Relationship between necrotic patterns in glioblastoma and patient survival: fractal dimension and lacunarity analyses using magnetic resonance imaging

Prediction of Survival Outcomes in Patients with Glioma Using Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis

BACKGROUND

Glioma is the most common malignancy in the central nervous system. Even with optimal therapies, glioblastoma (the most aggressive form of glioma) is incurable, with only 26.5% of patients having a 2-year survival rate. The present meta-analysis evaluated the association of magnetic resonance imaging (MRI)-derived parameters in glioma patients with progression-free survival (PFS) and overall survival. Eligible clinical articles on glioma patients included those that contained an evaluation of the association between MRI findings, PFS, and overall length of survival.

METHODS

Review of the literature included the following databases: WHO International Clinical Trials Registry Platform; Google Scholar; Web of Science; PubMed; SIGLE; NYAM; Scopus; Randomized controlled trial (RCT); Virtual Health Library (VHL); Cochrane Collaboration; EMBASE; and Clinical Trials.

RESULTS

The current review included 20 studies, and covered 2097 patients with gliomas. There were 1310 patients with glioblastoma and 320 with astrocytoma. There were 161 patients with grade-2 gliomas and 111 patients with grade-3. Tumour necrosis, peritumoural oedema, and multiple lesions were associated with PFS, as well as tumour necrosis and peritumoural oedema with overall survival.

CONCLUSIONS

The present meta-analysis highlighted the ability of MRI to predict PFS and overall survival in patients with gliomas. This is crucial to identify patients at risk for poor survival outcomes and for individualising the treatment plan for such patients. The PROSPERO Registration: CRD42023489535, https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=489535.

The molecular signature and prognosis of glioma with preoperative intratumoral hemorrhage: a retrospective cohort analysis

BACKGROUND

Intratumoral hemorrhage, though less common, could be the first clinical manifestation of glioma and is detectable via MRI; however, its exact impacts on patient outcomes remain unclear and controversial. The 2021 WHO CNS 5 classification emphasised genetic and molecular features, initiating the necessity to establish the correlation between hemorrhage and molecular alterations. This study aims to determine the prevalence of intratumoral hemorrhage in glioma subtypes and identify associated molecular and clinical characteristics to improve patient management.

METHODS

Integrated clinical data and imaging studies of patients who underwent surgery at the Department of Neurosurgery at Peking Union Medical College Hospital from January 2011 to January 2022 with pathological confirmation of glioma were retrospectively reviewed. Patients were divided into hemorrhage and non-hemorrhage groups based on preoperative magnetic resonance imaging. A comparison and survival analysis were conducted with the two groups. In terms of subgroup analysis, we classified patients into astrocytoma, IDH-mutant; oligodendroglioma, IDH-mutant, 1p/19q-codeleted; glioblastoma, IDH-wildtype; pediatric-type gliomas; or circumscribed glioma using integrated histological and molecular characteristics, according to WHO CNS 5 classifications.

RESULTS

457 patients were enrolled in the analysis, including 67 (14.7%) patients with intratumoral hemorrhage. The hemorrhage group was significantly older and had worse preoperative Karnofsky performance scores. The hemorrhage group had a higher occurrence of neurological impairment and a higher Ki-67 index. Molecular analysis indicated that CDKN2B, KMT5B, and PIK3CA alteration occurred more in the hemorrhage group (CDKN2B, 84.4% vs. 62.2%, p = 0.029; KMT5B, 25.0% vs. 8.9%, p = 0.029; and PIK3CA, 81.3% vs. 58.5%, p = 0.029). Survival analysis showed significantly worse prognoses for the hemorrhage group (hemorrhage 18.4 months vs. non-hemorrhage 39.1 months, p = 0.01). In subgroup analysis, the multivariate analysis showed that intra-tumoral hemorrhage is an independent risk factor only in glioblastoma, IDH-wildtype (162 cases of 457 overall, HR = 1.72, p = 0.026), but not in other types of gliomas. The molecular alteration of CDK6 (hemorrhage group p = 0.004, non-hemorrhage group p < 0.001), EGFR (hemorrhage group p = 0.003, non-hemorrhage group p = 0.001), and FGFR2 (hemorrhage group p = 0.007, non-hemorrhage group p = 0.001) was associated with shorter overall survival time in both hemorrhage and non-hemorrhage groups.

CONCLUSIONS

Glioma patients with preoperative intratumoral hemorrhage had unfavorable prognoses compared to their nonhemorrhage counterparts. CDKN2B, KMT5B, and PIK3CA alterations were associated with an increased occurrence of intratumoral hemorrhage, which might be future targets for further investigation of intratumoral hemorrhage.

The FKBP51s Splice Isoform Predicts Unfavorable Prognosis in Patients with Glioblastoma

