Radiological characteristics of glioblastoma multiforme using CT and MRI examination

Comprehensive insights into glioblastoma multiforme: drug delivery challenges and multimodal treatment strategies

Glioblastoma multiforme (GBM) is one of the most common and malignant brain tumors, with a high prevalence in elderly population. Most chemotherapeutic agents fail to reach the tumor site due to various challenges. However, smart nanocarriers have demonstrated excellent drug-loading capabilities, enabling them to cross the blood brain tumor barrier for the GBM treatment. Surface modification of nanocarriers has significantly enhanced their potential for targeting therapeutics. Moreover, recent innovations in drug therapies, such as the incorporation of theranostic agents in nanocarriers and antibody-drug conjugates, have offered newer insights for both diagnosis and treatment. This review focuses on recent advances in new therapeutic interventions for GBM, with an emphasis on the nanotheranostics systems to maximize therapeutic and diagnostic outcomes.

A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation

The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers.

MATERIALS AND METHODS

The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review.

RESULTS

A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video.

CONCLUSIONS

Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.

A Case Report on a Common Tumour With an Uncommon Presentation: Glioblastoma

The most common type of primary brain tumour in adults is gliomas although rare. Glioblastomas are a subtype of gliomas with the worst prognosis having the ability to rapidly increase in size, even doubling within days to weeks. Patients can present with varied presentations depending on the site of the involvement, thus misleading in diagnosis due to vagueness. The most common clinical presentations include headaches, seizures, and focal neurological signs. However, there can be atypical presentations like personality changes and back pain due to meningeal irritation which may be the only presenting complaint in the early stages. Magnetic resonance imaging (MRI) is usually considered the only investigation required for the diagnosis of the illness. However, it can mislead in the early stages. Therefore, brain biopsy remains the gold standard in the diagnosis of glioblastoma multiforme. It is important to identify the subtype to decide on the prognosis and plan the management thereafter. Here, we present a 49-year-old woman with prominent personality changes, depressive symptoms, and atypical brain imaging findings. The definitive diagnosis was made with the brain biopsy as two MRI findings were contradictory. This article highlights the importance of suspicion of primary brain tumours in adults presenting with atypical neuropsychiatric manifestations.

Retrospective Analysis of Glioblastoma Outcomes

This retrospective mono-center study focuses on 144 cases of glioblastoma treated over a time span of 12 years in our clinic in Romania. We offer critical insight into the dreadful aspect of this tumor by highlighting the principal characteristics such as localization, the genetic information of each case, progression-free survival (PFS), and overall survival (OS). A tenth of our patients underwent a second surgical procedure, providing a comparable OS to the other part of our study group, proving that surgical treatment as salvage therapy is a viable option. Also, our research reinforces the fact that utilizing the Karnofsky Performance Scale is a great predictor of patient outcomes in glioblastoma patients. Even though radiotherapy and chemotherapy have mild effects in the context of this oncological disease, our research shows that O6-methylguanine-DNA methyltransferase (MGMT) methylation status and epidermal growth factor receptor (EGFR) amplification have an important effect on OS. Moreover, the particularity of our study, that our patients did not start adjuvant therapy right after surgery, highlighted by a low OS compared to the international literature, sheds light on the fact that chemotherapy and radiotherapy must be started right after the surgical procedure, according to the Stupp protocol. To sum up, our research takes into consideration the factors that influence patient survival and outcome in the battle against glioblastoma.

MRI findings of primary intracranial sarcomas in children

BACKGROUND

Primary intracranial sarcoma is a very rare high-grade tumor. Scant reports exist on this malignancy in children, which limit the information about its imaging characteristics.

OBJECTIVE

We aimed to describe the main characteristics of primary intracranial sarcoma on MRI.

MATERIALS AND METHODS

In this cross-sectional descriptive observational study, we reviewed 18 patients (aged 1-18 years) with primary intracranial sarcomas diagnosed between 2015 and 2021.

RESULTS

The main findings were contrast enhancement (100%), diffusion restriction (78%), hemorrhage (89%), meningeal extension (67%), necrosis (67%), and supratentorial location (72%).

CONCLUSION

Primary intracranial sarcoma are typically supratentorial in location. MRI findings of primary intracranial sarcoma are similar to other intracranial malignancies.

Non-enhancing Glioblastoma: A Case Report

Glioblastoma multiforme (GBM) is a malignant adult brain tumor that is visualized as an enhancing lesion on post-contrast magnetic resonance imaging (MRI). In this study, we present a rare case of non-enhancing GBM that required histopathological examination for a definitive diagnosis. Our findings emphasize the critical role of biopsy in the diagnosis and treatment of GBMs, particularly in cases of non-enhancing lesions on MRI.

