Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

Hypoxia within the glioblastoma tumor microenvironment: a master saboteur of novel treatments

Glioblastoma (GBM) tumors are the most aggressive primary brain tumors in adults that, despite maximum treatment, carry a dismal prognosis. GBM tumors exhibit tissue hypoxia, which promotes tumor aggressiveness and maintenance of glioma stem cells and creates an overall immunosuppressive landscape. This article reviews how hypoxic conditions overlap with inflammatory responses, favoring the proliferation of immunosuppressive cells and inhibiting cytotoxic T cell development. Immunotherapies, including vaccines, immune checkpoint inhibitors, and CAR-T cell therapy, represent promising avenues for GBM treatment. However, challenges such as tumor heterogeneity, immunosuppressive TME, and BBB restrictiveness hinder their effectiveness. Strategies to address these challenges, including combination therapies and targeting hypoxia, are actively being explored to improve outcomes for GBM patients. Targeting hypoxia in combination with immunotherapy represents a potential strategy to enhance treatment efficacy.

Insights into the glioblastoma tumor microenvironment: current and emerging therapeutic approaches

Glioblastoma (GB) is an intrusive and recurrent primary brain tumor with low survivability. The heterogeneity of the tumor microenvironment plays a crucial role in the stemness and proliferation of GB. The tumor microenvironment induces tumor heterogeneity of cancer cells by facilitating clonal evolution and promoting multidrug resistance, leading to cancer cell progression and metastasis. It also plays an important role in angiogenesis to nourish the hypoxic tumor environment. There is a strong interaction of neoplastic cells with their surrounding microenvironment that comprise several immune and non-immune cellular components. The tumor microenvironment is a complex network of immune components like microglia, macrophages, T cells, B cells, natural killer (NK) cells, dendritic cells and myeloid-derived suppressor cells, and non-immune components such as extracellular matrix, endothelial cells, astrocytes and neurons. The prognosis of GB is thus challenging, making it a difficult target for therapeutic interventions. The current therapeutic approaches target these regulators of tumor micro-environment through both generalized and personalized approaches. The review provides a summary of important milestones in GB research, factors regulating tumor microenvironment and promoting angiogenesis and potential therapeutic agents widely used for the treatment of GB patients.

Smart Nanoscale Extracellular Vesicles in the Brain: Unveiling their Biology, Diagnostic Potential, and Therapeutic Applications

Information exchange is essential for the brain, where it communicates the physiological and pathological signals to the periphery and vice versa. Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound cellular informants actively transferring informative calls to and from the brain via lipids, proteins, and nucleic acid cargos. In recent years, EVs have also been widely used to understand brain function, given their "cell-like" properties. On the one hand, the presence of neuron and astrocyte-derived EVs in biological fluids have been exploited as biomarkers to understand the mechanisms and progression of multiple neurological disorders; on the other, EVs have been used in designing targeted therapies due to their potential to cross the blood-brain-barrier (BBB). Despite the expanding literature on EVs in the context of central nervous system (CNS) physiology and related disorders, a comprehensive compilation of the existing knowledge still needs to be made available. In the current review, we provide a detailed insight into the multifaceted role of brain-derived extracellular vesicles (BDEVs) in the intricate regulation of brain physiology. Our focus extends to the significance of these EVs in a spectrum of disorders, including brain tumors, neurodegenerative conditions, neuropsychiatric diseases, autoimmune disorders, and others. Throughout the review, parallels are drawn for using EVs as biomarkers for various disorders, evaluating their utility in early detection and monitoring. Additionally, we discuss the promising prospects of utilizing EVs in targeted therapy while acknowledging the existing limitations and challenges associated with their applications in clinical scenarios. A foundational comprehension of the current state-of-the-art in EV research is essential for informing the design of future studies.

Single-Cell Transcriptomics Reveals the Heterogeneity of the Immune Landscape of IDH-Wild-Type High-Grade Gliomas

Isocitrate dehydrogenase (IDH)-wild-type (WT) high-grade gliomas, especially glioblastomas, are highly aggressive and have an immunosuppressive tumor microenvironment. Although tumor-infiltrating immune cells are known to play a critical role in glioma genesis, their heterogeneity and intercellular interactions remain poorly understood. In this study, we constructed a single-cell transcriptome landscape of immune cells from tumor tissue and matching peripheral blood mononuclear cells (PBMC) from IDH-WT high-grade glioma patients. Our analysis identified two subsets of tumor-associated macrophages (TAM) in tumors with the highest protumorigenesis signatures, highlighting their potential role in glioma progression. We also investigated the T-cell trajectory and identified the aryl hydrocarbon receptor (AHR) as a regulator of T-cell dysfunction, providing a potential target for glioma immunotherapy. We further demonstrated that knockout of AHR decreased chimeric antigen receptor (CAR) T-cell exhaustion and improved CAR T-cell antitumor efficacy both in vitro and in vivo. Finally, we explored intercellular communication mediated by ligand-receptor interactions within the tumor microenvironment and PBMCs and revealed the unique cellular interactions present in the tumor microenvironment. Taken together, our study provides a comprehensive immune landscape of IDH-WT high-grade gliomas and offers potential drug targets for glioma immunotherapy.