The primary treatment for glioblastoma (GBM) is removing the tumor mass as defined by MRI. However, MRI has limited diagnostic and predictive value. Tumor-associated macrophages (TAM) are abundant in GBM tumor microenvironment (TME) and are found in peripheral blood (PB). FKBP51 expression, with its canonical and spliced isoforms, is constitutive in immune cells and aberrant in GBM. Spliced FKBP51s supports M2 polarization. To find an immunologic signature that combined with MRI could advance in diagnosis, we immunophenotyped the macrophages of TME and PB from 37 patients with GBM using FKBP51s and classical M1-M2 markers. We also determined the tumor levels of FKBP51s, PD-L1, and HLA-DR. Tumors expressing FKBP51s showed an increase in various M2 phenotypes and regulatory T cells in PB, indicating immunosuppression. Tumors expressing FKBP51s also activated STAT3 and were associated with reduced survival. Correlative studies with MRI and tumor/macrophages cocultures allowed to interpret TAMs. Tumor volume correlated with M1 infiltration of TME. Cocultures with spheroids produced M1 polarization, suggesting that M1 macrophages may infiltrate alongside cancer stem cells. Cocultures of adherent cells developed the M2 phenotype CD163/FKBP51s expressing pSTAT6, a transcription factor enabling migration and invasion. In patients with recurrences, increased counts of CD163/FKBP51s monocyte/macrophages in PB correlated with callosal infiltration and were accompanied by a concomitant decrease in TME-infiltrating M1 macrophages. PB PD-L1/FKBP51s connoted necrotic tumors. In conclusion, FKBP51s identifies a GBM subtype that significantly impairs the immune system. Moreover, FKBP51s marks PB macrophages associated with MRI features of glioma malignancy that can aid in patient monitoring.

SIGNIFICANCE

Our research suggests that by combining imaging with analysis of monocyte/macrophage subsets in patients with GBM, we can enhance our understanding of the disease and assist in its treatment. We discovered a similarity in the macrophage composition between the TME and PB, and through association with imaging, we could interpret macrophages. In addition, we identified a predictive biomarker that drew more attention to immune suppression of patients with GBM.

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience

BACKGROUND

The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI).

PATIENTS AND METHODS

We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded.

RESULTS

There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively).

CONCLUSIONS

Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

Modeling glioblastoma complexity with organoids for personalized treatments

Glioblastoma (GBM) remains a fatal diagnosis despite the current standard of care of maximal surgical resection, radiation, and temozolomide (TMZ) therapy. One aspect that impedes drug development is the lack of an appropriate model representative of the complexity of patient tumors. Brain organoids derived from cell culture techniques provide a robust, easily manipulatable, and high-throughput model for GBM. In this review, we highlight recent progress in developing GBM organoids (GBOs) with a focus on generating the GBM microenvironment (i.e., stem cells, vasculature, and immune cells) recapitulating human disease. Finally, we also discuss the use of organoids as a screening tool in drug development for GBM.

Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity

PURPOSE

Neuroblastoma is a pediatric solid tumor with a prognosis associated with histology and age of the patient, which are the parameters of the well-established current classification (Shimada classification). Despite the development of new treatment options, the prognosis of high-risk neuroblastoma patients is still poor. Therefore, there is a continuous need to stratify the children suffering from this tumor. A mathematical and computational approach is proposed to enable automatic and precise cancer diagnosis on the histological slide.

METHODS

We targeted the complexity of neuroblastoma by calculating its image entropy (S), fractal dimension (FD), and lacunarity (λ) in a combined mathematical code. First, we tested the proposed method for patient-derived glioma images. It allowed distinguishing between normal brain tissue, grade II, and grade III glioma, which harbor different outcomes.

RESULTS

In neuroblastoma, our analysis of image's FD, S, and λ combined with a machine learning algorithm automatically predicted tumor malignancy with a receiver operating characteristic curve of 0.82. FD, S, and λ distinguish between neuroblastoma and ganglioneuroma, but they only partially differentiate between the normal samples and the other classes. Ganglioneuroma, the most differentiated form, and poorly-differentiated neuroblastoma display different values of FD, S, and λ.

CONCLUSIONS

FD, S, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children's hospitals worldwide.

The Fractal Viewpoint of Tumors and Nanoparticles

Even though the promising therapies against cancer are rapidly improved, the oncology patients population has seen exponential growth, placing cancer in 5th place among the ten deadliest diseases. Efficient drug delivery systems must overcome multiple barriers and maximize drug delivery to the target tumors, simultaneously limiting side effects. Since the first observation of the quantum tunneling phenomenon, many multidisciplinary studies have offered quantum-inspired solutions to optimized tumor mapping and efficient nanodrug design. The property of a wave function to propagate through a potential barrier offer the capability of obtaining 3D surface profiles using imaging of individual atoms on the surface of a material. The application of quantum tunneling on a scanning tunneling microscope offers an exact surface roughness mapping of tumors and pharmaceutical particles. Critical elements to cancer nanotherapeutics apply the fractal theory and calculate the fractal dimension for efficient tumor surface imaging at the atomic level. This review study presents the latest biological approaches to cancer management based on fractal geometry.

Implications of Cellular Immaturity in Necrosis and Microvascularization in Glioblastomas IDH-Wild-Type

Necrosis and increased microvascular density in glioblastoma IDH-wild-type are the consequence of both hypoxia and cellular immaturity. Our study aimed to identify the main clinical-imaging and morphogenetic risk factors associated with tumor necrosis and microvascular in the prognosis of patient survival. We performed a retrospective study (10 years) in which we identified 39 cases. We used IDH1, Ki-67 and Nestin immunomarkers, as well as CDKN2A by FISH. The data were analyzed using SPSS Statistics. The clinical characterization identified only age over 50 years as a risk factor (HR = 3.127). The presence of the tumor residue, as well as the absence of any therapeutic element from the trimodal treatment, were predictive factors of mortality (HR = 1.024, respectively HR = 7.460). Cellular immaturity quantified by Nestin was associated with reduced overall survival (p = 0.007). Increased microvascular density was associated with an increased proliferative index (p = 0.009) as well as alterations of the CDKN2A gene (p < 0.001). CDKN2A deletions and cellular immaturity were associated with an increased percentage of necrosis (p < 0.001, respectively, p = 0.017). The main risk factors involved in the unfavorable prognosis are moderate and increased Nestin immunointensity, as well as the association of increased microvascular density with age over 50 years. Necrosis was not a risk factor.

