Clinical features associated with the efficacy of chemotherapy in patients with glioblastoma (GBM): a surveillance, epidemiology, and end results (SEER) analysis

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.

Development of Predicting Nomograms for Diffuse Astrocytoma and Anaplastic Astrocytoma: A Study Based on the Surveillance, Epidemiology, and End Results Database

BACKGROUND

Astrocytoma is a type of adult-type diffuse gliomas that includes diffuse astrocytoma (DA) and anaplastic astrocytoma (AA). However, comprehensive investigations into the risk assessment and prognosis of DA and AA using population-based studies remain noticeably scarce.

METHODS

In this study, we developed 2 predictive nomograms to evaluate the susceptibility and prognosis associated with DA and AA. The study cohort comprised 3837 individuals diagnosed with DA or AA between 2010 and 2019 selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent predictors were identified and used to construct the nomograms for overall death and cancer-specific death rates. The performance of the models was assessed using C-index, calibration curves, and receiver operating characteristic curve, and the clinical applicability was evaluated using decision curve analysis.

RESULTS

The receiver operating characteristic curves in this study show excellent clinical applicability and predictive power. Notably, the area under the curves of the training and verification queues was higher than 0.80, thereby cementing the models' precision. Additionally, the calibration plots demonstrate that the anticipated mortality rates strikingly match the measured values. This alignment of figures is sustained in the validation cohort. Furthermore, the decision curve analysis corroborates the models' translational potential, reinforcing their relevance within real-world clinical settings.

CONCLUSIONS

The presented nomograms have not only exhibited good predictive performance but also showcased pragmatic clinical utility in prognosticating patient outcomes. Significantly, this will undoubtedly serve as a valuable asset for oncologists, facilitating informed treatment decisions and meticulous follow-up planning.

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma

OBJECTIVE

To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.

METHODS

Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA).

RESULTS

A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance.

CONCLUSION

Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

Practical Nomograms and Risk Stratification System for Predicting the Overall and Cancer-specific Survival in Patients with Anaplastic Astrocytoma

OBJECTIVE

Anaplastic astrocytoma (AA) is an uncommon primary brain tumor with highly variable clinical outcomes. Our study aimed to develop practical tools for clinical decision-making in a population-based cohort study.

METHODS

Data from 2997 patients diagnosed with AA between 2004 and 2015 were retrospectively extracted from the Surveillance, Epidemiology, and End Results database. The Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses were applied to select factors and establish prognostic nomograms. The discriminatory ability of these nomogram models was evaluated using the concordance index and receiver operating characteristic curve. Risk stratifications were established based on the nomograms.

RESULTS

Selected 2997 AA patients were distributed into the training cohort (70%, 2097) and the validation cohort (30%, 900). Age, household income, tumor site, extension, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). In the training cohort, our nomograms for OS and CSS exhibited good predictive accuracy with concordance index values of 0.752 (95% CI: 0.741-0.764) and 0.753 (95% CI: 0.741-0.765), respectively. Calibration and decision curve analyses curves showed that the nomograms demonstrated considerable consistency and satisfactory clinical utilities. With the establishment of nomograms, we stratified AA patients into high- and low-risk groups, and constructed risk stratification systems for OS and CSS.

CONCLUSIONS

We constructed two predictive nomograms and risk classification systems to effectively predict the OS and CSS rates in AA patients. These models were internally validated with considerable accuracy and reliability and might be helpful in future clinical practices.

Competing Risk Model to Determine the Prognostic Factors for Patients with Gliosarcoma

BACKGROUND

Gliosarcoma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods because of a lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors.

METHODS

For this study, we extracted data of patients diagnosed with GSM from the SEER (Surveillance Epidemiology and End Results) database between 2000 and 2019. To account for the influence of competing risks, we used a Cumulative Incidence Function. Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for patients with GSM, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. In addition, we used both univariate and multivariate Cox analysis to account for non-GSM-related deaths that may confound our research.

RESULTS

The competing risk model showed that age, marital status, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60-70 years, ≥71 years) and larger tumor size (≥5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy, and being single were identified as protective factors against GSM-related death.

CONCLUSIONS

Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the use of protective measures may contribute to improved outcomes and reduced mortality for patients with GSM.