Immunity in malignant brain tumors: Tumor entities, role of immunotherapy, and specific contribution of myeloid cells to the brain tumor microenvironment

Malignant brain tumors lack effective treatment, that can improve their poor overall survival achieved with standard of care. Advancement in different cancer treatments has shifted the focus in brain tumor research and clinical trials toward immunotherapy-based approaches. The investigation of the immune cell landscape revealed a dominance of myeloid cells in the tumor microenvironment. Their exact roles and functions are the subject of ongoing research. Current evidence suggests a complex interplay of tumor cells and myeloid cells with competing functions toward support vs. control of tumor growth. Here, we provide a brief overview of the three most abundant brain tumor entities: meningioma, glioma, and brain metastases. We also describe the field of ongoing immunotherapy trials and their results, including immune checkpoint inhibitors, vaccination studies, oncolytic viral therapy, and CAR-T cells. Finally, we summarize the phenotypes of microglia, monocyte-derived macrophages, border-associated macrophages, neutrophils, and potential novel therapy targets.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study

Background

Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS).

Methods

We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features.

Results

For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS.

Conclusions

Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study

Background

Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS).

Methods

We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, and T2 FLAIR) and manual segmentations from 2 centers: 53 from 1 center formed the internal cohort and 16 from the other center formed the external cohort. We pretrained a deep learning model on a public adult brain tumor data set (BraTS 2021), and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 12-month survival from diagnosis. One model used only data obtained at diagnosis prior to any therapy (baseline study) and the other used data at both diagnosis and post-RT (post-RT study).

Results

Overall survival prediction accuracy was 77% and 81% for the baseline study, and 85% and 78% for the post-RT study, for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS.

Conclusions

Machine learning analysis of MRI radiomics has potential to accurately and noninvasively predict which pediatric patients with DMG will survive less than 12 months from the time of diagnosis to provide patient stratification and guide therapy.

A neural tract-inspired conduit for facile, on-demand biopsy of glioblastoma

Background

A major hurdle to effectively treating glioblastoma (GBM) patients is the lack of longitudinal information about tumor progression, evolution, and treatment response.

Methods

In this study, we report the use of a neural tract-inspired conduit containing aligned polymeric nanofibers (i.e., an aligned nanofiber device) to enable on-demand access to GBM tumors in 2 rodent models. Depending on the experiment, a humanized U87MG xenograft and/or F98-GFP+ syngeneic rat tumor model was chosen to test the safety and functionality of the device in providing continuous sampling access to the tumor and its microenvironment.

Results

The aligned nanofiber device was safe and provided a high quantity of quality genomic materials suitable for omics analyses and yielded a sufficient number of live cells for in vitro expansion and screening. Transcriptomic and genomic analyses demonstrated continuity between material extracted from the device and that of the primary, intracortical tumor (in the in vivo model).

Conclusions

The results establish the potential of this neural tract-inspired, aligned nanofiber device as an on-demand, safe, and minimally invasive access point, thus enabling rapid, high-throughput, longitudinal assessment of tumor and its microenvironment, ultimately leading to more informed clinical treatment strategies.

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

PURPOSE OF REVIEW

To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption.

RECENT FINDINGS

A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation.

SUMMARY

While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging

INTRODUCTION

Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS).

METHODOLOGY

A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models.

RESULTS

For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case.

CONCLUSIONS

When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

The application of electrosprayed minocycline-loaded PLGA microparticles for the treatment of glioblastoma

The survival of patients with glioblastoma multiforme (GBM), the most common and invasive form of malignant brain tumors, remains poor despite advances in current treatment methods including surgery, radiotherapy, and chemotherapy. Minocycline is a semi-synthetic tetracycline derivative that has been widely used as an antibiotic and more recently, it has been utilized as an antiangiogenic factor to inhibit tumorigenesis. The objective of this study was to investigate the utilization of electrospraying process to fabricate minocycline-loaded poly(lactic-co-glycolic acid) (PLGA) microparticles with high drug loading and loading efficiency and to evaluate their ability to induce cell toxicity in human glioblastoma (i.e., U87-MG) cells. The results from this study demonstrated that solvent mixture of dicholoromethane (DCM) and methanol is the optimal solvent combination for minocycline and larger amount of methanol (i.e., 70:30) resulted in a higher drug loading. All three solvent ratios of DCM:methanol tested produced microparticles that were both spherical and smooth, all in the micron size range. The electrosprayed microparticles were able to elicit a cytotoxic response in U87-MG glioblastoma cells at a lower concentration of drug compared to the free drug. This work provides proof of concept to the hypothesis that electrosprayed minocycline-loaded PLGA microparticles can be a promising agent for the treatment of GBM and could have potential application for cancer therapies.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints

PURPOSE

Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma.