Collagen Matrices Mediate Glioma Cell Migration Induced by an Electrical Signal

Glioma cells produce an increased amount of collagen compared with normal astrocytes. The increasing amount of collagen in the extracellular matrix (ECM) modulates the matrix structure and the mechanical properties of the microenvironment, thereby regulating tumor cell invasion. Although the regulation of tumor cell invasion mainly relies on cell–ECM interaction, the electrotaxis of tumor cells has attracted great research interest. The growth of glioma cells in a three-dimensional (3D) collagen hydrogel creates a relevant tumor physiological condition for the study of tumor cell invasion. In this study, we tested the migration of human glioma cells, fetal astrocytes, and adult astrocytes in a 3D collagen matrix with different collagen concentrations. We report that all three types of cells demonstrated higher motility in a low concentration of collagen hydrogel (3 mg/mL and 5 mg/mL) than in a high concentration of collagen hydrogel (10 mg/mL). We further show that human glioma cells grown in collagen hydrogels responded to direct current electric field (dcEF) stimulation and migrated to the anodal pole. The tumor cells altered their morphology in the gels to adapt to the anodal migration. The directedness of anodal migration shows a field strength-dependent response. EF stimulation increased the migration speed of tumor cells. This study implicates the potential role of an dcEF in glioma invasion and as a target of treatment.

Antibody-Based Formats to Target Glioblastoma: Overcoming Barriers to Protein Drug Delivery

Glioblastoma (GB) is recognized as the most aggressive form of primary brain cancer. Despite advances in treatment strategies that include surgery, radiation, and chemotherapy, the median survival time (∼15 months) of patients with GB has not significantly improved. The poor prognosis of GB is also associated with a very high chance of tumor recurrence (∼90%), and current treatment measures have failed to address the complications associated with this disease. However, targeted therapies enabled through antibody engineering have shown promise in countering GB when used in combination with conventional approaches. Here, we discuss the challenges in conventional as well as future GB therapeutics and highlight some of the known advantages of using targeted biologics to overcome these impediments. We also review a broad range of potential alternative routes that could be used clinically to administer anti-GB biologics to the brain through evasion of its natural barriers.

RNA Editing in Glioma as a Sexually Dimorphic Prognostic Factor That Affects mRNA Abundance in Fatty Acid Metabolism and Inflammation Pathways

RNA editing alters the nucleotide sequence and has been associated with cancer progression. However, little is known about its prognostic and regulatory roles in glioma, one of the most common types of primary brain tumors. We characterized and analyzed RNA editomes of glioblastoma and isocitrate dehydrogenase mutated (IDH-MUT) gliomas from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas (CGGA). We showed that editing change during glioma progression was another layer of molecular alterations and that editing profiles predicted the prognosis of glioblastoma and IDH-MUT gliomas in a sex-dependent manner. Hyper-editing was associated with poor survival in females but better survival in males. Moreover, noncoding editing events impacted mRNA abundance of the host genes. Genes associated with inflammatory response (e.g., EIF2AK2, a key mediator of innate immunity) and fatty acid oxidation (e.g., acyl-CoA oxidase 1, the rate-limiting enzyme in fatty acid β-oxidation) were editing-regulated and associated with glioma progression. The above findings were further validated in CGGA samples. Establishment of the prognostic and regulatory roles of RNA editing in glioma holds promise for developing editing-based therapeutic strategies against glioma progression. Furthermore, sexual dimorphism at the epitranscriptional level highlights the importance of developing sex-specific treatments for glioma.

A Preliminary Study of the Effect of Quercetin on Cytotoxicity, Apoptosis, and Stress Responses in Glioblastoma Cell Lines

A growing body of evidence indicates that dietary polyphenols show protective effects against various cancers. However, little is known yet about their activity in brain tumors. Here we investigated the interaction of dietary flavonoid quercetin (QCT) with the human glioblastoma A172 and LBC3 cell lines. We demonstrated that QCT evoked cytotoxic effect in both tested cell lines. Microscopic observations, Annexin V-FITC/PI staining, and elevated expression and activity of caspase 3/7 showed that QCT caused predominantly apoptotic death of A172 cells. Further analyses confirmed enhanced ROS generation, deregulated expression of SOD1 and SOD2, depletion of ATP levels, and an overexpression of CHOP, suggesting the activation of oxidative stress and ER stress upon QCT exposure. Finally, elevated expression and activity of caspase 9, indicative of a mitochondrial pathway of apoptosis, was detected. Conversely, in LBC3 cells the pro-apoptotic effect was observed only after 24 h incubation with QCT, and a shift towards necrotic cell death was observed after 48 h of treatment. Altogether, our data indicate that exposure to QCT evoked cell death via activation of intrinsic pathway of apoptosis in A172 cells. These findings suggest that QCT is worth further investigation as a potential pharmacological agent in therapy of brain tumors.

