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Purpose: This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes., Methods: A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses., Results: T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051)., Conclusion: T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic., (© 2024. The Author(s).)

Background and Purpose: Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics., Materials and Methods: Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation., Results: Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction., Conclusions: Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization., Abbreviations: DSC = dynamic susceptibility contrast; FA = flip angle; GBCA = gadolinium-based contrast agent; GBM = glioblastoma; GE = gradient echo; IDH = isocitrate dehydrogenase; IDH m = IDH-mutant; IDH wt = IDH-wild-type; 1p19q cod = 1p19q codeleted; 1p19q int = 1p19q intact; MRI = magnetic resonance imaging; PCNSL = primary CNS lymphoma; PSR = percentage of signal recovery; Rec = recurrent; SAGE-EPI = spin-and-gradient-echo echoplanar imaging; CBV = cerebral blood volume; nrCBV = normalized relative CBV; ROI = region of interest; TE = echo time; TN = treatment-naïve; TR = repetition time., (© 2024 by American Journal of Neuroradiology.)

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios., (© 2024 by American Journal of Neuroradiology.)

Background and Purpose: The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase ( IDH )-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions., Materials and Methods: Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied ( n  = 71 from The Cancer Imaging Archive (TCIA) and n  = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps., Results: Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH -mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH -mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume ( P  < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH -mutant astrocytomas ( P  < .05), and ≥15% T2-FLAIR mismatch volume IDH- mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH -mutant astrocytoma ( P  < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone., Conclusions: T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH -mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists., (© 2024 by American Journal of Neuroradiology.)

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. Integration of these technologies allows for the development of image-based biomarkers, potentially reducing the need for invasive biopsy procedures and enabling personalized therapy targeting specific pro-tumoral signaling pathways and resistance mechanisms. Although significant progress has been made, ongoing innovation is essential to address remaining challenges and further improve these methodologies. Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification of IDH-wildtype glioblastoma., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Congress of Neurological Surgeons.)

Glioblastoma multiforme (GBM) is an aggressive primary brain tumor characterized by extensive heterogeneity and vascular proliferation. Hypoxic conditions in the tissue microenvironment are considered a pivotal player leading tumor progression. Specifically, hypoxia is known to activate inducible factors, such as hypoxia-inducible factor 1alpha (HIF-1α), which in turn can stimulate tumor neo-angiogenesis through activation of various downward mediators, such as the vascular endothelial growth factor (VEGF). Here, we aimed to explore the role of HIF-1α/VEGF immunophenotypes alone and in combination with other prognostic markers or clinical and image analysis data, as potential biomarkers of GBM prognosis and treatment efficacy. We performed a systematic review (Medline/Embase, and Pubmed database search was completed by 16th of April 2024 by two independent teams; PRISMA 2020). We evaluated methods of immunoassays, cell viability, or animal or patient survival methods of the retrieved studies to assess unbiased data. We used inclusion criteria, such as the evaluation of GBM prognosis based on HIF-1α/VEGF expression, other biomarkers or clinical and imaging manifestations in GBM related to HIF-1α/VEGF expression, application of immunoassays for protein expression, and evaluation of the effectiveness of GBM therapeutic strategies based on HIF-1α/VEGF expression. We used exclusion criteria, such as data not reporting both HIF-1α and VEGF or prognosis. We included 50 studies investigating in total 1319 GBM human specimens, 18 different cell lines or GBM-derived stem cells, and 6 different animal models, to identify the association of HIF-1α/VEGF immunophenotypes, and with other prognostic factors, clinical and macroscopic data in GBM prognosis and therapeutic approaches. We found that increased HIF-1α/VEGF expression in GBM correlates with oncogenic factors, such as miR-210-3p, Oct4, AKT, COX-2, PDGF-C, PLDO3, M2 polarization, or ALK, leading to unfavorable survival. Reduced HIF-1α/VEGF expression correlates with FIH-1, ADNP, or STAT1 upregulation, as well as with clinical manifestations, like epileptogenicity, and a favorable prognosis of GBM. Based on our data, HIF-1α or VEGF immunophenotypes may be a useful tool to clarify MRI-PET imaging data distinguishing between GBM tumor progression and pseudoprogression. Finally, HIF-1α/VEGF immunophenotypes can reflect GBM treatment efficacy, including combined first-line treatment with histone deacetylase inhibitors, thimerosal, or an active metabolite of irinotecan, as well as STAT3 inhibitors alone, and resulting in a favorable tumor prognosis and patient survival. These data were supported by a combination of variable methods used to evaluate HIF-1α/VEGF immunophenotypes. Data limitations may include the use of less sensitive detection methods in some cases. Overall, our data support HIF-1α/VEGF's role as biomarkers of GBM prognosis and treatment efficacy. [ABSTRACT FROM AUTHOR]