The Effect of Therapy Regimen on Antitumor Efficacy of the Nanosomal Doxorubicin against Rat Glioblastoma 101.8

One of the key problems of glioblastoma treatment is the low effectiveness of chemotherapeutic drugs. Incorporation of doxorubicin into PLGA nanoparticles allows increasing the antitumor effect of the cytostatics against experimental rat glioblastoma 101.8. Animal survival, tumor volume, and oncogene expression in tumor cells were compared after early (days 2, 5, and 8 after tumor implantation) and late (days 8, 11, and 14) start of the therapy. At late start, a significant increase in the expression of oncogenes Gdnf, Pdgfra, and Melk and genes determining the development of multidrug resistance Abcb1b and Mgmt was revealed. At early start of therapy, only the expression of oncogenes Gdnf, Pdgfra, and Melk was enhanced. Early start of treatment prolonged the survival time and increased tumor growth inhibition by 141.4 and 95.7%, respectively, in comparison with the untreated group; these differences were not observed in the group with late start of therapy. The results indicate that the time of initiation of therapy is a critical parameter affecting the antitumor efficacy of DOX-PLGA.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.

A two-pronged approach against glioblastoma: drug repurposing and nanoformulation design for in situ-controlled release

Glioblastoma (GB) is one of the most lethal types of neoplasms. Its biologically aggressive nature and the presence of the blood-brain barrier (BBB) limit the efficacy of standard therapies. Several strategies are currently being developed to both overcome the BBB and deliver drugs site specifically to tumor cells. This work hypothesizes a two-pronged approach to tackle GB: drug repurposing with celecoxib (CXB) and a nanoformulation using ultra-small nanostructured lipid carriers (usNLCs). CXB antitumor druggable activity was inspected bioinformatically and screened in four glioma cell lines aiming at the comparison with temozolomide (TMZ), as standard of care. Delving into formulation design, it was tailored aiming at (i) improving the drug solubility/loading properties, (ii) assigning a thermal-triggerable drug release based on a lipid matrix with a low melting point, and (iii) enhancing the cytotoxic effect by selecting a template targetable to tumor cells. For this purpose, an integrated analysis of the critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs) was conducted under the umbrella of a quality by design approach. CMAs that demonstrate a high-risk level for the final quality and performance of the usNLCs include the drug solubility in lipids (solid and liquid), the lipid composition (envisioning a thermoresponsive approach), the ratio between lipids (solid vs. liquid), and the surfactant type and concentration. Particle size was shown to be governed by the interaction lipid-surfactant followed by surfactant type. The drug encapsulation did not influence colloidal characteristics, making it a promising carrier for lipophilic drugs. In general, usNLCs exhibited a controlled drug release during the 72 h at 37 °C with a final release of ca. 25%, while at 45 °C this was doubled. The in vitro cellular performance depended on the surfactant type and lipid composition, with the formulations containing a sole solid lipid (Suppocire® NB) and Kolliphor® RH40 as surfactant being the most cytotoxic. usNLCs with an average diameter of ca. 70 nm and a narrow size distribution (PdI lower than 0.2) were yielded, exhibiting high stability, drug protection, sustained and thermo-sensitive release properties, and high cytotoxicity to glioma cells, meeting the suitable CQAs for parenteral administration. This formulation may pave the way to a multi-addressable purpose to improve GB treatment.

S100A6 is a potential diagnostic and prognostic biomarker for human glioma

S100 calcium-binding protein A6 (S100A6) is a protein that belongs to the S100 family. The present study aimed to investigate the function of S100A6 in the diagnosis and survival prediction of glioma and elucidated the potential processes affecting glioma development. The Cancer Genome Atlas database was searched to identify the relationship among S100A6 expression, immune cell infiltration, clinicopathological parameters and glioma prognosis. Several clinical cases were used to verify these findings. S100A6 gene expression was high in glioma tissues, suggesting its diagnostic significance. In particular, S100A6 upregulation in glioma tissues exhibited a significant and positive correlation with the World Health Organization (WHO) grade, histological type, age, sex, primary treatment outcomes, 1p/19q codeletion, isocitrate dehydrogenase (IDH) status, overall survival (OS), progression-free interval and disease-specific survival. Kaplan-Meier and Cox regression analyses revealed that S100A6 gene expression can independently function as a risk factor affecting the prognosis of patients with glioma. Furthermore, Gene Ontology functional enrichment analysis revealed that S100A6 is implicated in immune responses and that the expression profiles of S100A6 are linked to the immune microenvironment. Furthermore, immunohistochemistry revealed that increased S100A6 protein levels are correlated with age, 1p/19q codeletion, IDH status, WHO grade and OS. The present findings suggest that increased S100A6 expression is an indicator of the dismal prognosis of patients with glioma and that it can be used as a potential diagnostic biomarker for this condition.