Altered Elemental Distribution in Male Rat Brain Tissue as a Predictor of Glioblastoma Multiforme Growth-Studies Using SR-XRF Microscopy

Glioblastoma multiforme (GBM) is a particularly malignant primary brain tumor. Despite enormous advances in the surgical treatment of cancer, radio- and chemotherapy, the average survival of patients suffering from this cancer does not usually exceed several months. For obvious ethical reasons, the search and testing of the new drugs and therapies of GBM cannot be carried out on humans, and for this purpose, animal models of the disease are most often used. However, to assess the efficacy and safety of the therapy basing on these models, a deep knowledge of the pathological changes associated with tumor development in the animal brain is necessary. Therefore, as part of our study, the synchrotron radiation-based X-ray fluorescence microscopy was applied for multi-elemental micro-imaging of the rat brain in which glioblastoma develops. Elemental changes occurring in animals after the implantation of two human glioma cell lines as well as the cells taken directly from a patient suffering from GBM were compared. Both the extent and intensity of elemental changes strongly correlated with the regions of glioma growth. The obtained results showed that the observation of elemental anomalies accompanying tumor development within an animal's brain might facilitate our understanding of the pathogenesis and progress of GBM and also determine potential biomarkers of its extension. The tumors appearing in a rat's brain were characterized by an increased accumulation of Fe and Se, whilst the tissue directly surrounding the tumor presented a higher accumulation of Cu. Furthermore, the results of the study allow us to consider Se as a potential elemental marker of GBM progression.

Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively. Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design. Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues. Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice. Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Postmortem brain donations vs premortem surgical resections for glioblastoma research: viewing the matter as a whole

There have been limited improvements in diagnosis, treatment, and outcomes of primary brain cancers, including glioblastoma, over the past 10 years. This is largely attributable to persistent deficits in understanding brain tumor biology and pathogenesis due to a lack of high-quality biological research specimens. Traditional, premortem, surgical biopsy samples do not allow full characterization of the spatial and temporal heterogeneity of glioblastoma, nor capture end-stage disease to allow full evaluation of the evolutionary and mutational processes that lead to treatment resistance and recurrence. Furthermore, the necessity of ensuring sufficient viable tissue is available for histopathological diagnosis, while minimizing surgically induced functional deficit, leaves minimal tissue for research purposes and results in formalin fixation of most surgical specimens. Postmortem brain donation programs are rapidly gaining support due to their unique ability to address the limitations associated with surgical tissue sampling. Collecting, processing, and preserving tissue samples intended solely for research provides both a spatial and temporal view of tumor heterogeneity as well as the opportunity to fully characterize end-stage disease from histological and molecular standpoints. This review explores the limitations of traditional sample collection and the opportunities afforded by postmortem brain donations for future neurobiological cancer research.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Effects of Multi-Shell Free Water Correction on Glioma Characterization

Diffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.

Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI

PURPOSE

Construction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.

MATERIALS AND METHODS

A total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.

RESULTS

The constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.

CONCLUSION

Our results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?

Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.

DNA damage repair in glioblastoma: current perspectives on its role in tumour progression, treatment resistance and PIKKing potential therapeutic targets

BACKGROUND

The aggressive, invasive and treatment resistant nature of glioblastoma makes it one of the most lethal cancers in humans. Total surgical resection is difficult, and a combination of radiation and chemotherapy is used to treat the remaining invasive cells beyond the tumour border by inducing DNA damage and activating cell death pathways in glioblastoma cells. Unfortunately, recurrence is common and a major hurdle in treatment, often met with a more aggressive and treatment resistant tumour. A mechanism of resistance is the response of DNA repair pathways upon treatment-induced DNA damage, which enact cell-cycle arrest and repair of DNA damage that would otherwise cause cell death in tumour cells.

CONCLUSIONS

In this review, we discuss the significance of DNA repair mechanisms in tumour formation, aggression and treatment resistance. We identify an underlying trend in the literature, wherein alterations in DNA repair pathways facilitate glioma progression, while established high-grade gliomas benefit from constitutively active DNA repair pathways in the repair of treatment-induced DNA damage. We also consider the clinical feasibility of inhibiting DNA repair in glioblastoma and current strategies of using DNA repair inhibitors as agents in combination with chemotherapy, radiation or immunotherapy. Finally, the importance of blood-brain barrier penetrance when designing novel small-molecule inhibitors is discussed.

Extracellular Matrix Proteome Remodeling in Human Glioblastoma and Medulloblastoma

Medulloblastomas (MBs) and glioblastomas (GBMs) are high-incidence central nervous system tumors. Different origin sites and changes in the tissue microenvironment have been associated with the onset and progression. Here, we describe differences between the extracellular matrix (ECM) signatures of these tumors. We compared the proteomic profiles of MB and GBM decellularized tumor samples between each other and their normal decellularized brain site counterparts. Our analysis revealed that 19, 28, and 11 ECM proteins were differentially expressed in MBs, GBMs, and in both MBs and GBMs, respectively. Next, we validated key findings by using a protein tissue array with 53 MB and 55 GBM cases and evaluated the clinical relevance of the identified differentially expressed proteins through their analysis on publicly available datasets, 763 MB samples from the GSE50161 and GSE85217 studies, and 115 GBM samples from RNAseq-TCGA. We report a shift toward a denser fibrillary ECM as well as a clear alteration in the glycoprotein signature, which influences the tumor pathophysiology. MS data have been submitted to the PRIDE repository, project accession: PXD023350.

An insight into electrical resistivity of white matter and brain tumors

BACKGROUND

There is a lack of information regarding electrical properties of white matter and brain tumors.

OBJECTIVE

To investigate the feasibility of in-vivo measurement of electrical resistivity during brain surgery and establish a better understanding of the resistivity patterns of brain tumors in correlation to the white matter.

METHODS

A bipolar probe was used to measure electrical resistivity during surgery in a prospective cohort of patients with brain tumors. For impedance measurement, the probe applied a constant current of 0.7 μA with a frequency of 140 Hz. The measurement was performed in the white matter within and outside peritumoral edema as well as in non-enhancing, enhancing and necrotic tumor areas. Resistivity values expressed in ohmmeter (Ω∗m) were compared between different intracranial tissues and brain tumors.

