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

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

MR Perfusion Imaging for Gliomas

Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PURPOSE

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

MATERIALS AND METHODS

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34-75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5-T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

RESULTS

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10^-3 mm²/s and 1.21×10^-3 mm²/s cut-off values, yielding 70% sensitivity each (95% CI: 59-82% and 61-80%, respectively), 80% (95% CI: 79-98%) and 90% (95% CI: 82-97%) specificity, and 73% (95% CI: 66-91%) and 76% (95% CI: 72-90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58-83%) and 70% (95% CI: 59-84%), specificities of 90% (95% CI: 80-95%) and 70% (95% CI: 64-83%) and accuracies of 79% (95% CI: 69-89%) and 70% (95% CI: 64-77%), respectively.

CONCLUSION

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

The combined role of MR spectroscopy and perfusion imaging in preoperative differentiation between high- and low-grade gliomas

Background

Brain tumors are an important health problem. The preoperative classification of gliomas by non-invasive techniques is a significant problem. Relative cerebral blood volume and spectroscopy have the ability to sample the entire lesion non-invasively. The present study aims to evaluate the combined role of dynamic susceptibility perfusion and spectroscopy in the classification of primary brain tumors. The combination of both provides overall diagnostic accuracy (100%). Relative cerebral blood volume in peritumoral region plays an important additional role in this regard.

Results

On the basis of histopathology, among 50 patients with brain tumors, high-grade gliomas accounted for 58%, while low-grade gliomas accounted for 42%. The relative cerebral blood volume in the tumor had the best sensitivity, specificity, and accuracy of 96.8%, 95.3%, and 96, respectively. The use of relative cerebral blood volume and choline/N-acetyl Aspartate increased diagnostic accuracy by 100%.

Conclusion

The combination of magnetic resonance spectroscopy and perfusion can increase sensitivity and positive predictive value to define the degree of glioma.

Evaluation of methotrexate-conjugated gadolinium(III) for cancer diagnosis and treatment

Background

Gliomas are one of the most common types of primary brain tumors. It is usually evaluated by gadolinium(III)-based contrast agents by magnetic resonance imaging (MRI) in the clinic. Methotrexate (MTX), as a type of folate analog that inhibits the enzyme dihydrofolate reductase, is widely used as a chemotherapeutic agent to treat gliomas in the experiment.

Purpose

In this study, a novel theranostic agent MTX-DOTA-Gd (MTX-Gd) was synthesized, which integrates magnetic resonance imaging (MRI) with anticancer treatment.

Methods

MTX-Gd was synthesized by connecting MTX and Gd through 1,4,7,10-tetraazacy-clododecane-1,4,7,10-tetraacetic acid (DOTA). The characterization of MTX-Gd was detected by ultraviolet (UV) and infrared spectroscopy (IR). To confirm the antitumor effect of MTX-Gd, the cytotoxicity of MTX-Gd was examined by the MTT assay. The contrast enhancement of the MTX-Gd was measured through MRI in vitro. Then, nude mice bearing C6 tumor xenografts were used to study in vivo imaging capabilities.

Results

The ultraviolet-visible-near infrared radiation (UV-NIR) absorption curve indicated that MTX-Gd had a broad absorption in the region of 500-700 nm. The formation of MTX-Gd was confirmed from the characteristic bands of MTX-DOTA-Gd in the 1413 cm⁻¹ (C-N), 1577 cm⁻¹ (−NH₂), and 3429 cm⁻¹ (N-H), in the fourier-transform infrared (FTIR) spectra. MTX-Gd showed little difference in the cell viability compared with MTX, except for the highest concentration (270 μM). In vitro, the imaging of MTX-Gd was significantly brighter than Gd-DOTA at the same concentration, and the brightness and signal intensity of MRI were increased followed by the increased concentration of MTX-Gd. And it also showed that MTX was not visualized on MRI. The other images revealed that the concentration of 4 mM MTX-Gd had the same imaging effect with the concentration of 10 mM Gd-DOTA. Then, MTX-Gd was injected in nude mice bearing C6 tumor xenografts through the tail vein. Significant contrast enhancement was observed at the tumor site from 0.5 h to 3 h. The signal of tumor area was strongest at 3 h due to accumulation by size effect of macromolecules.

Conclusion

A novel stable and unique theranostic agent (MTX-Gd) was successfully synthe-sized, and it has good stability, strong anticancer ability and excellent magnetic capacity. The methotrexate component of MTX-Gd, as a chemotherapeutic agent, played an important role in targeted therapies of cancer. The DOTA-Gd component of MTX-Gd performed as the MRI contrast agent. The superior MRI imaging performance and synergetic chemical antineoplastic ability of MTX-Gd was revealed, and it has great potential in the diagnosis and treatment of glioma and potentially other cancers, with prospects of clinical application in the near future.