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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: Pulmonary hypertension (PH) is a life-threatening disease affecting up to 1% of the global population. Diagnosis can be challenging and is often delayed due to the need for advanced imaging and invasive procedures. The use of artificial intelligence applied to ECGs (ECG-AI) has been shown to detect subtle patterns in voltage-time data and may be a valuable tool for the early detection of PH. Hypothesis and Purpose: To evaluate the performance of a previously trained, ECG-AI algorithm to detect PH (ECG-AI PH) using real-world data (RWD) collected in a multicenter, validation study. A joint primary hypothesis required sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) to exceed the null values of 76%, 70%, 10% and 90%. Study Design and Methods: This retrospective validation study was conducted at 5 geographically diverse U.S. health systems. Adult subjects were eligible for inclusion if they had a 12-lead ECG paired with an echocardiogram (Echo) in which tricuspid regurgitation velocity (TRV) was recorded, following presentation with dyspnea. Patients were classified according to echocardiographic criteria as either PH (PH+, TRV >3.4 m/s) or controls (PH-, TRV ≤2.8 m/s) to simulate the real-world use of ECG-AI, where a positive result could lead to a follow-up Echo. The study database was locked before processing the digital ECGs with ECG-AI PH. Performance was also estimated in a subset of subjects that later had a right heart catheterization using mPAP ≥ 20mmHg as the definition of PH. Results: A total of 14281 subjects (53% female, 63% aged 50+ years) met the inclusion criteria, including 3019 PH+ cases and 11262 PH- controls (Figure). Sn and Sp were 84.0% (95% CI: 82.6%, 85.3%) and 71.7% (95% CI: 70.9%, 72.6%), respectively. The positive and negative predictive values were 18.9% (95% CI: 18.4%, 19.4%) and 98.3% (95% CI: 98.1%, 98.4%), respectively, at 7.3% prevalence. Each endpoint met pre-defined performance criteria. In the subset of 1683 subjects with a follow up RHC, performance remained robust (Sn 85% (1155/1358); Sp 46% (151/325)). Conclusion: While ECG-AI PH was first developed as an investigational tool to detect PH, continued development as software as a medical device for clinical use demonstrated that the algorithm retained strong performance to detect PH in diverse, non-overlapping clinical settings and patient populations.

Abstract Purpose: AXL, a receptor tyrosine kinase related to oncogenic processes, is aberrantly expressed in various cancers and associated with treatment resistance. Enapotamab vedotin (EnaV), a novel anti-AXL human IgG1 and monomethyl auristatin E antibody–drug conjugate, demonstrated antitumor activity in preclinical models, including non–small cell lung cancer (NSCLC). This phase 1/2 study assessed the safety and preliminary efficacy of EnaV in solid tumors. Patients and Methods: This study comprised dose-escalation and dose-expansion phases; both phases investigated EnaV once every 3 weeks (Q3W) and EnaV on days 1, 8, and 15 of a 28-day cycle (3Q4W). Primary objectives determined the maximum tolerated dose (dose escalation) and safety (dose expansion). Pharmacokinetic profile, antitumor activity, and AXL expression were also assessed. Results: During dose escalation, 32 patients received EnaV Q3W; 15 received EnaV 3Q4W. The maximum tolerated dose and recommended phase 2 dose were 2.2 mg/kg in Q3W and 1.0 mg/kg in 3Q4W schedules. In dose expansion, 189 patients received EnaV Q3W; 70 received EnaV 3Q4W. Common adverse events in dose expansion included fatigue, constipation, nausea, decreased appetite, and diarrhea. Overall response rates ranged from 4.5% to 12.5% with Q3W dose schedule and from 9.1% to 11.5% with 3Q4W dose schedule. Disease control rates for NSCLC cohorts were 40.9% to 50.0%. NSCLC subset analysis demonstrated correlation between radiomics signature and disease control. The relationship between clinical activity and AXL expression was not apparent. Conclusions: EnaV had an acceptable safety profile; however, because the evaluation of antitumor activity did not show clinically meaningful responses, clinical development of EnaV was discontinued. Significance: EnaV, an anti-AXL human IgG1 and monomethyl auristatin E antibody–drug conjugate, showed single-agent antitumor activity in preclinical models. This phase 1/2 study of EnaV demonstrated a manageable safety profile and antitumor activity in selected tumor types. Further studies exploring alternative targeting modalities, patient selection, and/or combinations are needed.

