Preoperative Grading Of Gliomas Research Articles

The impact of diffusion and perfusion-weighted imaging on glioma grading

Abstract Objectives Determining the grade of a glioma is extremely important for treatment planning and prognosis prediction. The study aimed to evaluate the usefulness of multiparametric perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) in preoperative glioma grading. Methods In this retrospective study, 63 individuals with brain tumors histologically confirmed, of which 23 had low-grade gliomas (LGGs) and 40 had high-grade gliomas (HGGs) were involved. We conducted this paper on apparent diffusion coefficient (ADC) maps using the entire tumor volume method, allowing us to use all ADC values of the tumor. Small-sample regions of interest (ROIs) were drawn to collect parameters of relative cerebral blood flow (rCBF), cerebral blood flow (CBF), and relative cerebral blood volume (rCBV), from both the tumor core and peritumoral edema. The PWI and DWI metrics were compared to identify the most accurate distinguishing HGGs and LGGs, analyze receiver operating characteristics (ROC), and evaluate the diagnostic performance using solitary parameters and combined. Results In diffusion MRI, there were significant differences in minimum ADC and mean ADC between LGGs and HGGs (p<0.05), with the larger area under the curve (AUC) of 0.898 found for mean ADC at a cut-off value of 1.275, with sensitivity of 82.6 % and specificity of 90 %. The maximum ADC value did not differ significantly (p>0.05). All perfusion parameters in both the tumor core and peritumoral edema area were significantly greater values in cases of HGG compared to LGG (p<0.001), with the highest AUC of 0.946 found for solid tumor rCBV value (rCBVt), the cut-off is 3.585, sensitivity of 85 % and specificity of 100 %. Combining mean ADC and rCBVt provided an excellent AUC of 0.975, a sensitivity of 92.5 %, and a specificity of 91.3 % for differentiating between HGGs and LGGs. Conclusions Perfusion and diffusion MRI are valuable in discriminating between high-grade and low-grade gliomas, with the major criterion in the decision-making process being the combined mean ADC and rCBVt parameters.

Magnetic Resonance Spectroscopy of Intra-axial Gliomas With Histopathological Correlation in a Tertiary Care Center of Eastern Nepal.

Background and objective Magnetic resonance spectroscopy (MRS) is a magnetic resonance imaging technique used to identify in vivo metabolites non-invasively within the tissue of interest. It plays an important role in diagnosing brain lesions, particularly tumors and infections. There are certain metabolites whose levels are increased or decreased in brain tumors, the ratios of which can also be used to grade the tumors as high- or low-grade. This study aimed to assess the spectrum of different metabolites in intraaxial gliomas using magnetic resonance spectroscopy and to assess the usefulness of their ratios for grading gliomas into high-grade and low-grade. Methods This descriptive cross-sectional study was performed in the radiology department of Nobel Medical College and Teaching Hospital, Biratnagar, Nepal over one year (September 2019 to September 2020). Thirty-five patients diagnosed as having intra-axial tumors were enrolled. After taking informed consent the examination findings were recorded in structured proforma. Siemens' 3 Tesla open magnet MAGNETOM Skyra (Siemens Healthineers AG, Munich, Germany) MR scanner was used to evaluate each patient. Data was analyzed using the software Statistical Package for Social Sciences (SPSS), version 26.0 (IBM Corp., Armonk, NY). Results Out of 35 patients scanned, 18 had high-grade glioma and 17 had low-grade glioma. High-grade glioma had a choline/creatine (Cho/Cr) ratio of 2.44 ± 0.78 and a choline/N-acetyl-aspartate (Cho/NAA) ratio of 2.05 ± 0.84. Low-grade glioma had a Cho/Cr ratio of 1.48 ± 0.50 and a Cho/NAA ratio of 1.41 ± 0.19. Fourteen out of eighteen high-grade gliomas had raised lipid/lactate peaks. The sensitivity, specificity, positive and negative predictive values (PPV and NPV), and accuracy for diagnosing high-grade glioma with a Cho/Cr ratio cut-off of 1.5 was 83.3 %, 82.4%, 83.3%,82.4 %, and 82.85% respectively. Conclusion MRS metabolite ratios can be used to diagnose and grade gliomas. Cho/Cr, Cho/NAA, and the presence or absence of lipid/lactate peak can significantly improve the sensitivity, specificity, predictive values, and accuracy of preoperative glioma grading when used in conjunction with conventional MRI.

Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach.

Accurate grading of gliomas is a challenge in imaging diagnosis. This study aimed to evaluate the performance of a machine learning (ML) approach based on multiparametric diffusion-weighted imaging (DWI) in differentiating low- and high-grade adult gliomas. A model was developed from an initial cohort containing 74 patients with pathology-confirmed gliomas, who underwent 3 tesla (3T) diffusion magnetic resonance imaging (MRI) with 21 b values. In all, 112 histogram features were extracted from 16 parameters derived from seven diffusion models [monoexponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), continuous-time random walk (CTRW), stretched-exponential, and statistical]. Feature selection and model training were performed using five randomly permuted five-fold cross-validations. An internal test set (15 cases of the primary dataset) and an external cohort (n=55) imaged on a different scanner were used to validate the model. The diagnostic performance of the model was compared with that of a single DWI model and DWI radiomics using accuracy, sensitivity, specificity, and the area under the curve (AUC). Seven significant multiparametric DWI features (two from the stretched-exponential and FROC models, and three from the CTRW model) were selected to construct the model. The multiparametric DWI model achieved the highest AUC (0.84, versus 0.71 for the single DWI model, P<0.05), an accuracy of 0.80 in the internal test, and both AUC and accuracy of 0.76 in the external test. Our multiparametric DWI model differentiated low- (LGG) from high-grade glioma (HGG) with better generalization performance than the established single DWI model. This result suggests that the application of an ML approach with multiple DWI models is feasible for the preoperative grading of gliomas.

Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading.

Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.

Perfusion measurement in brain gliomas using velocity-selective arterial spin labeling: comparison with pseudo-continuous arterial spin labeling and dynamic susceptibility contrast MRI.

To evaluate the performance of velocity-selective (VS) ASL among patients with untreated gliomas by comparing with both pseudo-continuous (PC) ASL and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). Forty-four consecutive patients with newly diagnosed glioma who underwent preoperative perfusion MRI including VSASL, PCASL, and DSC-PWI between 2017 and 2019 were retrospectively evaluated. Visual inspection was performed to evaluate the tumor signal intensity relative to gray matter based on 1-5 score criteria and weighted kappa was used to evaluate the pair-wise concordance between VSASL or PCASL and DSC-PWI. The relative tumor blood flow (rTBF) was measured from sampling intra-tumoral areas of hot-spot on the blood flow map and normalized against the contralateral normal gray matter blood flow. Linear regression and Bland-Altman analyses were performed to evaluate the correlation and agreement of rTBF measurements between ASL methods and DSC-PWI. The ROC analysis was constructed to determine the diagnostic performance of three perfusion methods for grading gliomas. TBF maps derived from VSASL were more comparable with DSC-PWI than PCASL on visual inspection (weighted kappa of 0.90 vs 0.68). In quantitative analysis, VSASL-rTBF yielded higher correlation with the values from DSC-PWI than PCASL-rTBF (R2 = 80% vs 47%, p < 0.001 for both). Both ASL and DSC-derived rTBF showed good distinction between low-grade and high-grade gliomas (p < 0.001). Compared to PCASL, VSASL yielded superior diagnostic sensitivity, specificity, and accuracy in glioma grading. VSASL showed great promise for accurate quantification of TBF and could potentially improve the diagnostic performance of ASL in preoperative grading of gliomas. •VSASL demonstrated a greater agreement with DSC-PWI than with PCASL on visual inspection and perfusion quantification. •VSASL showed a higher diagnostic sensitivity, negative predictive value, and accuracy than PCASL for glioma grading. •With the advantages of insensitivity to transit delay and no need of prescribing a labeling plane, VSASL could potentially improve the diagnostic performance of ASL for a more accurate, noninvasive quantification of TBF in patients with glioma.