Purpose of the Review: The purpose of this review is to address the rising incidence of cerebral metastases in breast cancer patients, which is now estimated to affect 30–40% of advanced breast cancer (ABC) patients. Recent Findings: Magnetic resonance imaging (MRI) remains the gold standard for brain metastases (BM) diagnosis, with follow-up scans recommended every 3 months. Treatment options for BM include neurosurgery, stereotactic radiosurgery (SRS), stereotactic fractionated radiation therapy (SFRT), or whole brain radiation therapy (WBRT), selected based on BM number, size, and location. Local therapies like SRS or neurosurgery are preferred for single or oligo metastases, while SRS or WBRT may be used for multiple BM. Concurrent systemic treatment tailored to tumor biology is crucial, particularly with recent advancements in HER2-positive patient management.. Summary: Symptomatic BM warrants local treatment alongside systemic therapy, considering patient condition and prognosis. [ABSTRACT FROM AUTHOR]

This is a single arm, open label perioperative trial to assess the feasibility, pharmacokinetics and pharmacodynamics of treatment with safusidenib following biopsy, and prior to surgical resection in patients with IDH1 mutated glioma who have not received radiation therapy or chemotherapy. Fifteen participants will receive treatment in two parts. First, biopsy followed by one cycle (28 days) of safusidenib, an orally available, small molecular inhibitor of mutated IDH1, then maximal safe resection of the tumor (Part A). Second, after recovery from surgery, safusidenib until disease progression or unacceptable toxicity (Part B). This research will enable objective measurement of biological activity of safusidenib in patients with IDH1 mutated glioma. Anti-tumor activity will be assessed by progression free survival and time to next intervention. Clinical Trial Registration:NCT05577416 (ClinicalTrials.gov) Plain Language Summary Adult low-grade gliomas (aLGG) are primary brain cancers, defined by mutations in IDH1 or IDH2. When the IDH gene becomes abnormal (mutated), production of a metabolite that causes cancer cells to grow is increased. These tumors grow slowly but invade the normal functioning brain, making them nearly impossible to cure. The current standard of care treatment includes surgery, followed by radiation therapy and chemotherapy, the timing of which depends on the risk of cancer regrowth. Some patients may be suitable for monitoring with MRI scans alone, however recurrences will inevitably occur. Recently developed targeted mutant IDH inhibitors for aLGG patients may be beneficial both at diagnosis and recurrence. Notably, early treatment prior to radiation therapy and chemotherapy delays growth of aLGG and the need for subsequent radiation therapy and chemotherapy. Nevertheless, most patients will eventually suffer further tumor growth and the optimal timing and sequencing of these therapies remains an area of active research. This research investigates the mutant IDH1 inhibitor safusidenib. The researchers are conducting an innovative clinical trial where patients with aLGG, who have not received radiation therapy or chemotherapy, are treated with safusidenib following a biopsy and prior to surgical removal of their tumor. In this study they investigate whether this trial design is safe and feasible, and how safusidenib works; with the goal to better understand the optimal use of IDH inhibitors for patients with aLGG. Article highlights Introduction Background & rationale Adult low-grade gliomas (aLGGs) are defined by IDH1/2 mutations which lead to accumulation of 2-HG, an oncometabolite affecting tumorigenesis. 2-HG inhibits α-KG-dependent enzymes, affecting epigenetic regulation, collagen synthesis and cell signaling, hindering progenitor cell differentiation and promoting cancer. In addition, 2-HG promotes angiogenesis, an immunosuppressive tumor microenvironment and oxidative damage. aLGGs grow slowly, diffusely infiltrating the brain and are difficult to cure. Emergence of targeted mutant IDH inhibitors has shifted focus toward identifying prognostic markers and future combination studies. Current treatment of aLGG Treatment includes maximal safe surgical resection, radiation therapy and sequential chemotherapy. Early, extensive surgery improves overall survival. Active surveillance with MRI is suitable for some patients to delay side effects. Mutant IDH inhibitors for glioma Multiple mutant IDH inhibitors have shown promise in glioma. Vorasidenib (IDH1/2 inhibitor) improved progression-free survival in the INDIGO Phase III trial. Safusidenib (DS-1001b, AB-218) is an orally available small molecule inhibitor of mutant IDH1 with demonstrated brain penetration and clinical activity in Phase I trials. Perioperative trials There have been few therapeutic advances in glioma due to challenges in measuring drug penetration and pharmacodynamic (PD) evaluation. Perioperative trials, including biopsy to obtain pre-treatment tissue, are feasible and offer a means to examine the direct effects of new drugs. These studies allow evaluation of drug pharmacokinetics (PK) and PD outcomes. Objectives Hypothesis: Treatment with safusidenib following biopsy and prior to resection in newly diagnosed IDH1 mutated glioma is feasible. Estimate: >60% of screened patients completing planned investigations and procedures. Primary objective: Assess feasibility and acceptability of a perioperative study in IDH1 mutated aLGG patients. Secondary end points: Determine safusidenib toxicity, safety of two-stage surgery and biological activity by changes in 2-HG levels. Methods Participants, interventions, & outcomes Key inclusion: Adults ≥18 years with aLGG diagnosis not requiring urgent resection. Key exclusion: prior chemotherapy/radiation, cerebellar/brainstem tumors. Procedures: Open biopsy, oral safusidenib, second-stage resection, continued safusidenib post-resection. Data collection: tumor tissue, CSF and blood samples processed per protocols. PK/PD relationships: Evaluated by blood, tumor and CSF samples. Biological activity: Assessed by changes in 2-HG levels and IDH1 ctDNA. Data collection, management, & analysis Tumor tissue, CSF and blood samples collected for research. Safusidenib PK will be investigated to evaluate PK/PD relationships. Translational end points include paired whole-genome transcriptome profiling, single nuclei RNA sequencing, bulk metabolomics and DNA methylation. Spatial transcriptomics using in situ digital sequencing and spatial metabolomics on sections pre- and post-safusidenib treatment. Descriptive statistics for demographics, safety, PK, PD, efficacy and biomarker data by dose and time. Monitoring Toxicity: Monitored by adverse events, clinical tests and vital signs. Safety of surgery: 30-day morbidity and mortality recorded post-biopsy and resection. Radiological response: Determined by MRI changes and LGG-RANO criteria. Progression-free survival and time to treatment failure: Calculated from biopsy date. Conclusion Perioperative studies in aLGG offer a unique opportunity to study drug effects and are feasible, ethical and acceptable to patients. [ABSTRACT FROM AUTHOR]