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

Abstract Purpose: Polatuzumab vedotin (polatuzumab) was approved for up-front treatment of diffuse large B-cell lymphoma in combination with chemoimmunotherapy (Pola-R-CHP) based on the POLARIX trial. However, when stratified by cell of origin (COO), polatuzumab seems to have greater efficacy in activated B-cell than germinal center B-cell (GCB) subtype disease. Most studies of polatuzumab used RNA expression to assess COO, whereas in routine clinical practice, the immunohistochemistry-based Hans algorithm is used. Experimental Design: To assess the impact of COO by immunohistochemistry on polatuzumab efficacy, we conducted a multicenter real-world study of adults with large B-cell lymphoma (LBCL) receiving polatuzumab from 2015 to 2024, split by receipt of polatuzumab in first-line versus relapsed/refractory settings. The primary endpoint was overall response rate (ORR) to polatuzumab-based treatment in GCB versus non-GCB relapsed/refractory LBCL. Results: Of 740 patients, 305 received polatuzumab in the first-line setting and 435 in the relapsed/refractory setting. In the relapsed/refractory cohort, the ORR in non-GCB versus GCB LBCL was 59.7% versus 36.3% [OR, 2.6; 95% confidence interval (CI), 1.77–3.84; P < 0.0001], with a complete response rate of 35.7% versus 17.7% (OR, 2.6; 95% CI, 1.66–4.02; P < 0.0001). Progression-free survival was longer for patients with non-GCB versus GCB COO (HR, 0.64; 95% CI, 0.5–0.83; P = 0.0006). In the first-line cohort, ORR, complete response rate, and progression-free survival were similar in non-GCB versus GCB LBCL, as hypothesized based on outcomes in the Pola-R-CHP arm of POLARIX, suggesting that the addition of polatuzumab overcomes the adverse risk of non-GCB COO in patients receiving R-CHOP. Conclusions: Based on these data, COO classification by the Hans algorithm is a strong predictor of polatuzumab efficacy in LBCL, informing real-world treatment decisions.

ABSTRACT Suicidality contributes substantially to global morbidity and mortality, yet despite its heritability, its biological etiology remains largely elusive. We conducted multi-ancestry genome-wide association study meta-analyses of suicidal ideation (259,747 cases), suicide attempt (64,993 cases), suicide death (9,197 cases), and suicidal behavior (suicide attempt/ death, 75,300 cases), across 54 cohorts (e.g., the Psychiatric Genomics Consortium, Million Veteran Program, UK Biobank). We identified 77 significant loci across meta-analyses, including 59 previously unreported for suicidality. SNP-based heritability ranged from 2.0-6.7% and there were strong, yet incomplete, genetic correlations between suicidality phenotypes (0.70-0.88). Fine-mapping prioritized putative causal SNPs and 20 credible genes. Enrichment analyses implicated synaptic pathways and neuronal populations predominantly in subcortical brain regions (e.g., amygdala excitatory, medium spiny, hippocampal CA1-3). Together, these findings establish suicidality as a polygenic set of traits with both shared and distinct genetic influences, providing a foundation for future studies of suicide biology and etiology.

Abstract The sections in this article are: Vascular Anatomy and Fluid Phase Conduit Vessels to Bone Capillaries of Bone Fluid Transport Physiological Implications Bone Tissue Turnover Bone Remodeling Cells of Bone Mineral Phase Bone Repair Transport of Ions and Molecules in Bone Movement Into Bone From Bulk Extracellular Fluid (Influx) Efflux Methodology Indicator‐Dilution Methods Steady‐State Techniques (Volume of Distribution) Blood Flow Summary and Conclusions