

BackgroundGliomas are the most prevalent intrinsic tumors of the central nervous system and are categorized from grade I to grade IV. Magnetic resonance imaging (MRI) provides exact diagnosis, prognosis, and assessment of tumor response to current chemotherapy/immunotherapy and radiation therapy. With histopathology serving as the gold standard, we aimed to assess the diagnostic accuracy of magnetic resonance spectroscopy (MRS) in predicting glioma grade.MethodologyThis cross-sectional study was conducted in the Department of Radiology, KRL Hospital, Islamabad, from December 15, 2019, to September 30, 2021. After providing written consent, 80 patients with untreated gliomas were included in this study. The voxel of interest was identified using MRI brain conventional contrast-enhanced sequences to assess the grade of the gliomas and link it to the histology report. Following this identification, tissue metabolites were calculated using MRS.ResultsThe patients’ age ranged from 13 to 80 years, with a mean age of 49.5 years. Male patients comprised 57.5% of the total study population, while female patients comprised 42.5%. Overall, 23.75% of patients had low-grade tumors, while 76.25% had high-grade tumors. Low-grade tumors had a choline (Cho)/creatine (Cr) metabolite ratio of 1.7421, whereas high-grade tumors had an average Cho/Cr metabolite ratio of 2.5575. N-acetyl aspartate (NAA)/Cr ratio was 1.6368 in low grade and 0.6734 in high-grade tumors. Sensitivity of 77% and specificity of 84.2% were noted, with 78.75% diagnostic accuracy for the Cho/Cr ratio.ConclusionsMultivoxel MRS has been shown to reliably predict the grade of gliomas despite its non-invasive nature and lack of procedural challenges. When used together Cho/Cr and NAA/Cr ratios and histopathology can accurately determine tumor grade and can be used as a supplementary non-invasive technique.

NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY

Abstract PURPOSE Reporting guidelines are crucial in model development studies to ensure the quality, transparency and objectivity of reporting. While machine learning (ML) models have proven themselves effective in predicting glioma grade, their potential use can only be determined if they are clearly and comprehensively reported. Reporting quality has not yet been evaluated for ML glioma grade prediction studies, to our knowledge. We measured published literature against the TRIPOD Statement, a checklist of items considered essential for the reporting of diagnostic studies. MATERIALS AND METHODS A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Publications were screened in Covidence and scored against the 27 items in the TRIPOD Statement that were relevant and applicable. RESULTS The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles met the criteria for our study. The mean adherence rate to TRIPOD was 44.4% (range: 22.2% - 66.7%), with poor reporting adherence in categories including abstract (0%), model performance (0%), title (1.2%), justification of sample size (2.3%), full model specification (2.3%), participant demographics and missing data (7%). Studies had high reporting adherence in categories including results interpretation (100%), background (98.8%), study design/source of data (96.5%), and objectives (95.3%). CONCLUSION Existing publications on the use of ML in glioma grade prediction have a low overall quality of reporting. Improvements can be made in the reporting of titles and abstracts, justification of sample size, and model specification and performance.

Prediction of Glioma Grade Using Intratumoral and Peritumoral Radiomic Features From Multiparametric MRI Images.

The accurate prediction of glioma grade before surgery is essential for treatment planning and prognosis. Since the gold standard (i.e., biopsy)for grading gliomas is both highly invasive and expensive, and there is a need for a noninvasive and accurate method. In this study, we proposed a novel radiomics-based pipeline by incorporating the intratumoral and peritumoral features extracted from preoperative mpMRI scans to accurately and noninvasively predict glioma grade. To address the unclear peritumoral boundary, we designed an algorithm to capture the peritumoral region with a specified radius. The mpMRI scans of 285 patients derived from a multi-institutional study were adopted. A total of 2153 radiomic features were calculated separately from intratumoral volumes (ITVs)and peritumoral volumes (PTVs)on mpMRI scans, and then refined using LASSO and mRMR feature ranking methods. The top-ranking radiomic features were entered into the classifiers to build radiomic signatures for predicting glioma grade. The prediction performance was evaluated with five-fold cross-validation on a patient-level split. The radiomic signatures utilizing the features of ITV and PTV both show a high accuracy in predicting glioma grade, with AUCs reaching 0.968. By incorporating the features of ITV and PTV, the AUC of IPTV radiomic signature can be increased to 0.975, which outperforms the state-of-the-art methods. Additionally, our proposed method was further demonstrated to have strong generalization performance in an external validation dataset with 65 patients. The source code of our implementation is made publicly available at https://github.com/chengjianhong/glioma_grading.git.