Prognostic nomogram for glioblastoma (GBM) patients presenting with distant extension: a seer-based study

BACKGROUND

Glioblastoma (GBM) with distant extension is rarely reported. We retrieved the data of GBM patients from the SEER database to identify the prognostic factors of GBM with distant extension and constructed a nomogram to predict the overall survival (OS) of these patients.

METHODS

The data of GBM patients between 2003 and 2018 were retrieved from the SEER Database. 181 GBM patients with distant extension were randomly divided into the training cohort (n = 129) and the validation cohort (n = 52) at a ratio of 7:3. The prognostic factors associated with the OS of the GBM patients were identified through univariate and multivariate cox analyses. A nomogram was constructed based on the training cohort to predict OS, and its clinical value was verified using the validation cohort data.

RESULTS

Kaplan-Meier curves showed that the prognosis was significantly worse for GBM patients with distant extension than GBM patients without distant extension. Stage (GBM patients with distant extension) was independent prognostic factor of survival. Multivariate Cox analyses demonstrated that age, surgery, radiotherapy and chemotherapy were independent risk factors for OS of GBM patients presenting with distant extension. The C-indexes of the nomogram for predicting OS were 0.755 (95% CI 0.713-0.797) and 0.757 (95% CI 0.703-0.811) for the training and validation cohorts, respectively. The calibration curves of both cohorts showed good consistency. The area under the curve (AUC) for predicting 0.25-year, 0.5-year and 1-year OS in the training cohort were 0.793, 0.864 and 0.867, respectively, and that in the validation cohort were 0.845, 0.828 and 0.803, respectively. The decision curve analysis (DCA) curves showed that the model to predict the 0.25-year, 0.5-year and 1-year OS probabilities was good.

CONCLUSION

Stage (GBM patients with distant extension) is independent prognostic factor for GBM patients. Age, surgery, radiotherapy and chemotherapy are independent prognostic factors for GBM patients presenting with distant extension, and the nomogram based on these factors can accurately predict the 0.25-year, 0.5-year and 1-year OS of these patients.

An update on neurobiological mechanisms involved in the development of chemotherapy-induced cognitive impairment (CICI)

Cancer is the second leading cause of death worldwide despite efforts in early diagnosis of the disease and advances in treatment. The use of drugs that exert toxic effects on tumor cells or chemotherapy is one of the most widely used treatments against cancer. However, its low toxic selectivity affects both healthy cells and cancer cells. It has been reported that chemotherapeutic drugs may generate neurotoxicity that induces deleterious effects of chemotherapy in the central nervous system. In this sense, patients report decreased cognitive abilities, such as memory, learning, and some executive functions after chemotherapy. This chemotherapy-induced cognitive impairment (CICI) develops during treatment and persists even after chemotherapy. Here we present a review of the literature on the main neurobiological mechanisms involved in CICI using a Boolean formula following the steps of the PRISMA guidelines that were used to perform statements searches in various databases. The main mechanisms described in the literature to explain CRCI include direct and indirect mechanisms that induce neurotoxicity by chemotherapeutic agents. Therefore, this review provides a general understanding of the neurobiological mechanisms of CICI and the possible therapeutic targets to prevent it..

Clinical features and outcomes of pediatric intracranial gliomas: results from single center's 226 cases and corroborated with SEER database

BACKGROUND

Pediatric gliomas are the most common central nervous system (CNS) tumors in children and adolescents and show different clinical and histopathological characteristics from the adult. The prognostic factors were poorly defined in pediatric intracranial gliomas.