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to non-invasively image blood-brain barrier leakage, yet its clinical utility has been hampered by issues such as noise and partial volume artifacts. In this retrospective study involving 306 adult patients with diffuse glioma, we applied deep learning-based super-resolution and denoising (DLSD) techniques to enhance the signal-to-noise ratio (SNR) and resolution of DCE-MRI. Quantitative analysis comparing standard DCE-MRI (std-DCE) and DL-enhanced DCE-MRI (DL-DCE) revealed that DL-DCE achieved significantly higher SNR and contrast-to-noise ratio (CNR) compared to std-DCE (SNR, 52.09 vs 27.21; CNR, 9.40 vs 4.71; P < 0.001 for all). Diagnostic performance assessed by the area under the receiver operating characteristic curve (AUROC) showed improved differentiation of WHO grades based on a pharmacokinetic parameter (AUC, 0.88 vs 0.83, P = 0.02), while remaining comparable to std-DCE in other parameters. Analysis of arterial input function (AIF) reliability demonstrated that exhibited superior agreement compared to , as indicated by mostly higher intraclass correlation coefficients (Time to peak, 0.79 vs 0.43, P < 0.001). In conclusion, DLSD significantly enhances both the image quality and reliability of DCE-MRI in patients with diffuse glioma, while maintaining or improving diagnostic performance. [ABSTRACT FROM AUTHOR]

Background: Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. Methods: We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan–Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. Results: This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan–Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Conclusions: Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms. [ABSTRACT FROM AUTHOR]

Cancer is the second leading cause of death in the United States and among the most prevalent diseases globally, with an incidence expected to grow because of smoking, pollution, poor dietary habits, obesity, and the rise in the older population. Given their ability to reduce risk factors, albeit with varying efficacy, nutrition and fasting could help prevent cancer and other age-related disorders. Calorie restriction (CR), various forms of intermittent fasting (IF) or periodic fasting (PF), and fasting-mimicking diets (FMDs) have been shown to improve health span, increase lifespan, and prevent or postpone cancer in rodents. The effects of specific diets and fasting regimens on aging and cancer appear to be mediated in part by the reduction in the activity of the growth hormone (GH)/insulin-like-growth-factor-I (IGF-1) axis. Nevertheless, recent data indicate that the alternation of low and normal levels of these hormones and factors may be ideal for optimizing longevity and function. Here, we review the role of nutrition, CR, and fasting/FMD on cancer, focusing on the hypothesis that the modulation of GH, IGF-1, and insulin signaling partly mediates the effect of these dietary interventions on cancer prevention. [ABSTRACT FROM AUTHOR]

Background and purpose: Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), in comparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. Methods: In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann--Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Results: Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Conclusions: Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment. [ABSTRACT FROM AUTHOR]

Background and purpose: Magnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas. Methods and materials: A retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression. Results: In the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival. Conclusions: The ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non−enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma. [ABSTRACT FROM AUTHOR]