Comparing amide proton transfer imaging with dynamic susceptibility contrast-enhanced perfusion in predicting histological grades of gliomas: a meta-analysis.

Background As a subtype of chemical exchange saturation transfer imaging without contrast agent administration, amide proton transfer (APT) imaging has demonstrated the potential for differentiating the histologic grades of gliomas. Dynamic susceptibility contrast-enhanced perfusion, a perfusion-weighted imaging technique, is a well-established technique in grading gliomas. Purpose To compare the ability of amide proton transfer and dynamic susceptibility contrast-enhanced imaging for predicting the grades of gliomas. Material and Methods A comprehensive literature search was performed independently by two observers to identify articles about the diagnostic performance of amide proton transfer and dynamic susceptibility contrast-enhanced perfusion in predicting the grade of gliomas. Summary estimates of diagnostic accuracy were obtained by using a random-effects model. Results Of 179 studies identified, 23 studies were included the analysis. Eight studies evaluated amide proton transfer and 16 studies evaluated dynamic susceptibility contrast-enhanced perfusion with the parameter rCBV. The pooled sensitivities and specificities of each study’s best performing parameter were 88% (95% confidence interval [CI] 74–95) and 89% (95% CI 78–95) for amide proton transfer, and 95% (95% CI 87–98), 88% (95% CI 81–93) for perfusion-weighted imaging–dynamic susceptibility contrast-enhanced perfusion, respectively. The pooled sensitivities and specificities for grading gliomas using the two most commonly evaluated parameters, were 92% (95% CI 80–97) and 90% (95% CI 75–96) for APTmax, and 97% (95% CI 91–99) and 87% (95% CI 80–92) for rCBVmax, respectively. Conclusion Considering the similar performance of APT and dynamic susceptibility contrast-enhanced (DSC) in predicting glioma grade, the former method appears preferable since it needs no contrast agent.

Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images.

Purpose To evaluate the feasibility of a standardized protocol for acquisition and analysis of dynamic contrast material-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging in a multicenter clinical setting and to verify its accuracy in predicting glioma grade according to the new World Health Organization 2016 classification. Materials and Methods The local research ethics committees of all centers approved the study, and informed consent was obtained from patients. One hundred patients with glioma were prospectively examined at 3.0 T in seven centers that performed the same preoperative MR imaging protocol, including DCE and DSC sequences. Two independent readers identified the perfusion hotspots on maps of volume transfer constant (Ktrans), plasma (vp) and extravascular-extracellular space (ve) volumes, initial area under the concentration curve, and relative cerebral blood volume (rCBV). Differences in parameters between grades and molecular subtypes were assessed by using Kruskal-Wallis and Mann-Whitney U tests. Diagnostic accuracy was evaluated by using receiver operating characteristic curve analysis. Results The whole protocol was tolerated in all patients. Perfusion maps were successfully obtained in 94 patients. An excellent interreader reproducibility of DSC- and DCE-derived measures was found. Among DCE-derived parameters, vp and ve had the highest accuracy (are under the receiver operating characteristic curve [Az] = 0.847 and 0.853) for glioma grading. DSC-derived rCBV had the highest accuracy (Az = 0.894), but the difference was not statistically significant (P > .05). Among lower-grade gliomas, a moderate increase in both vp and rCBV was evident in isocitrate dehydrogenase wild-type tumors, although this was not significant (P > .05). Conclusion A standardized multicenter acquisition and analysis protocol of DCE and DSC MR imaging is feasible and highly reproducible. Both techniques showed a comparable, high diagnostic accuracy for grading gliomas. © RSNA, 2018 Online supplemental material is available for this article.

Dynamic Contrast-Enhanced MRI in Low-Grade Versus Anaplastic Oligodendrogliomas.