METHODS

PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test.

RESULTS

Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall.

CONCLUSION

Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Overall survival prediction for high-grade glioma patients using mathematical modeling of tumor cell infiltration

PURPOSE

This study aimed at applying a mathematical framework for the prediction of high-grade gliomas (HGGs) cell invasion into normal tissues for guiding the clinical target delineation, and at investigating the possibility of using tumor infiltration maps for patient overall survival (OS) prediction.

MATERIAL & METHODS

A model describing tumor infiltration into normal tissue was applied to 93 HGG cases. Tumor infiltration maps and corresponding isocontours with different cell densities were produced. ROC curves were used to seek correlations between the patient OS and the volume encompassed by a particular isocontour. Area-Under-the-Curve (AUC) values were used to determine the isocontour having the highest predictive ability. The optimal cut-off volume, having the highest sensitivity and specificity, for each isocontour was used to divide the patients in two groups for a Kaplan-Meier survival analysis.

RESULTS

The highest AUC value was obtained for the isocontour of cell densities 1000 cells/mm3 and 2000 cells/mm3, equal to 0.77 (p < 0.05). Correlation with the GTV yielded an AUC of 0.73 (p < 0.05). The Kaplan-Meier survival analysis using the 1000 cells/mm3 isocontour and the ROC optimal cut-off volume for patient group selection rendered a hazard ratio (HR) of 2.7 (p < 0.05), while the GTV rendered a HR = 1.6 (p < 0.05).

CONCLUSION

The simulated tumor cell invasion is a stronger predictor of overall survival than the segmented GTV, indicating the importance of using mathematical models for cell invasion to assist in the definition of the target for HGG patients.

Prognostic Values of Systemic Inflammatory Immunological Markers in Glioblastoma: A Systematic Review and Meta-Analysis

BACKGROUND

Neutrophils are an important part of the tumor microenvironment, which stimulates inflammatory processes through phagocytosis, degranulation, release of small DNA fragments (cell-free DNA), and presentation of antigens. Since neutrophils accumulate in peripheral blood in patients with advanced-stage cancer, a high neutrophil-to-lymphocyte ratio can be a biomarker of a poor prognosis in patients with glioblastoma. The present study aimed to explore the prognostic value of the preoperative levels of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and cell-free DNA (cfDNA) to better predict prognostic implications in the survival rate of glioblastoma patients.

METHODS

The meta-analysis was carried out according to the recommendations and standards established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Databases of PubMed, EBSCO, and Medline were systematically searched to select all the relevant studies published up to December 2022.

RESULTS

Poorer prognoses were recorded in patients with a high NLR or PLR when compared with the patients with a low NLR or PLR (HR 1.51, 95% CI 1.24-1.83, p < 0.0001 and HR 1.34, 95% CI 1.10-1.63, p < 0.01, respectively). Similarly, a worse prognosis was reported for patients with a higher cfDNA (HR 2.35, 95% CI 1.27-4.36, p < 0.01). The SII and SIRI values were not related to glioblastoma survival (p = 0.0533 and p = 0.482, respectively).

CONCLUSIONS

Thus, NLR, PLR, and cfDNA, unlike SII and SIRI, appeared to be useful and convenient peripheral inflammatory markers to assess the prognosis in glioblastoma.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Design and Synthesis of 3-(β-d-Glucopyranosyl)-4-amino/4-guanidino Pyrazole Derivatives and Analysis of Their Glycogen Phosphorylase Inhibitory Potential