Role of Susceptibility Weighted Images (SWI) in Grading of Brain Glioma

Abstract Background Glioma is the most common intracranial primary tumor of central nervous system (CNS) and accounts for about 70% of primary adult malignant brain tumors. The optimum therapeutic treatment and prognosis evaluation largely depends on the tumor pathological grades. Objective To evaluate the role of susceptibility weighted imaging (SWI) in grading of cerebral gliomas. The magnetic resonance imaging (MRI) results were compared and correlated to the pathology results to evaluate its role. The pathological grading of the glioma was done according to WHO 2007 classification system. Patients and Methods This was a retrospective study that included 35 adult patients, (11females &amp; 24 males), their ages ranging from 18 years to 73 years. They were pathologically proven glioma patients ranging from grade I to grade IV. All the patients were referred from neurosurgeon to our radiology center (private center). This study was carried out during the period between January 2017 and November 2018. Results In our study, there were a strong positive correlation between both conventional imaging and pathological grading and between pathological and SWI grading. Using SWI sequence in grading of glioma will be very beneficial in patients with contraindication to contrast. Conclusion SWI using 3T MR system may provide quite useful information for preoperative glioma grading. There seems to be a strong positive correlation between pathological grading and SWI grading system for glioma. The main disadvantage for SWI is the extra time added to the usual time of routine MRI protocol used in cases of intra cranial space occupying lesions (SOL).

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10⁻³ mm²/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

RCBV and ADC based grading of gliomas with glimpse into radiogenomics

Purpose. The present study was carried out to study the role of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and MR spectroscopy in grading gliomas to help the surgeon plan the approach and extent of surgery as well as judge the need for any adjuvant radio/chemotherapy.&#x0D; Methods. 65 patients with glioma were prospectively studied with MRI. Basic MR sequences (T1W, T2W, T2W/FLAIR) were followed by diffusion-weighted (DW) imaging with b value of 1000 (minimum ADC values used for analysis). Then the patients were administered Gadobenate Dimeglumine/ Meglumine Gadoterate in a dose of 0.1mmol/kg at a rate of 4ml/sec after which 20ml of saline was flushed at a rate of 4ml/sec and T2*W/FFE dynamic images were acquired; dynamics showing maximum fall in the intensity were used for creating rCBV and rCBF maps and calculating rCBV. Single voxel spectroscopy (SVC) was done using the PRESS sequence with intermediate TE of 144ms. NAA/Cr, Cho/Cr, Cho/NAA, Cho+Cr/NAA and NAA/Crn ratios (NAA from the tumour, Crn from the normal side) were calculated.&#x0D; Results. Grade I gliomas showed minimum ADC&gt;0.84x10-3mm2/s and maximum rCBV&lt;1.9ml/100gm, grade II gliomas showed min ADC 0.75-0.84x10-3mm2/s and max rCBV of 1.9-2.6ml/100gm, grade III had min ADC of 0.70-0.75x10-3mm2/s and max rCBV of 2.7-3.0ml/100gm, while grade IV tumors showed min ADC&lt;0.70x10-3mm2/s and max rCBV&gt;3.0ml/100gm. rCBV values were better than ADC values in differentiating grade I from II and grade II from III. The ADC values were better than rCBV values in differentiating grade III from grade IV.&#x0D; Conclusions. Both minimum ADC and maximum rCBV within the tumour were significant but these parameters within peritumoral oedema were not significant in grading gliomas. Though lipid and lactate (especially lipid) peaks were found more frequently in higher-grade tumours, various spectroscopy parameters were not significant in grading gliomas. Preoperative grading of gliomas with the help of advanced MR parameters like ADC and rCBV can help the surgeon plan the approach and extent of surgery as well as judge the need for any adjuvant radio/chemotherapy. Advancing radio-genomic and radiomic technologies can supplement the current radiologic methods of diagnosis and prognosis.

Arterial spin labeling in the grading of brain gliomas: could it help?