Low-grade and anaplastic oligodendrogliomas are often difficult to differentiate on the basis of conventional MR imaging characteristics. Dynamic contrast-enhanced (DCE) MRI can assess tumor microvasculature and has demonstrated utility for predicting glioma grade and prognosis in primary brain tumors. The aim of our study was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K(trans) ) derived from DCE MRI in differentiating between grade II and grade III oligodendrogliomas. Twenty-four consecutive patients with pathologically confirmed oligodendroglioma (World Health Organization grade II, n = 14 and grade III, n = 10) were retrospectively assessed. Pretreatment DCE MRI was performed and regions of interest were manually drawn around the entire tumor volume to calculate Vp and K(trans) . The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were performed to compare pharmacokinetic parameters between the 2 groups. The Vpmean values for grade III oligodendrogliomas were significantly higher (P = .03) than those for grade II oligodendrogliomas. The K(trans) mean values were higher in grade III lesions, but the difference between the 2 groups was not statistically significant (P > .05). Based on ROC analysis, the Vpmean (area under curve = .757, SD = .1) cut-off value that provided the best combination of high sensitivity and specificity to distinguish between grade II and III oligodendrogliomas was 2.35 (P < .03). The results of our study suggest the DCE MRI parameter Vpmean can noninvasively differentiate between grade II and grade III oligodendrogliomas.

NIMG-57QUANTITATION OF 18F-DOPA UPTAKE ON PET PREDICTS TUMOR GRADE WITH HIGH SENSITIVITY AND SPECIFICITY IN NEWLY-DIAGNOSED GLIOMA: UCLA EXPERIENCE

PURPOSE: The aims of this study were to determine whether quantifying the amount of 18F-DOPA uptake in newly-diagnosed gliomas predicted tumor grade and was more accurate than qualitative reporting. METHODS: A retrospective study was performed at UCLA Medical Center to identify patients with pathology-proven glioma who underwent 18F-DOPA PET scanning ≤6 weeks prior to biopsy/resection. Tumor grade was obtained by standard pathology assays. 18F-DOPA uptake was quantified by determining maximum standardized uptake value (SUVmax) based on the injected radiotracer dose per patient's body weight in the region corresponding to the MRI-based lesion (tumor SUVmax). Tumor-to-normal-brain tissue (T/N) ratio was defined as dividing the tumor SUVmax by the SUVmax of the contralateral normal tissue at the level of the centrum semiovale, and tumor-to-basal ganglia (T/BG) ratio was also determined. Patient demographics, tumor grade, and imaging metrics were tabulated and analyzed. RESULTS: 982 18F-DOPA PET scans from January 2003 through February 2015 in 523 individual patients were reviewed, and scans in 23 patients (n = 9 female, n = 14 male with age 46.3 ± 16.4 years; n = 9 grade II, n = 7 grade III, n = 7 grade IV) were eligible for study. T/N ratio was 1.5 ± 0.6, 1.8 ± 0.4, and 3.0 ± 1.0 for grade II, III, and IV gliomas, respectively. Grade IV T/N ratio was significantly greater than that in grade II (p = 0.008) and grade III (p = 0.02) gliomas. Applying T/N ratio cut-offs of 1-1.69/1.7-2.39/ ≥ 2.4 for grade II/III/IV gliomas, the respective sensitivity/specificity were 88.9%/100%, 85.7%/87.5%, and 71.4%/93.8%. This is in contrast to the diagnostic accuracy (based on the qualitative report in the chart) of 66.7%, 85.7%, and 100%, respectively. CONCLUSIONS: The results demonstrate the utility of quantitative T/N ratio cut-offs in predicting glioma grade. Additionally, a population of grade II/III gliomas that was not diagnosed based on the qualitative report was identified. Future work will correlate 18F-DOPA uptake with survival and tumor molecular profile.

Correlation between minimal apparent diffusion coefficient and expression of Ki-67 in glioma

To explore the correlation between minimal apparent diffusion coefficient (minADC) and expression of Ki-67 in glioma. Findings from 78 patients with pathologically proven gliomas (grade II:n = 28, grade III: n = 21, grade IV: n = 29) were analyzed retrospectively. The minADC value of each tumor was determined on ADC maps. The Ki-67 labeling index (LI) of surgical specimen was measured. The results were analyzed statistically. The mean value of minADC for gliomas of grade II ((1.23 ± 0.23)×10(-3) mm(2/s)) was higher than that of grade III ((0.92 ± 0.20)×10(-3) mm(2/s)) (P < 0.01) and than that of grade IV ((0.80 ± 0.16)×10(-3) mm(2)/s) (P < 0.01). But no significant difference existed between the mean value of minADC of grade III and that of grade IV (P = 0.069). There were significant negative correlations between minADC and different tumor grades (r = -0.678, P < 0.01). The mean value of Ki-67 LI showed significant differences between grade II (3.6% ± 2.3%), grade III (14.8% ± 7.3%) and grade IV (29.9% ± 13.1%) (P < 0.01). There were significant positive correlations between Ki-67 LI and different tumor grades (r = 0.835, P < 0.01). And a negative correlation existed between minADC and Ki-67 LI (r = -0.556, P < 0.01). The value of minADC is useful for predicting glioma grade.