Glycogen phosphorylase (GP) is a key regulator of glucose levels and, with that, an important target for the discovery of novel treatments against type 2 diabetes. β-d-Glucopyranosyl derivatives have provided some of the most potent GP inhibitors discovered to date. In this regard, C-β-d-glucopyranosyl azole type inhibitors proved to be particularly effective, with 2- and 4-β-d-glucopyranosyl imidazoles among the most potent designed to date. His377 backbone C=O hydrogen bonding and ion-ion interactions of the protonated imidazole with Asp283 from the 280s loop, stabilizing the inactive state, were proposed as crucial to the observed potencies. Towards further exploring these features, 4-amino-3-(β-d-glucopyranosyl)-5-phenyl-1H-pyrazole (3) and 3-(β-d-glucopyranosyl)-4-guanidino-5-phenyl-1H-pyrazole (4) were designed and synthesized with the potential to exploit similar interactions. Binding assay experiments against rabbit muscle GPb revealed 3 as a moderate inhibitor (IC50 = 565 µM), but 4 displayed no inhibition at 625 µM concentration. Towards understanding the observed inhibitions, docking and post-docking molecular mechanics-generalized Born surface area (MM-GBSA) binding free energy calculations were performed, together with Monte Carlo and density functional theory (DFT) calculations on the free unbound ligands. The computations revealed that while 3 was predicted to hydrogen bond with His377 C=O in its favoured tautomeric state, the interactions with Asp283 were not direct and there were no ion-ion interactions; for 4, the most stable tautomer did not have the His377 backbone C=O interaction and while ion-ion interactions and direct hydrogen bonding with Asp283 were predicted, the conformational strain and entropy loss of the ligand in the bound state was significant. The importance of consideration of tautomeric states and ligand strain for glucose analogues in the confined space of the catalytic site with the 280s loop in the closed position was highlighted.

Transcriptomic and connectomic correlates of differential spatial patterning among gliomas

Unravelling the complex events driving grade-specific spatial distribution of brain tumour occurrence requires rich datasets from both healthy individuals and patients. Here, we combined open-access data from The Cancer Genome Atlas, the UK Biobank and the Allen Brain Human Atlas to disentangle how the different spatial occurrences of glioblastoma multiforme and low-grade gliomas are linked to brain network features and the normative transcriptional profiles of brain regions. From MRI of brain tumour patients, we first constructed a grade-related frequency map of the regional occurrence of low-grade gliomas and the more aggressive glioblastoma multiforme. Using associated mRNA transcription data, we derived a set of differential gene expressions from glioblastoma multiforme and low-grade gliomas tissues of the same patients. By combining the resulting values with normative gene expressions from post-mortem brain tissue, we constructed a grade-related expression map indicating which brain regions express genes dysregulated in aggressive gliomas. Additionally, we derived an expression map of genes previously associated with tumour subtypes in a genome-wide association study (tumour-related genes). There were significant associations between grade-related frequency, grade-related expression and tumour-related expression maps, as well as functional brain network features (specifically, nodal strength and participation coefficient) that are implicated in neurological and psychiatric disorders. These findings identify brain network dynamics and transcriptomic signatures as key factors in regional vulnerability for glioblastoma multiforme and low-grade glioma occurrence, placing primary brain tumours within a well established framework of neurological and psychiatric cortical alterations.

Intracranial nanomedicine-gel with deep brain-penetration for glioblastoma therapy

Glioblastoma Multiforme (GBM) is one of the challenging tumors to treat as it recurs, almost 100%, even after surgery, radiation, and chemotherapy. In many cases, recurrence happens within 2-3cm depth of the resected tumor margin, indicating the inefficacy of current anti-glioma drugs to penetrate deep into the brain tissue. Here, we report an injectable nanoparticle-gel system, capable of providing deep brain penetration of drug up to 4 cm, releasing in a sustained manner up to >15 days. The system consists of ∼222 nm sized PLGA nanoparticles (NP-222) loaded with an anti-glioma drug, Carmustine (BCNU), and coated with a thick layer of polyethylene glycol (PEG). Upon release of the drug from PLGA core, it will interact with the outer PEG-layer leading to the formation of PEG-BCNU nanocomplexes of size ∼33 nm (BCNU-NC-33), which could penetrate >4 cm deep into the brain tissue compared to the free drug (< 5 mm). In vitro drug release showed sustained release of drug for 15 days by BCNU-NP gel, and enhanced cytotoxicity by BCNU-NC-33 drug-nanocomplexes in glioma cell lines. Ex vivo goat-brain phantom studies showed drug diffusion up to 4 cm in tissue and in vivo brain-diffusion studies showed almost complete coverage within the rat brain (∼1.2 cm), with ∼55% drug retained in the tissue by day-15, compared to only ∼5% for free BCNU. Rat orthotopic glioma studies showed excellent anti-tumor efficacy by BCNU-NP gel compared to free drug, indicating the potential of the gel-system for anti-glioma therapy. In effect, we demonstrate a unique method of sustained release of drug in the brain using larger PLGA nanoparticles acting as a reservoir while deep-penetration of the released drug was achieved by in situ formation of drug-nanocomplexes of size <50 nm which is less than the native pore size of brain tissue (> 100 nm). This method will have a major impact on a challenging field of brain drug delivery.