METHODS

We collected pediatric intracranial glioma cases in our institution between February 2011 and June 2022. The patient clinical data, tumor growth characteristics, treatments, and follow-up data were analyzed by Cox regression analysis to identify impact factors on the prognosis of pediatric intracranial glioma patients. To corroborate our data, an independent cohort of pediatric intracranial glioma from the Surveillance, Epidemiology, and End Results Program (SEER) database was analyzed.

RESULTS

A total of 181 cases of pediatric low-grade glioma (PLGG) and 45 cases of pediatric high-grade glioma (PHGG) were included. In multivariate Cox regression analysis, tumor size > 59.5 mm (p = 0.006) and non-gross total resection (non-GTR; subtotal resection, STR, p = 0.042; biopsy, p = 0.012) were associated with decreased overall survival (OS) in PLGG patients. In PHGG patients, only chemotherapy (p = 0.023) was associated with OS while tumor size was marginally prognostic for OS (p = 0.051). Additional independent analysis of 2734 PLGG and 741 PHGG from the SEER database corroborated that larger tumor size was associated with decreased OS in LGG (p = 0.001) and HGG (p < 0.001) patients, separately.

CONCLUSION

In this study, we found that tumor size was a significant prognostic factor for the OS of PLGG patients in our series. Besides the tumor size, the extent of resection also independently impacted the prognosis of PLGG patients. While in PHGG patients, only chemotherapy was associated with improved OS and tumor size was marginally prognostic.

Valproic acid as a radio-sensitizer in glioma: A systematic review and meta-analysis

Background

Histone deacetylase inhibitors (HDACi) including valproic acid (VPA) have the potential to improve radiotherapy (RT) efficacy and reduce treatment adverse events (AE) via epigenetic modification and radio-sensitization of neoplastic cells. This systematic review and meta-analysis aimed to assess the efficacy and AE associated with HDACi used as radio-sensitizers in adult solid organ malignancy patients.

Methods

A systematic review utilized electronic searches of MEDLINE(Ovid), Embase(Ovid), The Cochrane Library, and the International Clinical Trials Registry Platform to identify studies examining the efficacy and AEs associated with HDACi treatment in solid organ malignancy patients undergoing RT. Meta-analysis was performed with overall survival (OS) reported as hazard ratios (HR) as the primary outcome measure. OS reported as median survival difference, and AEs were secondary outcome measures.

Results

Ten studies reporting on the efficacy and/or AEs of HDACi in RT-treated solid organ malignancy patients met inclusion criteria. All included studies focused on HDACi valproic acid (VPA) in high-grade glioma patients, of which 9 studies (n = 6138) evaluated OS and 5 studies (n = 1055) examined AEs. The addition of VPA to RT treatment protocols resulted in improved OS (HR = 0.80, 95% CI 0.67-0.96). No studies focusing on non-glioma solid organ malignancy patients, or non-VPA HDACi met the inclusion criteria for this review.

Conclusions

This review suggests that glioma patients undergoing RT may experience prolonged survival due to HDACi VPA administration. Further randomized controlled trials are required to validate these findings. Additionally, more research into the use of HDACi radio-adjuvant treatment in non-glioma solid organ malignancies is warranted.

Double Immunohistochemical Staining on Formalin-Fixed Paraffin-Embedded Tissue Samples to Study Vascular Co-option

Vascular co-option is a non-angiogenic mechanism whereby tumor growth and progression move on by hijacking the pre-existing and nonmalignant blood vessels and is employed by various tumors to grow and metastasize.The histopathological identification of co-opted blood vessels is complex, and no specific markers were defined, but it is critical to develop new and possibly more effective therapeutic strategies. Here, in glioblastoma, we show that the co-opted blood vessels can be identified, by double immunohistochemical staining, as weak CD31⁺ vessels with reduced P-gp expression and proliferation and surrounded by highly proliferating and P-gp- or S100A10-expressing tumor cells. Results can be quantified by the Aperio Colocalization algorithm, which is a valid and robust method to handle and investigate large data sets.

Glioblastoma multiforme: Diagnosis, treatment, and invasion

Glioblastoma multiforme (GBM) is an essentially incurable brain tumor, which has been explored for approximately a century. Nowadays, surgical resection, chemotherapy, and radiation therapy are still the standardized therapeutic options. However, due to the intrinsic invasion and metastasis features and the resistance to chemotherapy, the survival rate of glioblastoma patients remains unsatisfactory. To improve the current situation, much more research is needed to provide comprehensive knowledge of GBM. In this review, we summarize the latest updates on GBM treatment and invasion. Firstly, we review the traditional and emerging therapies that have been used for GBM treatment. Given the limited efficiency of these therapies, we further discuss the role of invasion in GBM recurrence and progression, and present current research progress on the mode and mechanisms of GBM invasion.