RESULTS

Ninety-two patients (gliomas WHO II:16, WHO III:10, WHO IV:33, metastasis:33) were included. White matter outside peritumoral edema had higher resistivity values (13.3 ± 1.7 Ω∗m) than within peritumoral edema (8.5 ± 1.6 Ω∗m), and both had higher values than brain tumors including non-enhancing (WHO II:6.4 ± 1.3 Ω∗m, WHO III:6.3 ± 0.9 Ω∗m), enhancing (WHO IV:5 ± 1 Ω∗m, metastasis:5.4 ± 1.3 Ω∗m) and necrotic tumor areas (WHO IV:3.9 ± 1.1 Ω∗m, metastasis:4.3 ± 1.3 Ω∗m), p=<0.001. No difference was found between low-grade and anaplastic gliomas, p = 0.808, while resistivity values in both were higher than the highest values found in glioblastomas, p = 0.003 and p = 0.004, respectively.

CONCLUSIONS

The technique we applied enabled us to measure electrical resistivity of white matter and brain tumors in-vivo presumably with a significant effect with regard to dielectric polarization. Our results suggest that there are significant differences within different areas and subtypes of brain tumors and that white matter exhibits higher electrical resistivity than brain tumors.

Endurance of extremely prolonged nutrient prevention across kingdoms of life

Numerous observations demonstrate that microorganisms can survive very long periods of nutrient deprivation and starvation. Moreover, it is evident that prolonged periods of starvation are a feature of many habitats, and many cells in all kingdoms of life are found in prolonged starvation conditions. Bacteria exhibit a range of responses to long-term starvation. These include genetic adaptations such as the long-term stationary phase and the growth advantage in stationary phase phenotypes characterized by mutations in stress-signaling genes and elevated mutation rates. Here, we suggest using the term "endurance of prolonged nutrient prevention" (EPNP phase), to describe this phase, which was also recently described in eukaryotes. Here, we review this literature and describe the current knowledge about the adaptations to very long-term starvation conditions in bacteria and eukaryotes, its conceptual and structural conservation across all kingdoms of life, and point out possible directions that merit further research.

Enhanced Anti-Tumor Activity in Mice with Temozolomide-Resistant Human Glioblastoma Cell Line-Derived Xenograft Using SN-38-Incorporated Polymeric Microparticle

Glioblastoma multiforme (GBM) has remained one of the most lethal and challenging cancers to treat. Previous studies have shown encouraging results when irinotecan was used in combination with temozolomide (TMZ) for treating GBM. However, irinotecan has a narrow therapeutic index: a slight dose increase in irinotecan can induce toxicities that outweigh its therapeutic benefits. SN-38 is the active metabolite of irinotecan that accounts for both its anti-tumor efficacy and toxicity. In our previous paper, we showed that SN-38 embedded into 50:50 biodegradable poly[(d,l)-lactide-co-glycolide] (PLGA) microparticles (SMPs) provides an efficient delivery and sustained release of SN-38 from SMPs in the brain tissues of rats. These properties of SMPs give them potential for therapeutic application due to their high efficacy and low toxicity. In this study, we tested the anti-tumor activity of SMP-based interstitial chemotherapy combined with TMZ using TMZ-resistant human glioblastoma cell line-derived xenograft models. Our data suggest that treatment in which SMPs are combined with TMZ reduces tumor growth and extends survival in mice bearing xenograft tumors derived from both TMZ-resistant and TMZ-sensitive human glioblastoma cell lines. Our findings demonstrate that combining SMPs with TMZ may have potential as a promising strategy for the treatment of GBM.

Expression and Prognostic Value of the Immune Checkpoints Galectin-9 and PD-L1 in Glioblastomas

Immunotherapeutic targeting of the PD-1/PD-L1 axis has been widely implemented for treatment of several cancer types but shown disappointing results in glioblastomas (GBMs), potentially due to compensatory mechanisms of other expressed immune checkpoints. Galectin-9 is an immune-checkpoint protein that facilitates T-cell exhaustion and apoptosis and could be a potential target for immune-checkpoint inhibition. A total of 163 GBMs IDH wildtype were immunostained with anti-Galectin-9 and PD-L1 antibodies. Software-based quantitation of immunostainings was performed and co-expression was investigated using double immunofluorescence. Both Galectin-9 and PD-L1 protein expression were found in all 163 tumors and showed a significant positive correlation (p = 0.0017). Galectin-9 expression varied from 0.01% to 32% (mean = 6.61%), while PD-L1 membrane expression ranged from 0.003% to 0.14% (mean = 0.048%) of total tumor area. Expression of Galectin-9 and PD-L1 was found on both microglia/macrophages and tumor cells, and colocalization of both markers was found in 88.3% of tumors. In multivariate analysis, neither Galectin-9 (HR = 0.99), PD-L1 (HR = 1.05), nor their combinations showed prognostic value. Galectin-9 and PD-L1 were expressed in all investigated GBMs and the majority of patients had co-expression, which may provide rationale for multi-targeted immune checkpoint inhibition.

