Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype

BACKGROUND

This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status.

METHODS

Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen's kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models.

RESULTS

Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p < 0.001) and higher in the IDH wild-type group than in the IDH mutant group (p < 0.001). There were also significant differences in T2-FLAIR mismatch signs between HGGs and LGGs, as well as between IDH mutant and wild-type groups (p < 0.001). Two predictive models incorporating T2W hypointense ring, absence of T2-FLAIR mismatch, and age were constructed. The area under the ROC curve (AUROC) was 0.940 for predicting HGGs (95% CI = 0.907-0.972) and 0.830 for differentiating IDH wild-type (95% CI = 0.757-0.904).

CONCLUSIONS

The combination of T2W hypointense ring, absence of T2-FLAIR mismatch, and age demonstrate good predictive capability for HGGs and IDH wild-type. These findings suggest that MRI can be used noninvasively to predict glioma grading and IDH mutation status, which may have important implications for patient management and treatment planning.

T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas

AIM

To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models.

RESULTS

The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort.

CONCLUSION

The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.

Platelets as delivery vehicles for targeted enrichment of NO· to cerebral glioma for magnetic resonance imaging

Using a magnetic resonance imaging (MRI) contrast agent, MRI has made substantial contributions to glioma diagnosis. Metal-free MRI agents, such as the nano free radical nitric oxide (NO·) micelle, can overcome the inherent toxicity of metal-based agents in certain patient populations. However, the low spatial resolution of nano NO· micelle in MRI limits its clinical development. In this study, we pretreated platelets (PLTs) and loaded them with nano NO· micelles to synthesize NO·@PLT, which can overcome the low contrast and poor in vivo stability of nitroxide-based MRI contrast agents. The PLTs can serve as potential drug carriers for targeting and delivering nano NO· micelles to gliomas and thus increase the contrast in T1-weighted imaging (T1WI) of MRI. This drug carrier system uses the unique tumor-targeting ability of PLTs and takes advantage of the high signal presentation of steady nano NO· micelles in T1WI, thereby ultimately achieving signal amplification of glioma in T1WI. With the effect of PLTs-tumor cell adhesion, NO·@PLT has per-nitroxide transverse relativities of approximately 2-fold greater than those of free NO· particles. These features allow a sufficient NO·@PLT concentration to accumulate in murine subcutaneous glioma tumors up from 5 min to 2.5 h (optimum at 1.5 h) after systemic administration. This results in MRI contrast comparable to that of metal-based agents. This study established a promising metal-free MRI contrast agent, NO·@PLT, for glioma diagnosis, because it has superior spatial resolution owing to its high glioma-targeting ability and has significant translational implications in the clinic.

Quantitative analysis using intraoperative contrast-enhanced ultrasound in adult-type diffuse gliomas with isocitrate dehydrogenase mutations: association between hemodynamics and molecular features

PURPOSE

The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status.

METHODS

This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status.

RESULTS

Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas.

CONCLUSION

Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDHwildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas.

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.

Noninvasively evaluating the grade and IDH mutation status of gliomas by using mono-exponential, bi-exponential diffusion-weighted imaging and three-dimensional pseudo-continuous arterial spin labeling

OBJECTIVES

To noninvasively assess the diagnostic performance of diffusion-weighted imaging (DWI), bi-exponential intravoxel incoherent motion imaging (IVIM) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL) in differentiating lower-grade gliomas (LGGs) from high-grade gliomas (HGGs), and predicting the isocitrate dehydrogenase (IDH) mutation status.

MATERIALS AND METHODS

Ninety-five patients with pathologically confirmed grade 2-4 gliomas with preoperative DWI, IVIM and 3D pCASL were enrolled in this study. The Student's t test and Mann-Whitney U test were used to evaluate differences in parameters of DWI, IVIM and 3D pCASL between LGG and HGG as well as between mutant and wild-type IDH in grade 2 and 3 diffusion astrocytoma; receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance.

RESULTS

The value of ADC_mean, ADC_min, D_mean and D_min in HGGs were lower than in LGGs, while the value of CBF_mean and CBF_max in HGGs were higher than in LGGs. In ROC analysis, the AUC values of D_mean, D_min and CBF_max were 0.827, 0.878 and 0.839, respectively. The combination of CBF_max and D_min displayed the highest diagnostic performance to distinguish LGGs from HGGs, with AUC 0.906, sensitivity 82.4 %, and specificity 86.4 %. In grades 2 and 3 diffusion astrocytoma patients, ADC_min, D_mean, D_min, CBF_mean and CBF_max showed significant differences between IDH_mut and IDH_wt group (p < 0.05, 0.001, 0.001, 0.01 and 0.001, respectively) and the AUC values were 0. 709, 0.849, 0.919, 0.755 and 0.873, respectively. Similarly, the combination of CBF_max and D_min demonstrated the highest AUC value (0.938) in prediction IDH mutation status, with sensitivity 92.9 %, and specificity 95.5 %.

CONCLUSION

The combination of IVIM and 3D pCASL can be used in prediction histologic grade and IDH mutation status of glioma noninvasively.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study

Background

To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas.