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.

Retraction Note to: Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging

Purpose The purpose of our study was to determine the statistical significance of thresholds of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and ADC ratios in grading cerebral gliomas. Materials and methods In this retrospective study, 51 patients with histopathologically confirmed primary cerebral gliomas who had undergone conventional MR imaging, dynamic contrast-enhanced T2*-weighted perfusion MR imaging and diffusion MR imaging were included. A retrospective blinded analysis of the imaging findings including the perfusion and diffusion parameters was done. The rCBV measurements were obtained from regions of maximum perfusion. Minimum ADC values were obtained from the region of maximum hypointensity within the tumor and from the corresponding opposite white matter. Tumor grade determined with the two methods were then compared with the histopathologic grade. Mann–Whitney tests were performed to compare the DWI and PWI between tumor types. Receiver operating characteristic analyses were performed to determine optimum thresholds for tumor grading and also to calculate the sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas. Results Statistical analysis demonstrated a threshold value of 2.91 for rCBV to provide sensitivity, specificity, PPV, and NPV of 94.7, 93.75, 90.0, and 96.8%, respectively, in determining high-grade gliomas. An ADC value of 98.50 mm2/s was defined as a threshold below which tumors were classified as high-grade gliomas and a sensitivity, specificity, PPV, and NPV of 90, 87.1, 81.81 and 93.10% respectively, were obtained. Significant differences were noted in the rCBV ratios, ADC and ADC ratios between low- and high-grade gliomas (P < 0.0001). Conclusion Combining PWI and DWI with conventional MR imaging increases the accuracy of pre-operative imaging grading of glial neoplasms. The rCBV measurements had the most superior diagnostic performance in predicting glioma grade. Absolute ADC values or ADC ratios were also helpful in preoperative grading of gliomas. Threshold values can be used in a clinical setting to evaluate tumors preoperatively for histologic grade and provide a means for guiding treatment and predicting postoperative patient outcome.

Utility of multiparametric 3-T MRI for glioma characterization.

Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural magnetic resonance imaging and MR measurements of perfusion, diffusion, and whole-brain spectroscopic parameters for glioma grading. Fifty-six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, diffusion tensor imaging, and volumetric whole-brain MR spectroscopic imaging. Receiver-operating characteristic analysis was performed using the relative cerebral blood volume (rCBV), apparent diffusion coefficient, fractional anisotropy, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together. The rCBV individually classified glioma as low and high grade with a sensitivity and specificity of 100 and 88 %, respectively, based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2% error and sensitivity and specificity of 100 and 96%, respectively. Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters.

Potential role for magnetoencephalography in distinguishing low- and high-grade gliomas: a preliminary study with histopathological confirmation

Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non- and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.

Spectroscopic Evaluation of Glioma Grading at 3T: The Combined Role of Short and Long TE

Purpose. To evaluate the diagnostic value of 3T 1H-MRS in grading cerebral gliomas using short and long echo times. Methods. 1H-MRS was performed on 71 patients with untreated cerebral gliomas. Metabolite ratios of NAA/Cr, Cho/Cr, Cho/NAA, and mI/Cr were calculated for short and long TE and compared between low and high grade gliomas. Lipids were qualitatively evaluated. ROC analysis was performed to obtain the cut-off values for the metabolic ratios presenting statistical difference between the two glioma grades. Results. Intratumoral Cho/Cr at both TEs and long TE Cho/NAA were significantly different between low and high grade gliomas. Peritumoral NAA/Cr of both TEs, as well as long TE Cho/Cr and Cho/NAA ratios, significantly differentiated the two tumor grades. Diagnostic sensitivity of peritumoral short TE NAA/Cr proved to be superior over the other metabolic ratios, whereas intratumoral short TE Cho/Cr reached the highest levels of specificity and accuracy. Overall, short TE 1H-MRS reached higher total sensitivity in predicting glioma grade, over long TE. Conclusion. An advantage was found in using short TE over long TE 1H-MRS in the discrimination of low versus high grade gliomas. Moreover, the results suggested that the peritumoral area of gliomas may be more valuable in predicting glioma grade than using only the intratumoral area.