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI

Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI). Data from the Department of Neurosurgery, Leipzig University Hospital, were retrospectively analyzed. Patients treated between 2014 and 2019, aged older than 18 years and with adequate peri-operative MRI were included. Volumetric assessment was performed manually. One hundred and eighty-three patients were included. Survival of patients with mGBM was significantly shorter (p < 0.0001). Univariate analysis revealed extent of resection, adjuvant therapy regimen, residual tumor volume, tumor necrosis volume and ratio of tumor necrosis to initial volume as statistically significant for overall survival. In multivariate Cox regression, however, only EOR (for uGBM and the entire cohort) and adjuvant therapy were independently significant for survival. Decreased ratio of tumor necrosis to initial tumor volume and extent of resection were associated with prolonged survival in mGBM but failed to achieve statistical significance in multivariate analysis.

Combining Magnetic Resonance Imaging with Systemic Monocyte Evaluation for the Implementation of GBM Management

Magnetic resonance imaging (MRI) is the gold standard for glioblastoma (GBM) patient evaluation. Additional non-invasive diagnostic modalities are needed. GBM is heavily infiltrated with tumor-associated macrophages (TAMs) that can be found in peripheral blood. FKBP51s supports alternative-macrophage polarization. Herein, we assessed FKBP51s expression in circulating monocytes from 14 GBM patients. The M2 monocyte phenotype was investigated by qPCR and flow cytometry using antibodies against PD-L1, CD163, FKBP51s, and CD14. MRI assessed morphologic features of the tumors that were aligned to flow cytometry data. PD-L1 expression on circulating monocytes correlated with MRI tumor necrosis score. A wider expansion in circulating CD163/monocytes was measured. These monocytes resulted in a dramatic decrease in patients with an MRI diagnosis of complete but not partial surgical removal of the tumor. Importantly, in patients with residual tumor, most of the peripheral monocytes that in the preoperative stage were CD163/FKBP51s- had turned into CD163/FKBP51s+. After Stupp therapy, CD163/FKBP51s+ monocytes were almost absent in a case of pseudoprogression, while two patients with stable or true disease progression showed sustained levels in such circulating monocytes. Our work provides preliminary but meaningful and novel results that deserve to be confirmed in a larger patient cohort, in support of potential usefulness in GBM monitoring of CD163/FKBP51s/CD14 immunophenotype in adjunct to MRI.

Strategies to better treat glioblastoma: antiangiogenic agents and endothelial cell targeting agents

Glioblastoma multiforme (GBM) is the most prevalent and aggressive form of glioma, with poor prognosis and high mortality rates. As GBM is a highly vascularized cancer, antiangiogenic therapies to halt or minimize the rate of tumor growth are critical to improving treatment. In this review, antiangiogenic therapies, including small-molecule drugs, nucleic acids and proteins and peptides, are discussed. The authors further explore biomaterials that have been utilized to increase the bioavailability and bioactivity of antiangiogenic factors for better antitumor responses in GBM. Finally, the authors summarize the current status of biomaterial-based targeting moieties that target endothelial cells in GBM to more efficiently deliver therapeutics to these cells and avoid off-target cell or organ side effects.

Hypoxic Roadmap of Glioblastoma-Learning about Directions and Distances in the Brain Tumor Environment

Malignant brain tumor-glioblastoma is not only difficult to treat but also hard to study and model. One of the reasons for these is their heterogeneity, i.e., individual tumors consisting of cancer cells that are unlike each other. Such diverse cells can thrive due to the simultaneous co-evolution of anatomic niches and adaption into zones with distorted homeostasis of oxygen. It dampens cytotoxic and immune therapies as the response depends on the cellular composition and its adaptation to hypoxia. We explored what transcriptome reposition strategies are used by cells in the different areas of the tumor. We created the hypoxic map by differential expression analysis between hypoxic and cellular features using RNA sequencing data cross-referenced with the tumor's anatomic features (Ivy Glioblastoma Atlas Project). The molecular functions of genes differentially expressed in the hypoxic regions were analyzed by a systematic review of the gene ontology analysis. To put a hypoxic niche signature into a clinical context, we associated the model with patients' survival datasets (The Cancer Genome Atlas). The most unique class of genes in the hypoxic area of the tumor was associated with the process of autophagy. Both hypoxic and cellular anatomic features were enriched in immune response genes whose, along with autophagy cluster genes, had the power to predict glioblastoma patient survival. Our analysis revealed that transcriptome responsive to hypoxia predicted worse patients' outcomes by driving tumor cell adaptation to metabolic stress and immune escape.

Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

OBJECTIVE

To assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade.

METHODS

This retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed.

RESULTS

The inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05).

CONCLUSION

The 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade.

KEY POINTS

• Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade. • High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance. • The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.

Macropinocytosis confers resistance to therapies targeting cancer anabolism

Macropinocytic cancer cells scavenge amino acids from extracellular proteins. Here, we show that consuming necrotic cell debris via macropinocytosis (necrocytosis) offers additional anabolic benefits. A click chemistry-based flux assay reveals that necrocytosis provides not only amino acids, but sugars, fatty acids and nucleotides for biosynthesis, conferring resistance to therapies targeting anabolic pathways. Indeed, necrotic cell debris allow macropinocytic breast and prostate cancer cells to proliferate, despite fatty acid synthase inhibition. Standard therapies such as gemcitabine, 5-fluorouracil (5-FU), doxorubicin and gamma-irradiation directly or indirectly target nucleotide biosynthesis, creating stress that is relieved by scavenged nucleotides. Strikingly, necrotic debris also render macropinocytic, but not non-macropinocytic, pancreas and breast cancer cells resistant to these treatments. Selective, genetic inhibition of macropinocytosis confirms that necrocytosis both supports tumor growth and limits the effectiveness of 5-FU in vivo. Therefore, this study establishes necrocytosis as a mechanism for drug resistance.