Influence of MMR, MGMT Promotor Methylation and Protein Expression on Overall and Progression-Free Survival in Primary Glioblastoma Patients Treated with Temozolomide

Glioblastoma is the most common malignant brain tumor in adults. Standard treatment includes tumor resection, radio-chemotherapy and adjuvant chemotherapy with temozolomide (TMZ). TMZ methylates DNA, whereas O6-methylguanine DNA methyltransferase (MGMT) counteracts TMZ effects by removing the intended proteasomal degradation signal. Non-functional MGMT mediates the mismatch repair (MMR) system, leading to apoptosis after futile repair attempts. This study investigated the associations between MGMT promoter methylation, MGMT and MMR protein expression, and their effect on overall survival (OS) and progression-free survival (PFS) in patients with glioblastoma. MGMT promoter methylation was assessed in 42 treatment-naïve patients with glioblastoma WHO grade IV by pyrosequencing. MGMT and MMR protein expression was analyzed using immunohistochemistry. MGMT promoter methylation was present in 52%, whereas patients <70 years of age revealed a significantly longer OS using a log-rank test and a significance threshold of p ≤ 0.05. MGMT protein expression and methylation status showed no correlation. MMR protein expression was present in all patients independent of MGMT status and did not influence OS and PFS. Overall, MGMT promoter methylation implicates an improved OS in patients with glioblastoma aged <70 years. In the elderly, the extent of surgery has an impact on OS rather than the MGMT promoter methylation or protein expression.

Integrated MRI–Immune–Genomic Features Enclose a Risk Stratification Model in Patients Affected by Glioblastoma

Simple Summary

Despite crucial scientific advances, Glioblastoma (GB) remains a fatal disease with limited therapeutic options and a lack of suitable biomarkers. The unveiled competence of the brain immune system together with the breakthrough advent of immunotherapy has shifted the present translational research on GB towards an immune-focused perspective. Several clinical trials targeting the immunosuppressive GB background are ongoing. So far, results are inconclusive, underpinning our partial understanding of the complex cancer-immune interplay in brain tumors. High throughput Magnetic Resonance (MR) imaging has shown the potential to decipher GB heterogeneity, including pathologic and genomic clues. However, whether distinct GB immune contextures can be deciphered at an imaging scale is still elusive, leaving unattained the non-invasive achievement of prognostic and predictive biomarkers. Along these lines, we integrated genetic, immunopathologic and imaging features in a series of GB patients. Our results suggest that multiparametric approaches might offer new efficient risk stratification models, opening the possibility to intercept the critical events implicated in the dismal prognosis of GB.

Abstract

Background: The aim of the present study was to dissect the clinical outcome of GB patients through the integration of molecular, immunophenotypic and MR imaging features. Methods: We enrolled 57 histologically proven and molecularly tested GB patients (5.3% IDH-1 mutant). Two-Dimensional Free ROI on the Biggest Enhancing Tumoral Diameter (TDFRBETD) acquired by MRI sequences were used to perform a manual evaluation of multiple quantitative variables, among which we selected: SD Fluid Attenuated Inversion Recovery (FLAIR), SD and mean Apparent Diffusion Coefficient (ADC). Characterization of the Tumor Immune Microenvironment (TIME) involved the immunohistochemical analysis of PD-L1, and number and distribution of CD3+, CD4+, CD8+ Tumor Infiltrating Lymphocytes (TILs) and CD163+ Tumor Associated Macrophages (TAMs), focusing on immune-vascular localization. Genetic, MR imaging and TIME descriptors were correlated with overall survival (OS). Results: MGMT methylation was associated with a significantly prolonged OS (median OS = 20 months), while no impact of p53 and EGFR status was apparent. GB cases with high mean ADC at MRI, indicative of low cellularity and soft consistency, exhibited increased OS (median OS = 24 months). PD-L1 and the overall number of TILs and CD163+TAMs had a marginal impact on patient outcome. Conversely, the density of vascular-associated (V) CD4+ lymphocytes emerged as the most significant prognostic factor (median OS = 23 months in V-CD4^high vs. 13 months in V-CD4^low, p = 0.015). High V-CD4+TILs also characterized TIME of MGMT^meth GB, while p53^mut appeared to condition a desert immune background. When individual genetic (MGMT^unmeth), MR imaging (mean ADC^low) and TIME (V-CD4+TILs^low) negative predictors were combined, median OS was 21 months (95% CI, 0–47.37) in patients displaying 0–1 risk factor and 13 months (95% CI 7.22–19.22) in the presence of 2–3 risk factors (p = 0.010, HR = 3.39, 95% CI 1.26–9.09). Conclusion: Interlacing MRI–immune–genetic features may provide highly significant risk-stratification models in GB patients.