Introduction: We aimed to verify the application value of the Alberta Stroke ProgramEarly CT Score (ASPECTS) based onmultiple post-labeling delay (multi-PLD) arterial spin labeling (ASL) for outcome assessment in acute ischemic stroke (AIS) patients. Method: The endpoint was modified Rankin scale score at 90 days (90-day mRS). Patients were divided into the good outcome (0-2) and poor outcome (3-6) groups. The independent samples t-test, Mann-Whitney U-test, and U-test were used to compare clinical and imaging parameters between groups. We used partial correlation analysis to evaluate the relationships between ASPECTS and outcomes. Multivariate logistic regression analysis was used to examine potential independent prognostic indicators. The receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the independent prognostic indicators in predicting outcomes. Results: Fifty-five AIS patients were included. The good outcome group had a lower baseline National Institutes of Health Stroke Scale (NIHSS; Z = -3.413, P < 0.001) and infarct core volume (ICV; Z = -3.114, P = 0.002) as well as higher cerebral blood flow (CBF)-ASPECTS (Z = -3.835, P < 0.001) and cerebral blood volume (CBV)-ASPECTS (Z = -4.099, P < 0.001). Higher CBF-ASPECTS (r = -0.459, P = 0.001), and CBV-ASPECTS (r = -0.502, P < 0.001) were associated with a lower 90-day mRS. The baseline NIHSS, CBF-ASPECTS, and CBV-ASPECTS were identified as independent prognostic indicators. The AUCs of the baseline NIHSS, CBF-ASPECTS, and CBV-ASPECTS were 83.3, 87.4, and 89.9%, respectively. Combining NIHSS with CBF-ASPECTS and CBV-ASPECTS, the AUC significantly improved to 96.3%. The combined three factors showed a significant difference compared to the baseline NIHSS (Z = 2.039, P = 0.041) and CBF-ASPECTS (Z = 2.099, P = 0.036), but no difference with CBV-ASPECTS (Z = 1.176, P = 0.239). Conclusions: The ASPECTS based on multi-PLD ASL is a valuable tool for identifying independent prognostic indicators and assessing clinical outcomes in AIS patients. The baseline NIHSS, combined with CBF-ASPECTS and CBV-ASPECTS, enhances the predictive efficacy of clinical outcomes in AIS patients. The CBV-ASPECTS alone can offer comparable predictive efficacy to the combination. [ABSTRACT FROM AUTHOR]

Background: Molecular alteration events are common in glioblastomas, the isocitrate dehydrogenase (IDH)-wild of which have had poor survival results so far. The progress of radiomics-based model provides novel sights for its preoperatively noninvasive prediction. Purpose: To develop a radiomics-based model for predicting epidermal growth factor receptor (EGFR) amplification status in IDH1-wild glioblastomas of adults by pretreatment diffusion tensor imaging (DTI). Material and Methods: A total of 124 patients with diagnosed glioblastomas were retrospectively collected. Six conventional magnetic resonance imaging (MRI) features of all the tumors were evaluated visually. Patients were divided into the training (n = 87) and the test set (n = 37) with a ratio of 7:3. Radiomics features were extracted from two regions of the glioblastomas, which were the total tumor (ROI_1) and the solid portion of tumor (ROI_2). The radiomics features extracted from the DTI and T1–contrast-enhanced (T1C) images were selected using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Logistic regression analysis was conducted to develop models for EGFR amplification prediction in the training set. Results: The radiomics model based on ROI_1 demonstrated favorable discrimination in both the training (area under the curve [AUC] = 0.86) and the test set (AUC = 0.82) (P < 0.05). Combining the radiomics features and the conventional feature tumor location, no significant improvement of AUCs was achieved (AUC = 0.86 and 0.81). Conclusion: The radiomics model derived from pretreatment DTI may have potential in differentiating the EGFR mutation status in glioblastomas. [ABSTRACT FROM AUTHOR]

Metastases in the brain are a devastating and common cancer with a poor prognosis. Physicians, on the other hand, may help their patients by suspecting, recognizing, and treating them correctly. It is predicted that between 8 and 10% of cancer patients may develop brain metastases, resulting in roughly 200,000 new cases of brain metastases per year. Single and multiple metastases can share clinical, diagnostic, and therapeutic features. In the majority of brain metastasis, cancer cells move through the blood to the brain. The cerebellum is responsible for 15% of instances; whereas, the brainstem is responsible for 5%. The severity and speed with which the symptoms appear might vary substantially. In addition to the history and physical examination, CT and MRI with contrast give a safe, more sensitive diagnosis. The treatment is primarily palliative, although vigorous therapy in carefully selected patients can prolong the median survival time to about a year. Generally, the prognosis is guarded. Medical treatment includes corticosteroids and antiepileptic. Whole-brain irradiation, SRS, and chemotherapeutic agents are the most common adjuvant therapies. The neurosurgical approach to the management of such lesions has been greatly improved over the last few decades. Hereby, an updated review for the management of multiple brain metastasis. [ABSTRACT FROM AUTHOR]