Macropinocytosis allows cancer cells to cope with nutrient stress. Here, the authors use a selective, genetic approach to inhibit macropinocytosis and show that consuming necrotic cell debris via macropinocytosis—necrocytosis—affords resistance to many therapies that target biosynthesis.

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

BACKGROUND

To compare the diagnostic performance of two-dimensional (2D) and three-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade.

METHODS

This retrospective study included 123 meningioma patients [90 World Health Organization (WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared.

RESULTS

The reproducibility between observers was excellent, with intraclass correlation coefficients of 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively. WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) and higher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas. The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3D fractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878), 82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnostic performance than the 2D fractal model (p<0.001).

CONCLUSION

The 3D fractal analysis proved superiority in diagnostic performance to 2D fractal analysis in grading meningioma.

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

To investigate the effect of image preprocessing, in respect to intensity inhomogeneity correction and noise filtering, on the robustness and reproducibility of the radiomics features extracted from the Glioblastoma (GBM) tumor in multimodal MR images (mMRI). In this study, for each patient 1461 radiomics features were extracted from GBM subregions (i.e., edema, necrosis, enhancement, and tumor) of mMRI (i.e., FLAIR, T1, T1C, and T2) volumes for five preprocessing combinations (in total 116 880 radiomics features). The robustness and reproducibility of the radiomics features were assessed under four comparisons: (a) Baseline versus modified bias field; (b) Baseline versus modified bias field followed by noise filtering; (c) Baseline versus modified noise, and (d) Baseline versus modified noise followed bias field correction. The concordance correlation coefficient (CCC), dynamic range (DR), and interclass correlation coefficient (ICC) were used as metrics. Shape features and subsequently, local binary pattern (LBP) filtered images were highly stable and reproducible against bias field correction and noise filtering in all measurements. In all MRI modalities, necrosis regions (NC: n ® ~449/1461, 30%) had the highest number of highly robust features, with CCC and DR >= 0.9, in comparison with edema (ED: n ® ~296/1461, 20%), enhanced (EN: n ® ~ 281/1461, 19%) and active‐tumor regions (TM: n ® ~254/1461, 17%). The necrosis regions (NC: n¯ ~ 449/1461, 30%) had a higher number of highly robust features (CCC and DR >= 0.9) than edema (ED: n¯ ~ 296/1461, 20%), enhanced (EN: n¯ ~ 281/1461, 19%) and active‐tumor (TM: n¯ ~ 254/1461, 17%) regions across all modalities. Furthermore, our results identified that the percentage of high reproducible features with ICC >= 0.9 after bias field correction (23.2%), and bias field correction followed by noise filtering (22.4%) were higher in contrast with noise smoothing and also noise smoothing follow by bias correction. These preliminary findings imply that preprocessing sequences can also have a significant impact on the robustness and reproducibility of mMRI‐based radiomics features and identification of generalizable and consistent preprocessing algorithms is a pivotal step before imposing radiomics biomarkers into the clinic for GBM patients.

Hydrolyzed Rutin Decreases Worsening of Anaplasia in Glioblastoma Relapse

Background:

Gliomas are aggressive and resilient tumors. Progression to advanced stages of malignancy, characterized by cell anaplasia, necrosis, and reduced response to conventional surgery or therapeutic adjuvant, are critical challenges in glioma therapy. Relapse of the disease poses a considerable challenge for management. Hence, new compounds are required to improve therapeutic response. As hydrolyzed rutin (HR), a compound modified via rutin deglycosylation, as well as some flavonoids demonstrated antiproliferative effect for glioblastoma, these are considered potential epigenetic drugs.

Objective:

The purpose of this study was to determine the antitumor activity and evaluate the potential for modifying tumor aggressivity of rutin hydrolysates for treating both primary and relapsed glioblastoma.

Methods:

The glioblastoma cell line, U251, was used for analyzing cell cycle inhibition and apoptosis and for establishing the GBM mouse model. Mice with GBM were treated with HR to verify antitumor activity. Histological analysis was used to evaluate HR interference in aggressive behavior and glioma grade. Immunohistochemistry, comet assay, and thiobarbituric acid reactive substance (TBARS) values were used to evaluate the mechanism of HR action.

Results:

HR is an antiproliferative and antitumoral compound that inhibits the cell cycle via a p53- independent pathway. HR reduces tumor growth and aggression, mainly by decreasing mitosis and necrosis rates without genotoxicity, which is suggestive of epigenetic modulation.

Conclusion:

HR possesses antitumor activity and decreases anaplasia in glioblastoma, inhibiting progression to malignant stages of the disease. HR can improve the effectiveness of response to conventional therapy, which has a crucial role in recurrent glioma.

Glioblastoma heterogeneity and the tumour microenvironment: implications for preclinical research and development of new treatments

Glioblastoma is the deadliest form of brain cancer. Aside from inadequate treatment options, one of the main reasons glioblastoma is so lethal is the rapid growth of tumour cells coupled with continuous cell invasion into surrounding healthy brain tissue. Significant intra- and inter-tumour heterogeneity associated with differences in the corresponding tumour microenvironments contributes greatly to glioblastoma progression. Within this tumour microenvironment, the extracellular matrix profoundly influences the way cancer cells become invasive, and changes to extracellular (pH and oxygen levels) and metabolic (glucose and lactate) components support glioblastoma growth. Furthermore, studies on clinical samples have revealed that the tumour microenvironment is highly immunosuppressive which contributes to failure in immunotherapy treatments. Although technically possible, many components of the tumour microenvironment have not yet been the focus of glioblastoma therapies, despite growing evidence of its importance to glioblastoma malignancy. Here, we review recent progress in the characterisation of the glioblastoma tumour microenvironment and the sources of tumour heterogeneity in human clinical material. We also discuss the latest advances in technologies for personalised and in vitro preclinical studies using brain organoid models to better model glioblastoma and its interactions with the surrounding healthy brain tissue, which may play an essential role in developing new and more personalised treatments for this aggressive type of cancer.