Methods

Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC₁₅₀₀, α₁₅₀₀ fitted by 15 b-values (0-1,500 sec/mm²)and DDC₅₀₀₀ and α₅₀₀₀ fitted by 22 b-values (0-5,000 sec/mm²) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters.

Results

MDWI-derived α₁₅₀₀ negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC₁₅₀₀ and DDC₅₀₀₀ negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α₁₅₀₀(r=-0.485; P=0.005) and α₅₀₀₀(r=-0.395; P=0.025).

Conclusions

SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

Intravoxel incoherent motion DWI with different mathematical models in predicting rectal adenoma with and without canceration

PURPOSE

To evaluate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) with mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models for predicting rectal adenomas with canceration.

MATERIAL AND METHODS

Sixty patients with postoperative pathology-confirmed rectal adenoma (n = 31) and adenoma with canceration (n = 29) were enrolled and underwent IVIM-DWI scanning. The ME-derived apparent diffusion coefficient (ADC), BE-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), SE-derived distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured. The differences in each parameter between adenoma and canceration were compared. Multivariate binary logistic regression analysis was used to establish models for predicting rectal adenomas with canceration. Receiver operating characteristic curve analysis was applied to evaluate diagnostic performances of each model in terms of sensitivity, specificity, accuracy, and area under the curve (AUC).

RESULTS

The AUCs of ADC, D, D*, f, DDC and α were 0.851 (95 % confidence interval, CI, 0.735-0.930), 0.895 (95 % CI, 0.789-0.960), 0.720 (95 % CI, 0.589-0.828), 0.791 (95 % CI, 0.667-0.886), 0.841 (95 % CI, 0.724-0.923) and 0.738 (95 % CI, 0.608-0.834), respectively. The AUCs of BE and SE models were 0.927 (95 % CI, 0.829-0.978) and 0.874 (95 % CI, 0.763-0.946), respectively. The AUC, sensitivity, specificity, and accuracy of the derived four values (ADC, D, f, and DDC) from the combination of three models were 0.950, 96.6 % (95 % CI, 95.3-97.6 %), 80.6 % (95 % CI, 78.0-82.9 %), and 88.3 % (95 % CI, 86.2-90.2 %), respectively.

CONCLUSION

ADC can easily and effectively predict rectal adenomas with canceration. The BE model has a better combination of sensitivity and specificity for the diagnosis of rectal adenoma canceration.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Application of Enhanced T1WI of MRI Radiomics in Glioma Grading

OBJECTIVE

To explore the application value of the radiomics method based on enhanced T1WI in glioma grading.

MATERIALS AND METHODS

A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification.

RESULTS

The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905-0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871-1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%.

CONCLUSIONS

The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.

Radiomic Analysis of Contrast-Enhanced MRI Predicts DNA Copy-Number Subtype and Outcome in Lower-Grade Gliomas

RATIONALE AND OBJECTIVES

DNA copy-number (CN)2-subtype impairs outcomes in patients with lower-grade gliomas (LGG). We aimed to determine the value of preoperative nomograms integrating radiomic and radiographic (RR) features in predicting DNA copy-number subtype.

METHODS

Data of 153 consecutive patients were retrospectively analyzed. A total of 1167 radiomics features were extracted from contrast-enhanced MR images. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. Three CN-related RR model were built with multivariate logistic regression.

RESULTS

CN2-subtype was associated with shortest median PFS(p <0.001) and OS (p <0.001). The radiomics nomogram, which incorporated the signature (AUC:0.891, OR: 2.345; p = 0.001), extranodular growth (OR: 14.413; p <0.001) and width (OR: 0.194; p = 0.027), distinguished CN2-subtype with an AUC of 0.924(95%CI: 0.869-0.979).The radiomics nomogram, which incorporated the signature (AUC:0.730, OR: 2.408; p = 0.001), hemorrhage (OR: 0.100; p <0.001), poorly-defined margin (OR:4.433; p = 0.001) and volume>=60cm3 (OR: 4.195; p = 0.002) were associated with CN1-subtype (AUC:0.829,95%CI:0.765-0.892).The radiomics nomogram, which incorporated the signature (AUC:0.660, OR: 2.518; p = 0.003), necrosis/cystic(OR:6.975; p = 0.008), hemorrhage (OR:3.723; p = 0.024), poorly-defined margin (OR:0.124; p <0.001) and frontal lobe tumors (OR: 4.870; p <0.001) were associated with CN3-subtype (AUC: 0.837,95%CI: 0.767-0.909).All three RR models showed good discrimination and calibration. Decision curve analysis indicated that all RR models were clinically useful. The average accuracy of the ten-fold cross validation was 92.8% for CN2-subtype, 72.6% for CN1-subtype and 79.0% for CN3-subtype.

CONCLUSION

The shortest PFS and OS was observed in LGG patients with CN2-subtype. The RR models, integrating radiomic and radiographic features, demonstrates good performance for predicting DNA copy-number subtype and clinical outcomes.