Abstract The Central Brain Tumor Registry of the United States (CBTRUS) is the largest population-based site-specific registry in the world and is focused exclusively on primary brain and other central nervous system (CNS) tumors diagnosed in the United States (US). CBTRUS obtained de-identified incidence data from 48 central cancer registries (CCRs) from the Centers for Disease Control and Prevention’s National Program of Cancer Registries and 4 CCRs from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program. CBTRUS represents the entire US population and is inclusive of cases of all primary malignant and non-malignant brain and other CNS tumors for 50 state registries, plus the District of Columbia and Puerto Rico. Additional survival data from 21 CCRs were obtained from SEER. From 2019–2023, 515,112 tumors were reported, corresponding to an average annual age-adjusted incidence rate (AAAIR) of 27.02 per 100,000 population. Across the 50 states, AAAIR for all primary brain and other CNS tumors varied. Meningioma was the most frequently diagnosed tumor type (43.4%). Females had a higher overall incidence of primary brain and other CNS tumors than males, whereas males had higher incidence of many glioma subtypes, including glioblastoma, IDH-wildtype. Incidence varied by race/Hispanic ethnicity, sex, age, and tumor type. For children 0-14 years of age at diagnosis, primary brain and other CNS tumors were the leading cause of cancer-related mortality which is disproportionate to the AAAIR of these tumors in this age group. For most tumor sites, five-year relative survival generally decreased with older age at diagnosis, with children, adolescents, and young adults generally having better survival outcomes. This report provides a current, comprehensive source for the descriptive epidemiology of all primary brain and other CNS tumors in the United States.
Diffuse gliomas arise within and become functionally integrated into neural circuits, establishing bidirectional interactions between malignant cells and the surrounding brain. Neuronal activity promotes glioma growth and progression, while glioma cells remodel the excitability, connectivity, and functional organization of the neural circuits they infiltrate. Pathological excitation-inhibition imbalance can impair cortical information processing, while tumor infiltration simultaneously disrupts, preserves, and induces adaptive reorganization of distributed functional networks. Language processing provides a well defined framework for examining these competing effects because language processing depends on distributed cortical and subcortical networks whose organization can be interrogated using neuroimaging, electrophysiology, and direct cortical stimulation. The pattern and severity of glioma-associated language dysfunction reflect the interaction among tumor location, growth kinetics, molecular and histopathological features, white matter involvement, and the capacity for adaptive redistribution of function within ipsilateral and contralateral networks. Slowly growing gliomas can permit substantial functional reorganization, whereas rapidly infiltrating tumors may constrain the time and biological substrate available for effective network adaptation. In this review, we examine reciprocal glioma-neural circuit interactions, with particular emphasis on the mechanisms of glioma-associated neuronal hyperexcitability and the disruption, preservation, and adaptive remodeling of language networks.
Abstract Background Contemporary risk stratification of isocitrate dehydrogenase (IDH)–mutant lower-grade gliomas relies primarily on molecular characteristics and surgical tumor burden but does not capture dynamic tumor behavior. Our primary objective was to determine whether pretherapeutic tumor growth velocity (TGV) provides independent prognostic information for overall survival (OS) beyond established factors. Methods This multicenter retrospective study included 569 adults with grade 2–3 IDH-mutant gliomas from three French centers, comprising a development cohort (n = 440) and two external validation cohorts (n = 129). TGV was derived from automated, outcome-blinded volumetric analysis of serial preoperative MRI using mean diameter expansion and log-volume growth. Prognostic value was assessed using multivariable Cox regression, external validation, decision curve analysis, and exploratory random survival forests. Results During a median follow-up of 6.8 years, 73 deaths occurred in the development cohort. Higher TGV was independently associated with shorter overall survival after adjustment for age, sex, grade, 1p/19q status, and postoperative residual tumor volume (HR per 1-mm/year increase, 1.046; 95%CI, 1.011–1.083; P=.011), and was independently replicated externally (HR, 1.077; 95% CI, 1.005–1.153; P=.035). Findings were consistent using log-volume growth and after accounting for CDKN2A/B status. Adding TGV improved prognostic model fit (likelihood-ratio χ²=5.70; P=.017), with greatest incremental value in astrocytomas. Decision curve analysis demonstrated additional clinical utility, while survival forests identified TGV among the strongest survival predictors alongside 1p/19q status and postoperative residual tumor volume. Conclusions Pretherapeutic TGV independently predicts survival beyond contemporary molecular and surgical factors in IDH-mutant lower-grade gliomas. This accessible MRI-derived biomarker may refine individualized risk stratification, particularly in astrocytoma.
Abstract Background Maximal safe glioma resection requires distinguishing infiltrative tumor from surrounding brain. Visible 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX) fluorescence guides surgery, but low or heterogeneous PpIX accumulation and optical attenuation limit sensitivity. We tested whether combined fluorescence spectroscopy (FS) and diffuse reflectance spectroscopy (DRS) could improve tumor-tissue discrimination beyond visible fluorescence. Methods Thirty-three patients with historically classified low-grade (LGG), high-grade (HGG), or recurrent glioma underwent 5-ALA-guided surgery. A handheld bimodal probe acquired in vivo FS and DRS from biopsy-paired and navigation-defined presumed non-tumor sites. A light-transport model estimated optical properties, corrected fluorescence, and quantified biomarkers. Five predictor representations were evaluated using supervised machine learning for non-tumor versus tumor discrimination and sequential LGG versus HGG classification with patient-grouped leave-one-patient-out cross-validation. Patient-clustered bootstrapping compared FS+DRS with visible fluorescence. Results Across 212 sites, FS+DRS achieved 85.82% sensitivity, 74.36% specificity, 80.09% balanced accuracy, and 0.81 AUROC for tumor discrimination. FS and DRS alone achieved 73.16% and 66.17% balanced accuracy, respectively. Compared with visible fluorescence, FS+DRS increased sensitivity by 34.3 percentage points (95% CI, 19.3–47.5; p < 0.001), while specificity differed by − 11.5 points (95% CI, −27.8–4.0; p = 0.2). FS+DRS detected 55/65 nonfluorescent tumor sites, including 19/23 historical LGG sites. For grade discrimination, biomarkers achieved 71.98% balanced accuracy and 0.71 AUROC. Conclusion Combined FS+DRS improved tumor detection and identified histology-positive tumor not visible with standard fluorescence. Prospective multicenter evaluation should use a locked model, molecularly classified cohorts, prespecified clinical endpoints, and spatial registration to determine whether the added signal improves intraoperative decisions and patient outcomes.
