Citations

Importance Primary brain and central nervous system cancer (collectively referred to as CNS cancer) comprises 2% of all human cancers and poses significant health and economic challenges in the United States. Objective To analyze CNS cancer burden in the US, stratified by time, location (state and division), sex, age group, and Sociodemographic Index (SDI). Design, Setting, and Participants This cross-sectional study involved a repeated analysis of Global Burden of Disease Study (GBD) 2021 data in 2024. Using data from 183 sources, CNS cancer metrics in the US were estimated across states and years. US CNS cancer metrics across all sexes and age groups were included in the GBD. Exposure CNS cancer diagnosis. Main Outcomes and Measures Overall and age-standardized estimates of the incidence, prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability per 100 000 population, including 95% uncertainty intervals (UIs), and time trends. Results In 2021, for all age groups and sexes across the US, there were 31 780 incident cases (95% UI, 29971.1 to 32843.9). Age-standardized incidence, DALYs, and mortality rates per 100 000 population were 6.91 (95% UI, 6.58 to 7.12), 134.38 (95% UI, 129.83 to 137.95), and 4.1 (95% UI, 3.87 to 4.22), respectively. Despite no significant change observed in the overall incidence between 1990 and 2021, DALY and mortality rates decreased by 15.77% (95% UI, −17.75% to −13.68%) and 8.41% (95% UI, −11.09% to −6.22%), respectively. Substantial geographic variability was noted. Mississippi, Alabama, Kentucky, and Kansas (West North Central and East South Central divisions) and West Virginia faced persistently high burdens over the past 30 years. Sex differences were evident; disease burden was consistently higher in males compared with females. Age-specific estimates showed a bimodal distribution: the youngest group (<5 years) showed a significant decrease in incidence rate (−34.42% to −11.56%), whereas older age groups (>70 years) experienced increasing trends. DALYs and mortality rates were negatively correlated with SDI (ρ = −0.6860 and ρ = −0.6391; P < .001). Conclusions and Relevance These findings provide valuable insights into the CNS cancer burden across the US by age, sex, location, and SDI, enabling better public health status assessments, health care policy restructuring, and resource redistribution for improved care.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control.

Purpose of review The rapid growth and integration of artificial intelligence in healthcare has the potential to revolutionize all fields of medicine, including anesthesiology. This review summarizes the role of artificial intelligence in anesthesiology, highlighting how it can enhance patient safety, workflow efficiency, and research capabilities, while also examining the potential risks and ethical issues of introducing this new technology. Recent findings Evolving applications in anesthesiology include closed-loop anesthetic delivery, risk stratification, intraoperative monitoring, natural language processing for literature synthesis, and predictive modeling for adverse events. While these tools offer significant promise for improving efficiency and safety, emerging risks, such as algorithmic bias, lack of transparency, hallucinations, and automation complacency – must be carefully addressed. Regulatory frameworks, clinician education, and transparent model development are necessary to ensure responsible integration. Summary Artificial intelligence is poised to transform anesthetic care, but it must be developed and deployed with caution. Clinician oversight, robust validation, and ethical safeguards are essential to ensure artificial intelligence enhances, rather than replaces, clinical judgment. Strategic adoption can improve patient outcomes, reduce preventable harm, and streamline perioperative workflows.

Extracorporeal membrane oxygenation (ECMO) use during liver transplantation is a complex and evolving aspect of critical care. This retrospective analysis presents the largest North American case series to date, offering a detailed comparison of survival outcomes in liver transplant (LT) patients supported by ECMO. A total of 15 patients were supported with ECMO, eight were supported on venoarterial (VA) ECMO and the remaining seven on venovenous (VV) ECMO. The median duration of ECMO support was 10 days. Mean intensive care unit (ICU) and hospital stays were 34.8 (interquartile range [IQR]: 1–158) and 49.2 days (IQR: 1–121), respectively. Overall survival was 33.3%, comparable to published rates (20–30%). Survival was based on ECMO initiation timing: preoperative 33.3% (1/3); intraoperative 50% (3/6); postoperative, 16.6% (1/6). All the patients (five) discharged from hospital were alive at 1 year follow-up. Significant ECMO-related complications included acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT) (40%, n = 6/15), neurological complications (40%, n = 6/15), and bleeding requiring transfusion (26.6%, n = 4/15). Literature review suggests better outcomes with intraoperative or postoperative ECMO. While ECMO use is increasing in LT, outcomes remain poor. Improved survival may depend on careful patient selection, optimal timing of initiation, and distinguishing reversible pathology from irreversible one.

Background/Objectives: Erythromelalgia is a rare condition characterized by burning pain, redness, and warmth primarily in the extremities, usually worsened by heat and alleviated by cold. The objective of this study was to identify the top 10 priorities in pediatric erythromelalgia from multiple perspectives, including clinicians, people with lived experience of childhood-onset erythromelalgia, and their family members. Methods: A modified James Lind Alliance Priority-Setting Process was conducted. The top priorities were identified through four phases: (1) an international online survey to gather priorities, (2) data processing, (3) an interim prioritization online survey, and (4) a virtual workshop to set the final priorities. Results: In phase 1, 185 potential priorities were submitted by 74 respondents (53% patients, 24% family members, and 23% clinicians) that were developed into 68 unique research questions (phase 2). In phase 3, of the 68 questions, 50 were rated for importance by 58 participants (38% patients, 36% family members, and 26% clinicians), reducing the list to 25 questions. In phase 4, the top 10 was reached through consensus by 12 participants (33% patients, 25% family members, and 42% clinicians) across Canada, South Africa, the United States of America, and the United Kingdom. Conclusions: The final priorities focused on the treatment of erythromelalgia, understanding underlying mechanisms, the association of erythromelalgia with various body systems, and generating awareness. This list is the first international patient-centered research agenda for childhood-onset erythromelalgia and a call to action from key partners to improve future research and care.

Galactic cosmic ray (GCR) radiation is a major barrier to human space exploration beyond Earth’s magnetic field protection. Mesenchymal stem cells (MSCs) are found in all organs and play a critical role in repair and regeneration of tissue. We engineered bone marrow-derived MSCs and evaluated their response to ionizing radiation exposure. Epidermal growth factor receptor (EGFR) expression by certain types of cancers has been shown to induce radioresistance. In this study, we tested the feasibility of transfecting MSCs to overexpress EGFR (eMSC-EGFR) and their capacity to tolerate and recover from X-ray exposure. Quantitative real-time PCR (qRT-PCR) and immunoblotting results confirmed the efficient transfection of EGFR into MSCs and EGFR protein production. eMSC-EGFR maintained characteristics of human MSCs as outlined by the International Society for Cell & Gene Therapy. Then, engineered MSCs were exposed to various dose rates of X-ray (1–20 Gy) to assess the potential radioprotective role of EGFR overexpression in MSCs. Post-irradiation analysis included evaluation of morphology, cell proliferation, viability, tumorigenic potential, and DNA damage. eMSC-EGFR showed signs of radioresistance compared to naïve MSCs when assessing relative proliferation one week following exposure to 1–8 Gy X-rays, and significantly lower DNA damage content 24 h after exposure to 4 Gy. We establish for the first time the efficient generation of EGFR overexpressing MSCs as a model for enhancing the human body to tolerate and recover from moderate dose radiation injury in long-term manned space travel.