BackgroundGliomas are characterized by high morbidity and mortality with low cure and high recurrence rates, which depends to a great degree on the angiogenesis of the tumor. Assessment of such angiogenesis by perfusion techniques is of utmost importance for the preoperative grading of gliomas. The purpose of this study was to assess the role of arterial spin labeling (ASL) perfusion as a non-contrast MRI technique in the grading of brain gliomas, in correlation with the dynamic susceptibility contrast perfusion imaging (DSC-PI). The study was carried out on 35 patients admitted to the Neurosurgery Department with MRI features of gliomas and sent for further perfusion imaging. Non-contrast ASL followed by DSC-PI was done for all cases. The final diagnosis of the cases was established by histopathology.ResultsFourteen patients (14/35) had low-grade gliomas while twenty-one (21/35) had high-grade gliomas. In low-grade gliomas, four cases out of 14 were falsely graded as high-grade tumors showing hyperperfusion on ASL, three of which showed DSC-PI hypoperfusion. In high-grade gliomas, two cases out of 21 were interpreted as an indeterminate grade by ASL showing isoperfusion, however showed hyperperfusion on DSC-PI. ROC curve analysis showed ASL-derived rCBF > 2.08 to have 80.95% sensitivity, 85.71% specificity, and overall accuracy of 82.86% compared to 100% sensitivity, specificity, and accuracy of DSC-PI-derived rCBV and rCBF of > 1.1 and > 0.9, respectively. A significant positive correlation was noted between ASL and DSC-PI with correlation coefficient reaching r = 0.80 between ASL-rCBF and DSC-rCBF (p < 0.01) and r = 0.68 between ASL and DSC-rCBV (p < 0.01).ConclusionsASL is a relatively recent non-contrast perfusion technique that obtains results which are in fair agreement with the more established DSC perfusion imaging making it an alternative method for preoperative assessment of perfusion of gliomas, especially for patients with contraindications to contrast agents.

Evaluation of Diagnostic Accuracy of the Approved Tumor Mapping Protocol in Grading of Glial Tumors

Objective: Evaluation of Diagnostic Accuracy of the Approved Tumor Mapping Protocol in Grading of Glial Tumors.&#x0D; Methods: This descriptive cross-sectional study was performed on patients aged 2 to 82 years with glial tumor. Patients were referred to the hospital for tumor mapping and underwent imaging with simultaneous methods of MRS and magnetic resonance (MR) perfusion and conventional MRI under the supervision of NIAG group. Then, the results of the second evaluation, including the ratios of the desired metabolites and the amount of blood flow, permeability of the target area were compared with the results of pathology. The results were analyzed by SPSS software version 24.&#x0D; Results: In this study, 30 patients were included. Sensitivity, specificity, positive and negative predictive value for the determination of high-grade glioma with peripheral/internal rCBV were 100/100%, 100/93%, 93/100% and 100.100%, respectively. Sensitivity, specificity, positive and negative predictive value for the diagnosis of glioma by using peripheral/internal rCBV and thresholds of 2.65 and 1.06 were 100/100%, 93/100%, 93/100% and 100/100%, respectively. Sensitivity, specificity, positive predictive value and negative predictive value were determined for diagnosis of high-grade glioma tumor using Ch + Cr / NAA Cho / Cr and Cho / NAA ratios with detection threshold of 2.97 (93.3%), 3.5 (78.9%,100%, 100%, and 73.3%), and 2.1 (100%). Threshold values of 3.5, 2.1 and 2.97 were obtained using Cho / Cr, Ch + Cr / NAA and Cho / NAA, respectively, for the detection of high-grade gliomas. The combination of rCBV, Cho / Cr, Ch + Cr / NAA and Cho / NAA had sensitivity, specificity, positive and negative predictive value of 67.7%, 80%, 77% and 70.5%, respectively. Significant differences in rCBV and Cho / Cr, Cho / NAA and NAA / Cr ratios were observed between low- and high-grade gliomas (P &lt;0.0001).&#x0D; Conclusion: Preoperative grading of glioma based on routine MR imaging is often unreliable. As a result, measuring rCBV and Cho / Cr and Cho / NAA ratios independently and somewhat together can significantly improve the sensitivity and predictive values of preoperative glioma grading.

Role of Diffusion and Perfusion Magnetic Resonance Imaging in Predicting the Histopathological Grade of Gliomas - A Prospective Study.