Reclassification of Glioblastoma Multiforme According to the 2021 World Health Organization Classification of Central Nervous System Tumors: A Single Institution Report and Practical Significance

Introduction The 2021 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) has introduced significant changes to tumor taxonomy. One of the most significant changes in the isolation of isocitrate dehydrogenase (IDH) mutant forms of glioblastoma multiforme (GBM) into separate entities, as well as no longer allowing for entries to be classified as not otherwise specified (NOS). As a result, this entity now includes only the most aggressive adult-type tumors. As such, established prognostic factors no longer apply, as they now form the criteria of different disease entries or have been established based on a mixed cohort. Herein, we aimed to reclassify glioblastoma cases diagnosed per the 2016 WHO tumors of the CNS classification into the 2021 WHO tumors of the CNS classification and establish a patient survival pattern based on age, gender, tumor location, and size as well as tumor O-6-methylguanine-DNA methyltransferase (MGMT) mutation. Materials and methods A retrospective, non-clinical approach was utilized. Biopsy specimens of adults diagnosed with GBM, WHO grade 4, NOS in the period February 2018-February 2021 were reevaluated. The data regarding the patient's gender and age were withdrawn from the medical documentation. Immunohistochemistry was performed with mouse monoclonal anti-IDH R132H and rabbit polyclonal anti-MGMT. Radiology data on tumor location and size were pulled from the radiology repository. Data were statistically analyzed for significance, using Kaplan-Meier survival analysis, with a 95% confidence interval and p<0.05 defined as significant. Results A total of 58 cases fit the set criteria, with eight of them (13.7%) harboring an IDH R132H mutation and were hence reclassified as diffuse astrocytoma IDH-mutant, WHO CNS grade 4. The cases that retained their GBM classification included n=28 males and n=22 females, a male to female ratio of 1.27:1, and a mean age of 65.3 years (range 43-86 years). The MGMT mutational status revealed a total of n=17 positive cases (35%), while the remaining cases were negative. No hemispheric predilection could be established. Lobar predilection was as follows: temporal (37.78%), parietal (28.89%), frontal (24.44%), and occipital (8.89%). The mean tumor size measured on neuroradiology across the cohort was 50.51 mm (range 20-76 mm). The median survival across cases was 255.96 days (8.41 months), with a range of 18-1150 days (0.59-37.78 months). No statistical correlation could be established between patient survival and gender, hemispheric location, lobar location, and tumor size. A significant difference in survival was established only when comparing the 41-50 age groups to the 71-80 and 81-90 age groups and MGMT positive versus negative tumors (p=0.0001). Conclusion From a practical standpoint, the changes implemented in the new classification of CNS tumors define GBM as the most aggressive adult type of tumor. Based on their significantly more favorable prognosis, the reclassification of IDH mutant forms of astrocytomas has had little epidemiological impact on this relatively common malignancy but has significantly underlined the dismal prognosis. The changes have also led to MGMT promoter methylation status being the only significant prognostic factor for patient survival in clinical use, based on its prediction for response to temozolomide therapy in this nosological unit clinically presenting when it has already reached immense size.

Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.

The Additional Value of 18F-FDG PET and MRI in Patients with Glioma: A Review of the Literature from 2015 to 2020

AIM

Beyond brain computed tomography (CT) scan, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) hold paramount importance in neuro-oncology. The aim of this narrative review is to discuss the literature from 2015 to 2020, showing advantages or complementary information of fluorine-18 fluorodeoxyglucose (18F-FDG) PET imaging to the anatomical and functional data offered by MRI in patients with glioma.

METHODS

A comprehensive Pubmed/MEDLINE literature search was performed to retrieve original studies, with a minimum of 10 glioma patients, published from 2015 until the end of April 2020, on the use of 18F-FDG PET in conjunction with MRI.

RESULTS

Twenty-two articles were selected. Combined use of the two modalities improves the accuracy in predicting prognosis, planning treatments, and evaluating recurrence.

CONCLUSION

According to the recent literature, 18F-FDG PET provides different and complementary information to MRI and may enhance performance in the whole management of gliomas. Therefore, integrated PET/MRI may be particularly useful in gliomas, since it could provide accurate morphological and metabolic information in one-shoot examination and improve the diagnostic value compared to each of procedures.