Feasibility of fractionated gamma knife radiosurgery in the management of newly diagnosed Glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with overall survival remaining poor despite ongoing efforts to explore new treatment paradigms. Given these outcomes, efforts have been made to shorten treatment time. Recent data report on the safety of CyberKnife (CK) fractionated stereotactic radiosurgery (SRS) in the management of GBM using a five-fraction regimen. The latest Gamma Knife (GK) model also supports frameless SRS, and outcomes using GK SRS in the management of primary GBM have not yet been reported.

OBJECTIVE

To report on the feasibility of five-fraction SRS with the GammaKnife ICON in the management of newly diagnosed GBM.

METHODS

In this single institutional study, we retrospectively reviewed all patients from our medical center from January 2017 through December 2021 who received fractionated SRS with Gamma Knife ICON for newly diagnosed GBM. Patient demographics, upfront surgical margins, molecular subtyping, radiation treatment volumes, systemic therapies, and follow-up imaging findings were extracted to report on oncologic outcomes.

RESULTS

We identified six patients treated within the above time frame. Median age at diagnosis was 73.5 years, 66% were male, and had a median Karnofsky Performance Status (KPS) of 70. All tumors were IDH wild-type, and all but one were MGMT methylated and received concurrent temozolomide (TMZ). Within this group, progression free survival was comparable to that of historical data without significant radiation-induced toxicities.

CONCLUSION

Gamma Knife ICON may be discussed as a potential treatment option for select GBM patients and warrants further investigation in the prospective setting.

Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review

Simple Summary

The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers.

Abstract

Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging

Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.

Minimally Invasive Detection of IDH1 Mutation With Cell-Free Circulating Tumor DNA and D-2-Hydroxyglutarate, D/L-2-Hydroxyglutarate Ratio in Gliomas

Isocitrate dehydrogenase-1 (IDH1) mutation is accepted as one of the earliest events in tumorigenesis in gliomas. This mutation causes preferential accumulation of D- relative to L-enantiomer of 2-hydroxyglutarate (2-HG). Minimally invasive techniques to detect IDH1 mutation may prove useful for clinical practice. We adopted 2 different diagnostic approaches to detect IDH1 mutation status in glioma patients: Evaluation of D- and L-2-HG levels in cerebrospinal fluid (CSF), urine, and plasma, and identification of IDH1 mutation using cell-free circulating tumor DNA (ctDNA) in CSF and plasma. Forty-nine glioma patients in different stages were included. Levels of D- and L-2-HG were determined using liquid chromatography-tandem mass spectrometry; IDH1 R132H mutation was determined by digital-PCR. D-2-HG levels and D/L-2-HG ratio (rDL) in CSF and rDL in plasma were significantly higher in the mutant group than in the wild-type group (p = 0.029, 0.032, 0.001, respectively). The IDH1 mutation detection rates in CSF- and plasma-ctDNA were 63.2% and 25.0%, respectively. These data indicate that D-2-HG values in CSF and rDL in plasma and CSF can be considered as significant contributors to the identification of IDH1 mutation status. In addition, detection of IDH1 mutation in CSF-ctDNA from glioma patients provides a basis for future use of ctDNA for minimally invasive clinical assessment of gliomas.

Prognostic Values of Combined Ratios of White Blood Cells in Glioblastoma: A Retrospective Study

In some malignant tumours, the changes in neutrophil counts in relation to other blood cells are connected with unfavourable prognosis. Nevertheless, the prognostic value of the combinations of the haematological components in glioblastoma (GBM) remains under dispute. The clinical significance of the neutrophil-to-lymphocyte ratio (NLR), systemic immune inflammation index (SII), and systemic inflammation response index (SIRI) was investigated in our study. We retrospectively studied 358 patients (males n = 195; females n = 163) aged 59.9 ± 13.5 yrs with newly diagnosed glioma and admitted to the Neurosurgery Centre. Routine blood tests and clinical characteristics were recorded within the first hour of hospital admission. The inflammatory variables: NLR, SII and SIRI exceeded the reference values and were significantly elevated in Grade 3 and Grade 4 tumour. The Cox model analysis showed that the age ≥ 63 years, NLR ≥ 4.56 × 103/µL, SII ≥ 2003 × 103/µL and SIRI ≥ 3.03 × 103/µL significantly increased the risk of death in Grade 4 tumour patients. In the inflammatory variables, NLR demonstrated the highest impact on the survival time (HR 1.56; 95% CI 1.145-2.127; p = 0.005). In the first Polish study including GBM patients, the age in relation to simple parameters derived from complete blood cell count were found to have prognostic implications in the survival rate.