Purpose: SRS MapCHECK (SMC) is a commercially available patient‐specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off‐axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions. Methods: Volumetric‐modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off‐axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software. Results: Comparing on‐axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA‐calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off‐axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms. Conclusions: MF, dose calculation algorithm, and LDT selections are vital in VMAT‐based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on‐axis and 3.6 MU/cGy off‐axis. [ABSTRACT FROM AUTHOR]

Purpose: Glioma-associated epilepsy affects a significant proportion of glioma patients, contributing to disease progression and diminished survival rates. However, the lack of a reliable preoperative seizure predictor hampers effective surgical planning. This study investigates the potential of Alpha B crystallin protein (CRYAB) plasma levels as a predictive biomarker for epilepsy seizures in glioma patients. Methods: Plasma samples were obtained from 75 participants, including 21 glioma patients with pre-operative epilepsy, 14 glioma patients without pre-operative epilepsy, and 21 age- and sex-matched control subjects. Additionally, 11 idiopathic epilepsy patients and 8 intractable epilepsy patients served as positive disease control groups. The study utilized ELISA to accurately quantify the circulating levels of CRYAB in the plasma samples of all participants. Results: The analysis revealed a significant reduction in plasma CRYAB levels in glioma patients with pre-operative epilepsy and idiopathic epilepsy. The receiver operating characteristic (ROC) curve analysis displayed an impressive performance, indicating an AUC of 0.863 (95% CI, 0.810–0.916) across the entire patient cohort. Furthermore, plasma CRYAB levels exhibited a robust diagnostic capability, with an AUC of 0.9135, a sensitivity of 100.0%, and a specificity of 73.68%, effectively distinguishing glioma patients with preoperative epilepsy from those without epilepsy. The Decision Curve Analysis (DCA) underscored the clinical relevance of plasma CRYAB levels in predicting pre-operative epilepsy in glioma. Conclusion: The findings imply that the reduced levels of CRYAB may assist in prediction of seizure occurrence in glioma patients, although future large-scale prospective studies are warranted. [ABSTRACT FROM AUTHOR]

Bevacizumab is a common strategy for the treatment of recurrent glioblastoma. Survival status is a crucial issue for patients with recurrent glioblastoma, and the apparent diffusion coefficient (ADC) values of the lower Gaussian curve have been reported to have the potential to predict prognosis in recurrent glioblastoma. In the present study, we aimed to clarify the survival prediction of ADC values in patients with recurrent glioblastoma receiving bevacizumab treatment through a systematic review and meta-analysis of randomized clinical trials, comparing ADC values higher than the cut-off values with those lower than the cut-off values to determine which type of ADC values can be associated with significant survival benefits. Different survival indicators were analyzed, including overall survival (OS) and progression-free survival (PFS). Ten studies with a total of 782 patients with recurrent glioblastoma were included. The focused outcomes were OS and PFS. Our results showed that ADC values lower than the cut-off values were associated with significant benefits for OS status compared with ADC values higher than the cut-off values. Similar significant benefits were observed for PFS. The meta-analysis results suggest that ADC values lower than the cut-off values might be associated with significant benefits for OS and PFS when compared with ADC values higher than the cut-off values. However, bias in relation to the different stages of recurrent glioblastoma and different types, doses, and regimens of bevacizumab should not be ignored. [ABSTRACT FROM AUTHOR]