Parvifloron D-based potential therapy for glioblastoma: Inducing apoptosis via the mitochondria dependent pathway

Glioblastoma (GB) is the most malignant and frequent primary tumor of the central nervous system. The lack of diagnostic tools and the poor prognosis associated with this tumor type leads to restricted and limited options of treatment, namely surgical resection and radio-chemotherapy. However, despite these treatments, in almost all cases, patients experience relapse, leading to survival rates shorter than 5 years (∼15-18 months after diagnosis). Novel therapeutic approaches are urgently required (either by discovering new medicines or by repurposing drugs) to surpass the limitations of conventional treatments and improve patients' survival rate and quality of life. In the present work, we investigated the antitumor potential of parvifloron D (ParvD), a drug lead of natural origin, in a GB cell line panel. This natural drug lead induced G2/M cell cycle arrest and apoptosis via activation of the intrinsic mitochondria-dependent pathway. Moreover, the necessary doses of ParvD to induce pronounced inhibitory effects were substantially lower than that of temozolomide (TMZ, first-line treatment) required to promote comparable effects. Therefore, ParvD may have the potential to overcome the resistance related to TMZ and contribute to the pursuit of hopeful treatments based on ParvD as a drug lead for future chemotherapeutics.

Trends and outcomes of early and late palliative care consultation for adult patients with glioblastoma: A SEER-Medicare retrospective study

Background

Glioblastoma (GBM) carries a poor prognosis despite standard of care. Early palliative care (PC) has been shown to enhance survival and quality of life while reducing healthcare costs for other cancers. This study investigates differences in PC timing on outcomes for patients with GBM.

Methods

This study used Surveillance, Epidemiology, and End Results (SEER)-Medicare data from 1997 to 2016. Based on ICD codes, three groups were defined: (1) early PC within 10 weeks of diagnosis, (2) late PC, and (3) no PC. Outcomes were compared between the three groups.

Results

Out of 10 812 patients with GBM, 1648 (15.24%) patients had PC consultation with an overall positive trend over time. There were no significant differences in patient characteristics. The late PC group had significantly higher number of hospice claims (1.06 ± 0.69) compared to those without PC, in the last month of life. There were significant differences in survival among the three groups (P < .0001), with late PC patients with the longest mean time to death from diagnosis (11.72 ± 13.20 months).

Conclusion

We present the first investigation of PC consultation prevalence and outcomes, stratified by early versus late timing, for adult GBM patients. Despite an overall increase in PC consultations, only a minority of GBM patients receive PC. Patients with late PC had the longest survival times and had greater hospice use in the last month of life compared to other subgroups. Prospective studies can provide additional valuable information about this unique population of patients with GBM.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Valproic Acid Enhanced Temozolomide-Induced Anticancer Activity in Human Glioma Through the p53-PUMA Apoptosis Pathway

Glioblastoma (GBM), the most lethal type of brain tumor in adults, has considerable cellular heterogeneity. The standard adjuvant chemotherapeutic agent for GBM, temozolomide (TMZ), has a modest response rate due to the development of drug resistance. Multiple studies have shown that valproic acid (VPA) can enhance GBM tumor control and prolong survival when given in conjunction with TMZ. However, the beneficial effect is variable. In this study, we analyzed the impact of VPA on GBM patient survival and its possible correlation with TMZ treatment and p53 gene mutation. In addition, the molecular mechanisms of TMZ in combination with VPA were examined using both p53 wild-type and p53 mutant human GBM cell lines. Our analysis of clinical data indicates that the survival benefit of a combined TMZ and VPA treatment in GBM patients is dependent on their p53 gene status. In cellular experiments, our results show that VPA enhanced the antineoplastic effect of TMZ by enhancing p53 activation and promoting the expression of its downstream pro-apoptotic protein, PUMA. Our study indicates that GBM patients with wild-type p53 may benefit from a combined TMZ+VPA treatment.