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma

BACKGROUND AND PURPOSE

Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

MATERIALS AND METHODS

Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60-74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29-43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.

RESULTS

The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472-0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692-0.750) and progression-free survival (concordance index = 0.680-0.715).

CONCLUSIONS

A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18-80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey

BACKGROUND

Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to augment surgical care. The aim of this study was to similarly evaluate the attitudes of surgeons and the wider surgical team toward the role of AI in neurosurgery.

METHODS

In a 2-stage cross sectional survey, an initial open-question qualitative survey was created to determine the perspective of the surgical team on AI in neurosurgery including surgeons, anesthetists, nurses, and operating room practitioners. Thematic analysis was performed to develop a second-stage quantitative survey that was distributed via social media. We assessed the extent to which they agreed and were comfortable with real-world AI implementation using a 5-point Likert scale.

RESULTS

In the first-stage survey, 33 participants responded. Six main themes were identified: imaging interpretation and preoperative diagnosis, coordination of the surgical team, operative planning, real-time alert of hazards and complications, autonomous surgery, and postoperative management and follow-up. In the second stage, 100 participants responded. Responders somewhat agreed or strongly agreed about AI being used for imaging interpretation (62%), operative planning (82%), coordination of the surgical team (70%), real-time alert of hazards and complications (85%), and autonomous surgery (66%). The role of AI within postoperative management and follow-up was less agreeable (49%).

CONCLUSIONS

This survey highlights that the majority of surgeons and the wider surgical team both agree and are comfortable with the application of AI within neurosurgery.

Survival prediction of patients suffering from glioblastoma based on two-branch DenseNet using multi-channel features

PURPOSE

As the most common primary intracranial tumor, glioblastoma (GBM) is a malignant tumor that originated from neuroepithelial tissue, accounting for 40-50% of brain tumors. Precise survival prediction for patients suffering from GBM can not only help patients and doctors formulate treatment plans, but also help researchers understand the development of the disease and stimulate medical development.

METHODS

In view of the tedious process of manual feature extraction and selection in traditional radiomics, we propose an end-to-end survival prediction model based on DenseNet to extract the features of magnetic resonance images including T1-weighted post-contrast images and T2-weighted images through two-branch networks. After segmenting the region of interest, the original image, the image of tumor region and the image without tumor are combined as input sample sets with three channels. Additionally, for some patients having only one of T1- or T2-weighted images, One2One CycleGAN is used to generate the T1 image from the T2 image, or vice versa. Flipping and rotating are also used for sample augmentation.

RESULT

By using the augmented training sample set to train the model, the classification and prediction accuracy of the two-branch DenseNet survival prediction model can reach up to 94%, and the Kaplan-Meier survival curve indicates that the model can classify patients into high-risk group and low-risk group based on whether they could survive for more than three years.

CONCLUSION

The classification and prediction results of the model and the survival analysis demonstrate that our model can get superior classification results which can be referenced by doctors and patients' families for developing medical plans. However, improving the loss function and expanding the sample size can further improve the prediction results, which are the target of our subsequent research.

Evaluation of the anti-angiogenic effect of bevacizumab on rat C6 glioma by spectral computed tomography

BACKGROUND

Anti-angiogenic drugs have become a research hotspot in recent years. However, dynamically observing their therapeutic effect at different time points during treatment is a clinical problem.

PURPOSE

To explore the feasibility of the quantitative parameters of spectral computed tomography (CT) in evaluating the anti-angiogenic effect of bevacizumab on rat C6 glioma.

MATERIAL AND METHODS

Twenty-six male Sprague-Dawley rats were used to establish the C6 glioma model. The rats were randomly divided into the experimental group (n = 13) and control group (n = 13). The experimental group was intraperitoneally injected with 0.2 µL/g bevacizumab every day, whereas the control group was injected with the same dose of normal saline every day for one week. Spectral CT scanning was performed on the 4th and 8th days after treatment; meanwhile, the brain tissues were collected by heart perfusion for H&E staining, and VEGF and HIF-1α immunohistochemical staining.

RESULTS

On the 4th and 8th days, significant differences in the 70-keV single-energy CT value, slope of the energy spectrum curve, and iodine concentration were found between the experimental group and the control group. Correlation analysis between immunohistochemistry and quantitative parameters of spectral CT showed that the single energy CT value of 70 keV, slope of the energy spectrum curve, and concentration of iodine were positively correlated with VEGF and HIF-1α at different time points in the experimental group and the control group.

CONCLUSION

Spectral CT multi-parameter imaging can be employed as a new method to evaluate the anti-angiogenic effect of bevacizumab on rat C6 glioma.