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Background and Aim: Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. Methods: In this cross‐sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10−6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Results: Sixty‐eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two‐by‐two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p‐value = 0.87) and glioblastoma and medulloblastoma (p‐value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two‐by‐two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p‐value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. Conclusion: The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging is increasingly available for brain investigation. Image interpretation of intracranial space-occupying lesions often includes the derived apparent diffusion coefficient (ADC) analysis. In human medicine, ADC can help discriminate between benign and malignant lesions in intracranial tumors. This study investigates the difference in ADC values depending on the sample strategies of image analysis. MRI examination, including diffusionweighted images of canine and feline patients presented between 2015 and 2020, were reviewed retrospectively. Patients with single, large intracranial space-occupying lesions were included. Lesions homogeneity was subjectively scored. ADC values were calculated using six different methods of sampling (M1-M6) on the ADC map. M1 included as much as possible of the lesion on a maximum of five consecutive slices; M2 included five central and five peripheral ROIs; M3 included a single ROI on the solid part of the lesion; M4 included three central ROIs on one slice; M5 included three central ROIs on different slices; and M6 included one large ROI on the entire lesion. A total of 201 animals of various breeds, genders, and ages were analyzed. ADC values differed significantly between M5 against M2 (peripheral) (p < 0.001), M5 against M6 (p = 0.009), and M4 against M2 (peripheral) (p = 0.005). When lesions scored as homogeneous in all sequences were excluded, an additional significant difference in three further sampling methods was present (p < 0.005). ADC of single, large, intracranial space-occupying lesions differed significantly in half of the tested methods of sampling. Excluding homogeneous lesions, additional significant differences among the sampling methods were present. The obtained results should increase awareness of the variability of the ADC, depending on the sample strategies used. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging is increasingly available for brain investigation. Image interpretation of intracranial space-occupying lesions often includes the derived apparent diffusion coefficient (ADC) analysis. In human medicine, ADC can help discriminate between benign and malignant lesions in intracranial tumors. This study investigates the difference in ADC values depending on the sample strategies of image analysis. MRI examination, including diffusionweighted images of canine and feline patients presented between 2015 and 2020, were reviewed retrospectively. Patients with single, large intracranial space-occupying lesions were included. Lesions homogeneity was subjectively scored. ADC values were calculated using six different methods of sampling (M1--M6) on the ADC map. M1 included as much as possible of the lesion on a maximum of five consecutive slices; M2 included five central and five peripheral ROIs; M3 included a single ROI on the solid part of the lesion; M4 included three central ROIs on one slice; M5 included three central ROIs on different slices; and M6 included one large ROI on the entire lesion. A total of 201 animals of various breeds, genders, and ages were analyzed. ADC values differed significantly between M5 against M2 (peripheral) (p < 0.001), M5 against M6 (p = 0.009), and M4 against M2 (peripheral) (p = 0.005). When lesions scored as homogeneous in all sequences were excluded, an additional significant difference in three further sampling methods was present (p < 0.005). ADC of single, large, intracranial space-occupying lesions differed significantly in half of the tested methods of sampling. Excluding homogeneous lesions, additional significant differences among the sampling methods were present. The obtained results should increase awareness of the variability of the ADC, depending on the sample strategies used. [ABSTRACT FROM AUTHOR]

Glioma is amalignant tumor of the central nervous system (CNS). Currently, effective treatment options for gliomas are still lacking. Neutrophils, as an important member of the tumor microenvironment (TME), are widely distributed in circulation. Recently, the discovery of cranial-meningeal channels and intracranial lymphatic vessels has provided new insights into the origins of neutrophils in the CNS. Neutrophils in the brain may originate more from the skull and adjacent vertebral bone marrow. They cross the blood-brain barrier (BBB) under the action of chemokines and enter the brain parenchyma, subsequently migrating to the glioma TME and undergoing phenotypic changes upon contact with tumor cells. Under glycolytic metabolism model, neutrophils show complex and dual functions in different stages of cancer progression, including participation in the malignant progression, immune suppression, and anti-tumor effects of gliomas. Additionally, neutrophils in the TME interact with other immune cells, playing a crucial role in cancer immunotherapy. Targeting neutrophils may be a novel generation of immunotherapy and improve the efficacy of cancer treatments. This article reviews the molecular mechanisms of neutrophils infiltrating the central nervous system from the external environment, detailing the origin, functions, classifications, and targeted therapies of neutrophils in the context of glioma. [ABSTRACT FROM AUTHOR]

Background: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2–4 gliomas. Methods: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. Results: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. Conclusions: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas. [ABSTRACT FROM AUTHOR]