Context:Gliomas are the most common brain tumors. In addition to conventional magnetic resonance imaging (MRI) techniques, a variety of new techniques offers more than the anatomic information. The new MRI techniques include perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI).Aims:The aim of this study is to assess the sensitivity, specificity, predictive value, and accuracy of diffusion- and perfusion-weighted MRI in the preoperative grading of gliomas.Setting/Design:The study was conducted in the Department of Neurosurgery, Pathology, and Radiodiagnosis, Sher-e-Kashmir Institute of Medical Sciences, Kashmir, India, which is the only tertiary care neurosurgical center in the state. It was a prospective study.Patients and Methods:Thirty-one consecutive patients with gliomas were included in the study. All the patients were evaluated by a standard conventional contrast-enhanced study on Siemens 1.5 Tesla MRI. In addition to the standard MRI, diffusion- and perfusion-weighted MRI were also performed. The histopathological grading of the tumor was done as per the WHO classification of 2007. The sensitivity, specificity, predictive value, and accuracy of diffusion- and perfusion-weighted MRI in determining tumor grade were calculated. Comparison was done between PWI, DWI findings, and WHO histopathological grading.Analysis Method:The statistical analysis was done using the Statistical Package for the Social Sciences, and receiver operating characteristic curves were used to estimate sensitivity, specificity, and accuracy.Results:The overall sensitivity of PWI (with regional cerebral blood volume cutoff of 1.7) in the preoperative assessment of high-grade gliomas was 82.6% and specificity was 75%, the positive predictive value (PPV) was 90.48%, and the negative predictive value (NPV) was 60%. The overall accuracy was 80.65%. In case of DWI, the sensitivity was 69.57% and the specificity was 75%, and the PPV and NPVs were 88.8% and 46.15%, respectively. The overall accuracy was 71%.Conclusion:Our results clearly show higher accuracy of diffusion- and perfusion-weighted MRI in assessment of glioma grade as compared to conventional MRI. This information can prove very useful for the operating neurosurgeon in preoperative assessment and surgical planning. Postoperatively, the neuropathologist can also benefit from such information.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

Preoperative Grading of Glioma Using Dynamic Susceptibility Contrast MRI: Relative Cerebral Blood Volume Analysis of Intra-tumoural and Peri-tumoural Tissue

ObjectivesTo assess the usefulness of intra-tumor and peri-tumoral relative cerebral blood volume (rCBV) in preoperative glioma grading. Patients and methods21 patients with histopathologically confirmed glioma were included. Imaging was achieved on a 1.5T MRI scanner. Dynamic susceptibility contrast (DSC) MRI was performed using T2* weighted gradient echo-planner imaging (EPI). Multiple regions of interest (ROIs) have been drawn in the hotspots regions, the highest ROI has been selected to represent the rCBV of each intra-tumoral and peri-tumoral regions. Based on histopathology, tumors were subdivided into low grade and high grade. Receiver operating characteristic analysis (ROC) of rCBV, of both intra-tumoral and peri-tumoral regions, was performed to find cut-off values between high and low-grade tumors. The resulting sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. ResultsBased on the histopathology, high-grade glioma (HGG) represented 76.2% whereas low-grade glioma (LGG) represented 23.8%. Both intra-tumoral and peri-tumoral rCBV of HGG were significantly higher than those of LGG. A cut-off value >2.9 for intra-tumoral rCBV provided sensitivity, specificity, and accuracy of 80%, 100%, and 85.7% respectively to differentiate between HGG and LGG. Additionally, the cut-off value >0.7 for peri-tumoral rCBV provided sensitivity, specificity, and accuracy of 100%, 66.6%, and 90.5% respectively to differentiate between HGG and LGG. ConclusionrCBV of each of intra-tumoral and peri-tumoral rCBV are significantly reliable for the preoperative distinction between HGG and LGG. Combined intra-tumoral and peri-tumoral rCBV provides overall better diagnostic accuracy and helps to decrease the invasive intervention for non-surgical candidates.