A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival

OBJECTIVES

To construct a radiomics nomogram for the individualized estimation of the survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI, which could facilitate the clinical decision-making for GBM patients.

MATERIALS AND METHODS

A total of 105 eligible GBM patients (57 in the long-term and 48 in the short-term survival groups, separated by an overall survival of 12 months) were selected from the Cancer Genome Atlas. These patients were divided into a training set (n = 70) and a validation set (n = 35). Radiomics features (n = 4000) were extracted from multiple regions of the GBM using multiparametric MRI. Then, a radiomics signature was constructed using least absolute shrinkage and selection operator regression for each patient in the training set. Combined with clinical risk factors, a radiomics nomogram was constructed based on a multivariate logistic regression model. The performance of this radiomics nomogram was assessed by calibration, discrimination, and clinical usefulness.

RESULTS

The radiomics signature consisted of 25 selected features and performed better than clinical risk factors (i.e., age, Karnofsky performance status, and treatment strategy) in survival stratification. When the radiomics signature and clinical risk factors were combined, the radiomics nomogram exhibited promising discrimination in the training (C-index, 0.971) and validation (C-index, 0.974) sets. The favorable calibration and decision curve analysis indicated the clinical usefulness of the radiomics nomogram.

CONCLUSIONS

The presented radiomics nomogram, as a non-invasive prediction tool, could exhibit a favorable predictive accuracy and provide individualized probabilities of survival stratification for GBM patients.

KEY POINTS

• Non-invasive survival stratification of GBM patients can be obtained with a radiomics nomogram. • The proposed nomogram constructed by radiomics signature selected from 4000 radiomics features, combined with independent clinical risk factors such as age, Karnofsky performance status, and treatment strategy. • The proposed radiomics nomogram exhibited good calibration and discrimination for survival stratification of GBM patients in both training (C-index, 0.971) and validation (C-index, 0.974) sets.

The Path Toward PET-Guided Radiation Therapy for Glioblastoma in Laboratory Animals: A Mini Review

Glioblastoma is the most aggressive and malignant primary brain tumor in adults. Despite the current state-of-the-art treatment, which consists of maximal surgical resection followed by radiation therapy, concomitant, and adjuvant chemotherapy, progression remains rapid due to aggressive tumor characteristics. Several new therapeutic targets have been investigated using chemotherapeutics and targeted molecular drugs, however, the intrinsic resistance to induced cell death of brain cells impede the effectiveness of systemic therapies. Also, the unique immune environment of the central nervous system imposes challenges for immune-based therapeutics. Therefore, it is important to consider other approaches to treat these tumors. There is a well-known dose-response relationship for glioblastoma with increased survival with increasing doses, but this effect seems to cap around 60 Gy, due to increased toxicity to the normal brain. Currently, radiation treatment planning of glioblastoma patients relies on CT and MRI that does not visualize the heterogeneous nature of the tumor, and consequently, a homogenous dose is delivered to the entire tumor. Metabolic imaging, such as positron-emission tomography, allows to visualize the heterogeneous tumor environment. Using these metabolic imaging techniques, an approach called dose painting can be used to deliver a higher dose to the tumor regions with high malignancy and/or radiation resistance. Preclinical studies are required for evaluating the benefits of novel radiation treatment strategies, such as PET-based dose painting. The aim of this review is to give a brief overview of promising PET tracers that can be evaluated in laboratory animals to bridge the gap between PET-based dose painting in glioblastoma patients.

Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time

This paper presents a novel set of image texture features generalizing standard grey-level co-occurrence matrices (GLCM) to multimodal image data through joint intensity matrices (JIMs). These are used to predict the survival of glioblastoma multiforme (GBM) patients from multimodal MRI data. The scans of 73 GBM patients from the Cancer Imaging Archive are used in our study. Necrosis, active tumor, and edema/invasion subregions of GBM phenotypes are segmented using the coregistration of contrast-enhanced T1-weighted (CE-T1) images and its corresponding fluid-attenuated inversion recovery (FLAIR) images. Texture features are then computed from the JIM of these GBM subregions and a random forest model is employed to classify patients into short or long survival groups. Our survival analysis identified JIM features in necrotic (e.g., entropy and inverse-variance) and edema (e.g., entropy and contrast) subregions that are moderately correlated with survival time (i.e., Spearman rank correlation of 0.35). Moreover, nine features were found to be associated with GBM survival with a Hazard-ratio range of 0.38-2.1 and a significance level of p < 0.05 following Holm-Bonferroni correction. These features also led to the highest accuracy in a univariate analysis for predicting the survival group of patients, with AUC values in the range of 68-70%. Considering multiple features for this task, JIM features led to significantly higher AUC values than those based on standard GLCMs and gene expression. Furthermore, an AUC of 77.56% with p = 0.003 was achieved when combining JIM, GLCM, and gene expression features into a single radiogenomic signature. In summary, our study demonstrated the usefulness of modeling the joint intensity characteristics of CE-T1 and FLAIR images for predicting the prognosis of patients with GBM.