Objective: Glioblastomas are highly vascularized malignant tumors. We determined the efficacy and safety of the anti-angiogenic multi-kinase inhibitor, anlotinib, for a newly diagnosed glioblastoma. Methods: This multicenter, single-arm trial (NCT04119674) enrolled 33 treatment-naïve patients with histologically proven glioblastomas between March 2019 and November 2020. Patients underwent treatment with the standard STUPP regimen [fractionated focal irradiation in daily fractions of 1.8-2 Gy given 5 d/w × 6 w (total = 54-60 Gy)] or radiotherapy plus continuous daily temozolomide (TMZ) (75 mg/m2 of body surface area/d, 7 d/w from the first to the last day of radiotherapy), followed by 6 cycles of adjuvant TMZ (150-200 mg/m2 × 5 d during each 28-d cycle) plus anlotinib (8 mg/d on d 1-14 of each 3-w cycle for 2 cycles during concomitant chemoradiotherapy, 8 maximal cycles as adjuvant therapy, followed by maintenance at 8 mg/d. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS) and adverse events (AEs). Results: Thirty-three patients received the planned treatment. The median PFS was 10.9 months (95% CI, 9.9-18.7 months) and the 12-month PFS rate was 48.5%. The median OS was 17.4 months (95% CI, 14.5-21.1 months) and the 12-month OS rate was 81.8%. The most common AEs included hypertriglyceridemia [58% (n = 19)], hypoalbuminemia [46% (n = 15)], and hypercholesterolemia [46% (n = 15)] during concurrent chemoradiotherapy and leukopenia [73% (n = 24)], hypertriglyceridemia [67% (n = 22)], and neutropenia [52% (n = 17)] during adjuvant therapy. Five patients discontinued treatment due to AEs. HEG1 (HR, 5.6; 95% CI, 1.3-23.7; P = 0.021) and RP1L1 alterations (HR, 11.1; 95% CI, 2.2-57.2; P = 0.004) were associated with a significantly shorter PFS. Conclusions: Anlotinib plus the STUPP regimen has promising anti-tumor activity against glioblastoma and manageable toxicity. HEG1 and RP1L1 alterations might be novel predictive biomarkers of the response to anlotinib. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma. Materials and methods: In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted. Results: CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models. Conclusion: APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma. Clinical relevance statement: Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies. Key Points: • This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a fatal brain tumour that is traditionally diagnosed based on histological features. Recent molecular profiling studies have reshaped the World Health Organization approach in the classification of central nervous system tumours to include more pathogenetic hallmarks. These studies have revealed that multiple oncogenic pathways are dysregulated, which contributes to the aggressiveness and resistance of GBM. Such findings have shed light on the molecular vulnerability of GBM and have shifted the disease management paradigm from chemotherapy to targeted therapies. Targeted drugs have been developed to inhibit oncogenic targets in GBM, including receptors involved in the angiogenic axis, the signal transducer and activator of transcription 3 (STAT3), the PI3K/AKT/mTOR signalling pathway, the ubiquitination-proteasome pathway, as well as IDH1/2 pathway. While certain targeted drugs showed promising results in vivo, the translatability of such preclinical achievements in GBM remains a barrier. We also discuss the recent developments and clinical assessments of targeted drugs, as well as the prospects of cell-based therapies and combinatorial therapy as novel ways to target GBM. Targeted treatments have demonstrated preclinical efficacy over chemotherapy as an alternative or adjuvant to the current standard of care for GBM, but their clinical efficacy remains hindered by challenges such as blood-brain barrier penetrance of the drugs. The development of combinatorial targeted therapies is expected to improve therapeutic efficacy and overcome drug resistance. [ABSTRACT FROM AUTHOR]

Objectives: Our study aims to determine the incidence and potential risk factors for cerebral radiation necrosis (CRN) following treatment of sinonasal malignancies. Methods: One hundred thirty‐two patients diagnosed with sinonasal malignancies over an 18‐year period were identified at two institutions. Forty‐six patients meeting inclusion criteria and treated with radiation therapy were included for analysis. Demographic and clinical‐pathologic characteristics were collected and reviewed. Post‐treatment magnetic resonance imaging (MRI) at least 1 year following treatment was reviewed to determine presence or absence of CRN. Results: CRN was identified on MRI in 8 of 46 patients (17.4%) following radiation treatment. Patients with a history of reirradiation were more likely to develop CRN (50% vs. 10.5%, p <.05). The BEDs of radiation were also higher in CRN patients compared to non‐CRN patients, but this difference was not significant (p >.05). CRN patients had a higher proportion of tumors with skull base involvement than non‐CRN patients (100% vs. 57.9%, p =.037). Demographics, comorbidities, pathology, primary tumor subsite, chemotherapy use, and stage of disease demonstrated no significant increase in risk of CRN. Conclusions: Reirradiation and tumor skull base involvement were significant risk factors associated with CRN. Higher average total prescribed and BEDs of radiation were seen in the CRN groups, but these differences were not statistically significant. Gender, comorbidities, tumor subsite, tumor location, and treatment type were not significantly different between groups. Level of evidence: Level 3. [ABSTRACT FROM AUTHOR]

Objectives: Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-l-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping. Methods: Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80–100% and 60–75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann–Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations. Results: Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p <.05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance — most pronounced for FA60 (AUC = 0.86, p <.001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p <.001) and was negatively correlated with amino acid uptake (rs = − 0.46, p <.001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p <.001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p =.04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting. Conclusion: Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone. Clinical relevance statement: A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings. Key Points: • Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone. [ABSTRACT FROM AUTHOR]