Differentiating the histologic grades of gliomas preoperatively using amide proton transfer‐weighted (APTW) and intravoxel incoherent motion MRI

The purpose of this work was to investigate the diagnostic performance of amide proton transfer-weighted (APTW) and intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in the preoperative grading of gliomas. Fifty-one patients with suspected gliomas were recruited and underwent a preoperative MRI examination that included APTW and IVIM sequences. All cases were confirmed by postsurgical histopathology. APTW signal intensity, true diffusion coefficient (D), perfusion fraction (f) and pseudo-diffusion coefficient (D*) were applied to assess the solid tumor component and contralateral normal-appearing white matter. The relative APTW signal intensity (rAPTW) was also used. Independent-sample and paired-sample t-tests were used to compare differences in MRI parameters between low-grade glioma (LGG) and high-grade glioma (HGG) groups. The diagnostic performance was assessed with the receiver operating characteristic curve. Twenty-six patients were pathologically diagnosed with LGG and 25 were diagnosed with HGG. APTW, rAPTW and f values were significantly higher (all p<0.001), whereas D values were significantly lower (p<0.001) in the HGG group than in the LGG group. There was no significant difference between D* values for the two groups. rAPTW had an area under the curve (AUC) of 0.957, with a sensitivity of 100% and a specificity of 84.6%, followed by APTW, f, D and D*. The combined use of APTW and IVIM showed the best diagnostic performance, with an AUC of 0.986. In conclusion, APTW and IVIM, as two promising supplementary sequences for routine MRI, could be valuable in differentiating LGGs from HGGs.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading.

To compare the efficacy of ultra-high and conventional mono-b-value DWI for glioma grading, in 109 pathologically confirmed glioma patients, ultra-high apparent diffusion coefficient (ADCuh)was calculated using a tri-exponential mode, distributed diffusion coefficients (DDCs) and α values were calculated using a stretched-exponential model, and conventional ADC values were calculated using a mono-exponential model. The efficacy and reliability of parameters for grading gliomas were investigated using receiver operating characteristic (ROC) curve and intra-class correlation (ICC) analyses, respectively. The ADCuh values differed (P < 0.001) between low-grade gliomas (LGGs; 0.436 ×10−3 mm2/sec) and high-grade gliomas (HGGs; 0.285 × 10−3 mm2/sec). DDC, a and various conventional ADC values were smaller in HGGs (all P ≤ 0.001, vs. LGGs). The ADCuh parameter achieved the highest diagnostic efficacy with an area under curve (AUC) of 0.993, 92.9% sensitivity and 98.8% specificity for glioma grading at a cutoff value of 0.362×10−3 mm2/sec. ADCuh measurement appears to be an easy-to-perform technique with good reproducibility (ICC = 0.9391, P < 0.001). The ADCuh value based in a tri-exponential model exhibited greater efficacy and reliability than other DWI parameters, making it a promising technique for glioma grading.

Preoperative glioma grading by MR diffusion and MR spectroscopic imaging

ObjectivesTo assess the diagnostic accuracy of DW-MRI, ADC value, and MRS in preoperative glioma grading because of its importance in treatment planning. Patients and methodsA prospective study included 30 patients with gliomas, based on CT and cMRI findings, referred from Neurosurgery Department for DWI and MRS. Results were correlated with histopathological diagnosis after surgical resection. ResultsADC values were significantly higher in low grade gliomas relative to high grade ones. The lowest value was for GBM, 100% sensitivity, specificity, PPV and NPP in glioma grading. MRS revealed higher Cho/NAA and Cho/Cr ratios in high grade neoplasms and characteristic elevated lipid peak with statistical significant difference between low grade and high grade gliomas. MRS was more accurate than ADC value in detecting peritumoral infiltration of high grade gliomas, but there were no statistically significant differences between anaplastic and GBM in tumoral and peritumoral regions. ConclusionDW-MRI had higher sensitivity, specificity and accuracy than cMRI and MRS in glioma grading while MRS is more accurate than ADC value in assessing peri-tumoral infiltration based on high metabolite ratios in peri-tumoral tissue for anaplastic glioma and GBM, but there were no statistically significant differences between high grade groups.

The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis.