Promises and Perils of Artificial Intelligence in Neurosurgery

Artificial intelligence (AI)-facilitated clinical automation is expected to become increasingly prevalent in the near future. AI techniques may permit rapid and detailed analysis of the large quantities of clinical data generated in modern healthcare settings, at a level that is otherwise impossible by humans. Subsequently, AI may enhance clinical practice by pushing the limits of diagnostics, clinical decision making, and prognostication. Moreover, if combined with surgical robotics and other surgical adjuncts such as image guidance, AI may find its way into the operating room and permit more accurate interventions, with fewer errors. Despite the considerable hype surrounding the impending medical AI revolution, little has been written about potential downsides to increasing clinical automation. These may include both direct and indirect consequences. Directly, faulty, inadequately trained, or poorly understood algorithms may produce erroneous results, which may have wide-scale impact. Indirectly, increasing use of automation may exacerbate de-skilling of human physicians due to over-reliance, poor understanding, overconfidence, and lack of necessary vigilance of an automated clinical workflow. Many of these negative phenomena have already been witnessed in other industries that have already undergone, or are undergoing "automation revolutions," namely commercial aviation and the automotive industry. This narrative review explores the potential benefits and consequences of the anticipated medical AI revolution from a neurosurgical perspective.

Advanced magnetic resonance imaging to support clinical drug development for malignant glioma

Even though the treatment options and survival of patients with glioblastoma multiforme (GBM), the most common type of malignant glioma, have improved over the past decade, there is still a high unmet medical need to develop novel therapies. Complexity in pathology and therapy require biomarkers to characterize tumors, to define malignant and active areas, to assess disease prognosis, and to quantify and monitor therapy response. While conventional magnetic resonance imaging (MRI) techniques have improved these assessments, limitations remain. In this review, we evaluate the role of various non-invasive biomarkers based on advanced structural and functional MRI techniques in the context of GBM drug development over the past 5 years.

Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58-0.98), accuracy (0.69-0.98), and C-index (0.66-0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review

Simple Summary

An accurate survival analysis is crucial for disease management in glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a quantitative assessment of GBM tumours, an ever-growing number of studies aimed at investigating the role of diffusion MRI metrics in survival prediction of GBM patients. Since the role of diffusion MRI in prediction and evaluation of survival outcomes has not been fully addressed and results are often controversial or unsatisfactory, we performed this systematic review in order to collect, summarize and evaluate all studies evaluating the role of diffusion MRI metrics in predicting survival in GBM patients. We found that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Abstract

Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

MRI and 18FET-PET Predict Survival Benefit from Bevacizumab Plus Radiotherapy in Patients with Isocitrate Dehydrogenase Wild-type Glioblastoma: Results from the Randomized ARTE Trial

PURPOSE

To explore a prognostic or predictive role of MRI and O-(2-¹⁸F-fluoroethyl)-L-tyrosine (¹⁸FET) PET parameters for outcome in the randomized multicenter trial ARTE that compared bevacizumab plus radiotherapy with radiotherpay alone in elderly patients with glioblastoma.

PATIENTS AND METHODS

Patients with isocitrate dehydrogenase wild-type glioblastoma ages 65 years or older were included in this post hoc analysis. Tumor volumetric and apparent diffusion coefficient (ADC) analyses of serial MRI scans from 67 patients and serial ¹⁸FET-PET tumor-to-brain intensity ratios (TBRs) from 31 patients were analyzed blinded for treatment arm and outcome. Multivariate Cox regression analysis was done to account for established prognostic factors and treatment arm.

RESULTS

Overall survival benefit from bevacizumab plus radiotherapy compared with radiotherapy alone was observed for larger pretreatment MRI contrast-enhancing tumor [HR per cm³ 0.94; 95% confidence interval (CI), 0.89-0.99] and for higher ADC (HR 0.18; CI, 0.05-0.66). Higher ¹⁸FET-TBR on pretreatment PET scans was associated with inferior overall survival in both arms. Response assessed by standard MRI-based Response Assessment in Neuro-Oncology criteria was associated with overall survival in the bevacizumab plus radiotherapy arm by trend only (P = 0.09). High ¹⁸FET-TBR of noncontrast-enhancing tumor portions during bevacizumab therapy was associated with inferior overall survival on multivariate analysis (HR 5.97; CI, 1.16-30.8).

CONCLUSIONS

Large pretreatment contrast-enhancing tumor mass and higher ADCs identify patients who may experience a survival benefit from bevacizumab plus radiotherapy. Persistent ¹⁸FET-PET signal of no longer contrast-enhancing tumor after concomitant bevacizumab plus radiotherapy suggests pseudoresponse and predicts poor outcome.

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning

Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.

Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors

Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting. Keywords: Adults and Pediatrics, Brain/Brain Stem, CNS, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Image Postprocessing, Informatics, Neural Networks, Neuro-Oncology, Oncology, Treatment Effects, Tumor Response Supplemental material is available for this article. © RSNA, 2020.

MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment

Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

Intranasal perillyl alcohol therapy improves survival of patients with recurrent glioblastoma harboring mutant variant for MTHFR rs1801133 polymorphism

Background

Polymorphisms in MTHFR gene influence risk and overall survival of patients with brain tumor. Global genomic DNA (gDNA) methylation profile from tumor tissues is replicated in peripheral leukocytes. This study aimed to draw a correlation between rs1801133 MTHFR variants, gDNA methylation and overall survival of patients with recurrent glioblastoma (rGBM) under perillyl alcohol (POH) treatment.

Methods

gDNA from whole blood was extracted using a commercially available kit (Axygen) and quantified by spectrophotometry. Global gDNA methylation was determined by ELISA and rs1801133 polymorphism by PCR-RFLP. Statistical analysis of gDNA methylation profile and rs1801133 variants included Mann-Whitney, Kruskal-Wallis, Spearman point-biserial correlation tests (SPSS and Graphpad Prism packages; significant results for effect size higher than 0.4). Prognostic value of gDNA methylation and rs1801133 variants considered survival profiles at 25 weeks of POH treatment, having the date of protocol adhesion as starting count and death as the final event.

Results

Most rGBM patients showed global gDNA hypomethylation (median = 31.7%) and a significant, moderate and negative correlation between TT genotype and gDNA hypomethylation (median = 13.35%; rho = − 0.520; p = 0.003) compared to CC variant (median = 32.10%), which was not observed for CT variant (median = 33.34%; rho = − 0.289; p = 0.06). gDNA hypermethylated phenotype (median = 131.90%) exhibited significant, moderate and negative correlations between TT genotype (median = 112.02%) and gDNA hypermethylation levels when compared to CC (median = 132.45%; rho = − 0,450; p = 0.04) or CT (median = 137.80%; rho = − 0.518; p = 0.023) variants. TT variant of rs1801133 significantly decreased gDNA methylation levels for both patient groups, when compared to CC (d values: hypomethylated = 1.189; hypermethylated = 0.979) or CT (d values: hypomethylated = 0.597; hypermethylated = 1.167) variants. Positive prognostic for rGBM patients may be assigned to gDNA hypermethylation for survivors above 25 weeks of treatment (median = 88 weeks); and TT variant of rs1801133 regardless POH treatment length.

Conclusion

rGBM patients under POH-based therapy harboring hypermethylated phenotype and TT variant for rs1801133 had longer survival. Intranasal POH therapy mitigates detrimental effects of gDNA hypomethylation and improved survival of patients with rGBM harboring TT mutant variant for MTHFR rs1801133 polymorphism.

Trial registration

CONEP -9681- 25,000.009267 / 2004. Registered 12th July, 2004.

Attitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery

BACKGROUND

Artificial intelligence (AI) may favorably support surgeons but can result in concern among patients and their relatives. The aim of this study was to evaluate attitudes of patients and their relatives regarding use of AI in neurosurgery.

METHODS

In a 2-stage cross-sectional survey, a qualitative survey was administered to a focus group of former patients to investigate their perception of AI and its role in neurosurgery. Five themes were identified and used to generate a case-based quantitative survey administered to inpatients and their relatives over a 2-week period. Presented AI platforms were rated appropriate and acceptable using 5-point Likert scales. Demographic data were collected. χ² test was used to determine whether demographics influenced participants' attitudes.

RESULTS

In the first stage, 20 participants responded. Five themes were identified: interpretation of imaging (4/20; 20%), operative planning (5/20; 25%), real-time alert of potential complications (10/20; 50%), partially autonomous surgery (6/20; 30%), and fully autonomous surgery (3/20; 15%). In the second stage, 107 participants responded. Most thought it appropriate and acceptable to use AI for imaging interpretation (76.7%; 66.3%), operative planning (76.7%; 75.8%), real-time alert of potential complications (82.2%; 72.9%), and partially autonomous surgery (58%; 47.7%). Conversely, most did not think that fully autonomous surgery was appropriate (27.1%) or acceptable (17.7%). Demographics did not have a significant influence on perception.

CONCLUSIONS

Most patients and their relatives believed that AI has a role in neurosurgery and found it acceptable. Notable exceptions were fully autonomous systems, with most wanting the neurosurgeon ultimately to remain in control.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Machine learning and glioma imaging biomarkers

AIM

To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

MATERIALS AND METHODS

The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction.

RESULTS

Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).

CONCLUSION

Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.