Since the overall glioma mass and its subcomponents-enhancing region (malignant part of the tumor), non-enhancing (less aggressive tumor cells), necrotic core (dead cells), and edema (water deposition)-are complex and irregular structures, non-Euclidean geometric measures such as fractal dimension (FD or "fractality") and lacunarity are needed to quantify their structural complexity. Fractality measures the extent of structural irregularity, while lacunarity measures the spatial distribution or gaps. The complex geometric patterns of the glioma subcomponents may be closely associated with the grade and molecular landscape. Therefore, we measured FD and lacunarity in the glioma subcomponents and developed machine learning models to discriminate between tumor grades and isocitrate dehydrogenase (IDH) gene status. 3D fractal dimension (FD3D) and lacunarity (Lac3D) were measured for the enhancing, non-enhancing plus necrotic core, and edema-subcomponents using preoperative structural-MRI obtained from the The Cancer Genome Atlas (TCGA) and University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) glioma cohorts. The FD3D and Lac3D measures of the tumor-subcomponents were then compared across glioma grades (HGGs: high-grade gliomas vs. LGGs: low-grade gliomas) and IDH status (mutant vs. wild type). Using these measures, machine learning platforms discriminative of glioma grade and IDH status were developed. Kaplan-Meier survival analysis was used to assess the prognostic significance of FD3D and Lac3D measurements. HGG exhibited significantly higher fractality and lower lacunarity in the enhancing subcomponent, along with lower fractality in the non-enhancing subcomponent compared to LGG. This suggests that a highly irregular and complex geometry in the enhancing-subcomponent is a characteristic feature of HGGs. A comparison of FD3D and Lac3D between IDH-wild type and IDH-mutant gliomas revealed that mutant gliomas had ~2.5-fold lower FD3D in the enhancing-subcomponent and higher FD3D with lower Lac3D in the non-enhancing subcomponent, indicating a less complex and smooth enhancing subcomponent, and a more continuous non-enhancing subcomponent as features of IDH-mutant gliomas. Supervised ML models using FD3D from both the enhancing and non-enhancing subcomponents together demonstrated high-sensitivity in discriminating glioma grades (~97.9%) and IDH status (~94.4%). A combined fractal estimation of the enhancing and non-enhancing subcomponents using MR images could serve as a non-invasive, precise, and quantitative measure for discriminating glioma grade and IDH status. The combination of 2-hydroxyglutarate-magnetic resonance spectroscopy (2HG-MRS) with FD3D and Lac3D quantification may be established as a robust imaging signature for glioma subtyping., (© 2024 John Wiley & Sons Ltd.)

Background: Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management., Methods: A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort. All patients received contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans at 3.0 T before treatment. Radiomics features were calculated from BM and brain-to-tumor interface (BTI) region in the MRI image and screened using least absolute shrinkage and selection operator (LASSO) to construct the radiomics signature (RS). Volume of peritumor edema (VPE) was calculated and combined with RS to create a joint model. Performance of the models was assessed by receiver operating characteristic (ROC)., Results: The BTI-based RS showed better performance compared to BM-based RS. The combined models integrating BTI features and VPE can improve identification performance in AUCs in the training (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918), internal validation (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.717 vs. 0.854 vs. 0.840), and external test (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.744 vs. 0.839 vs. 0.800) cohorts., Conclusion: This study indicated that BTI-based radiomics features and VPE are associated with the metastatic tumor types of BM., Competing Interests: Declarations. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Ethical approval: This retrospective study was approved by the Institutional Review Board of the Cancer Hospital of China Medical University, Shengjing Hospital of China Medical University, and the People's Hospital of Liaoning Province, and the consents from patients were waived., (© 2024. Italian Society of Medical Radiology.)

Background and Objectives: Photodynamic therapy (PDT) with talaporfin sodium is an intraoperative local therapy administered after the surgical removal of malignant gliomas. However, its clinical efficacy in a large patient population has not been determined. To analyze the clinical outcomes and prognosis in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients treated with PDT., Methods: This retrospective study included patients with newly diagnosed IDH-wildtype glioblastoma treated at Kobe University Hospital between January 2013 and December 2022. PDT involves irradiation of the resection cavity with a 664-nm semiconductor laser after an intravenous infusion of talaporfin sodium. The main outcome measures were the recurrence patterns and survival times, which were compared between the PDT and non-PDT groups. Univariate and multivariate analyses were used to determine the prognostic factors. In addition, adverse events and prognostic factors in the PDT group were analyzed., Results: A total of 44 and 56 patients were included in the PDT and non-PDT groups, respectively. The local recurrence rate was significantly lower in the PDT group than in the non-PDT group (51.3% vs 83.9%), whereas the distant recurrence and dissemination rates were significantly higher in the PDT group than in the non-PDT group (48.7% vs 16.1%). Two grade 3 adverse events were observed in the PDT group. The median progression-free survival and overall survival times were significantly longer in the PDT group than in the non-PDT group (progression-free survival: 10.8 vs 9.3 months, respectively, and overall survival: 24.6 vs 17.6 months, respectively). Multivariate analysis of the PDT groups revealed that younger age was an independent prognostic factor., Conclusion: PDT with talaporfin sodium provided effective local control with minimal adverse effects. The survival time of the patients treated with PDT was significantly longer than that of the patients who did not receive PDT. Therefore, a randomized controlled clinical trial on PDT is warranted., (Copyright © Congress of Neurological Surgeons 2024. All rights reserved.)