Magnetic resonance spectroscopy (MRS) is a powerful tool for preoperative grading of gliomas. We performed a meta-analysis to evaluate the diagnostic performance of MRS in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). PubMed and Embase databases were systematically searched for relevant studies of glioma grading assessed by MRS through 27 March 2015. Based on the data from eligible studies, pooled sensitivity, specificity, diagnostic odds ratio and areas under summary receiver operating characteristic curve (SROC) of different metabolite ratios were obtained. Thirty articles comprising a total sample size of 1228 patients were included in our meta-analysis. Quantitative synthesis of studies showed that the pooled sensitivity/specificity of Cho/Cr, Cho/NAA and NAA/Cr ratios was 0.75/0.60, 0.80/0.76 and 0.71/0.70, respectively. The area under the curve (AUC) of the SROC was 0.83, 0.87 and 0.78, respectively. MRS demonstrated moderate diagnostic performance in distinguishing HGGs from LGGs using tumoural metabolite ratios including Cho/Cr, Cho/NAA and NAA/Cr. Although there was no significant difference in AUC between Cho/Cr and Cho/NAA groups, Cho/NAA ratio showed higher sensitivity and specificity than Cho/Cr ratio and NAA/Cr ratio. We suggest that MRS should combine other advanced imaging techniques to improve diagnostic accuracy in differentiating HGGs from LGGs. • MRS has moderate diagnostic performance in distinguishing HGGs from LGGs. • There is no significant difference in AUC between Cho/Cr and Cho/NAA ratios. • Cho/NAA ratio is superior to NAA/Cr ratio. • Cho/NAA ratio shows higher sensitivity and specificity than Cho/Cr and NAA/Cr ratios. • MRS should combine other advanced imaging techniques to improve diagnostic accuracy.

Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases

PurposeTo explore the role of Diffusion Tensor Imaging in preoperative glioma grading, as well as in differentiation between gliomas and metastatic brain tumors. We measured diffusion tensor variables in enhancement and edema regions, which were compared between the different subject groups. Materials and methodsWe performed DTI in 48 patients (11 Low Grade Gliomas, 27 High Grade Gliomas, 10 Single Metastatic brain tumors). We measured FA, λ1, λ2, λ3, ADC, Cl, Cp, Cs, RA, and VR in enhancing portions of tumors and edema regions. Additionally, ratios of enhancement to edema values were created for each variable. ResultsIn peritumoral edema, Cl and RA were proven to be significantly different in pair-wise comparisons, in addition to ADC, Cp, Cs and VR in enhancement regions. Enhancement to edema values were significantly different as well. ConclusionDiffusion tensor indices could be used for the differentiation between low and high grade gliomas, as well as for distinction between gliomas and metastases.

(18)F]-fluoro-L-thymidine PET and advanced MRI for preoperative grading of gliomas.

PurposeConventional MRI based on contrast enhancement is often not sufficient in differentiating grade II from grade III and grade III from grade IV diffuse gliomas. We assessed advanced MRI, MR spectroscopy and [18F]-fluoro-l-thymidine ([18F]-FLT) PET as tools to overcome these limitations.MethodsIn this prospective study, thirty-nine patients with diffuse gliomas of grades II, III or IV underwent conventional MRI, perfusion, diffusion, proton MR spectroscopy (1H-MRS) and [18F]-FLT-PET imaging before surgery. Relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), Cho/Cr, NAA/Cr, Cho/NAA and FLT-SUV were compared between grades.ResultsCho/Cr showed significant differences between grade II and grade III gliomas (p = 0.03). To discriminate grade II from grade IV and grade III from grade IV gliomas, the most relevant parameter was the maximum value of [18F]-FLT uptake FLTmax (respectively, p < 0.001 and p < 0.0001). The parameter showing the best correlation with the grade was the mean value of [18F]-FLT uptake FLTmean (R2 = 0.36, p < 0.0001) and FLTmax (R2 = 0.5, p < 0.0001).ConclusionWhereas advanced MRI parameters give indications for the grading of gliomas, the addition of [18F]-FLT-PET could be of interest for the accurate preoperative classification of diffuse gliomas, particularly for identification of doubtful grade III and IV gliomas.