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BACKGROUND AND OBJECTIVES: Stereotactic radiosurgery (SRS) for brain arteriovenous malformations (AVMs) can cause radiation-induced changes (RIC), but its interactions with AVM embolization are not well established. The aim of this study was to assess the effect of previous embolization on RIC in SRS-treated AVM patients. METHODS: Data of AVM patients treated with single-session SRS from 1987 to 2018 were retrieved from the International Radiosurgery Research Foundation. Univariable and stepwise backward logistic regression models were used to identify RIC predictors. Patients were stratified into those who received pre-SRS embolization (E + SRS) vs those who did not (SRS-only). Radiologic RIC (rRIC) was the primary end point. Secondary outcomes included symptomatic RIC (sRIC) and permanent symptomatic RIC (pRIC). RESULTS: Among 1187 AVM patients, the mean age was 36.4 years (SD 16.8), with 50.4% female patients. AVMs had a mean untreated AVM volume of 5.2 cm 3 . A total of 130 patients (11%) underwent neoadjuvant embolization (E + SRS). The overall rRIC and sRIC rates were 32.8% and 12.1%, respectively, with time to rRIC and sRIC being significantly longer in the E + SRS vs SRS-only cohorts (103.6 ± 339.4 vs 11.3 ± 12.5 and 60.4 ± 262.9 vs 9.5 ± 9.0 months, respectively). Seizures at presentation and ≥3-cm untreated AVM volume significantly correlated with a higher likelihood of rRIC (adjusted odds ratio [aOR] 2.32 [1.25-4.30] and aOR 2.77 [1.24-6.16], respectively). Age and pre-SRS embolization were associated with a significantly lower risk of rRIC (aOR 0.97 [0.96-0.99] and aOR 0.32 [0.13-0.74], respectively). The E + SRS cohort had significantly reduced odds of rRIC (aOR = 0.37 [0.15-0.89]) and pRIC (aOR = 0.04 [0.00-0.30]) after adjusting for significant covariates. CONCLUSION: Seizures at presentation and larger untreated AVM volume were independently associated with an increased risk of rRIC, whereas older age and pre-SRS embolization were protective. Notably, patients who underwent embolization before SRS had lower odds of rRIC and pRIC, with delayed onset of complications compared with SRS-only.

Importance The choice of induction agent during rapid sequence intubation (RSI) of critically ill adults may affect clinical outcomes. Although ketamine and etomidate are frequently used for RSI, their comparative effectiveness remains controversial. Objective To compare the safety of etomidate vs ketamine for emergency RSI in critically ill adults. Design, Setting, and Participants This cohort study used a target trial emulation with observational data collected between March 1, 2022, and April 30, 2024, from 18 emergency departments across Brazil, with data from the Brazilian Airway Registry Cooperation. Adults who underwent RSI and received either etomidate or ketamine as a sole hypnotic agent were included; those with preintubation cardiac arrest or immediate postintubation transfer were excluded. Exposure Administration of etomidate or ketamine as the induction agent for RSI. Main Outcomes and Measures The primary outcome was 28-day in-hospital mortality. Secondary outcomes included 7-day in-hospital mortality, first-attempt intubation success, and major adverse events (new hemodynamic instability, severe hypoxemia, and cardiac arrest) within 30 minutes after intubation. Inverse probability of treatment weighting was used to adjust for confounding. Risk ratios (RRs) and risk differences (RDs) with 95% CIs were used to compare outcomes between groups. Results Among 1810 patients (median age, 64 years [IQR, 50-74 years]; 1048 men [57.9%]), 514 received ketamine and 1296 received etomidate. The median shock index was higher in the ketamine group than the etomidate group (0.81 [IQR, 0.65-1.01] vs 0.76 [IQR, 0.59-0.99]), and preintubation vasopressor use was more common in the ketamine group than the etomidate group (191 of 514 [37.2%] vs 391 of 1296 [30.2%]). Weighted 28-day mortality was higher with etomidate than ketamine (60.5% [95% CI, 57.2%-63.8%] vs 54.4% [95% CI, 45.0%-63.9%]; RR, 1.14 [95% CI, 1.03-1.27]; RD, 7.6% [95% CI, 2.0%-13.3%]). Seven-day mortality was also higher with etomidate than ketamine (35.2% [95% CI, 32.0%-38.3%] vs 30.1% [95% CI, 23.5%-36.7%]; RR, 1.19 [95% CI, 1.04-1.35]). New hemodynamic instability within 30 minutes after intubation was more frequent with ketamine (24.2% [95% CI, 20.4%-28.0%] vs 18.9% [95% CI, 16.7%-21.0%]; RR, 0.78 [0.64-0.95]). There were no statistically significant differences in the other secondary outcomes. Conclusions and Relevance In this cohort study of critically ill adults undergoing RSI, etomidate use was associated with higher in-hospital mortality at 7 and 28 days compared with ketamine. These findings highlight the need for definitive randomized clinical trials to compare both agents.

Abstract Background Women with a pathogenic variant in BRCA1 or BRCA2 are at high risk of developing ovarian cancer, and it is often recommended that they undergo bilateral salpingo-oophorectomy at an early age, resulting in surgical menopause. Menopausal hormone replacement therapy (HRT) is an effective way to mitigate the adverse outcomes of early menopause; however, the safety of menopausal HRT on breast cancer risk in this population has not been established. Methods We conducted a prospective matched analysis of HRT use following menopause and breast cancer risk in BRCA carriers. Women who initiated HRT were matched one-to-one with women who had not initiated menopausal HRT by gene, year of birth, and age at menopause, resulting in 676 matched pairs. Menopausal HRT use collected by questionnaire included formulation and mode of administration. Results After a mean of 5.6 years, there were 87 (12.9%) incident breast cancer cases in the 676 exposed women and 128 (18.9%) cases in the 676 unexposed women (P = .002). Compared with unexposed matched control individuals, women who used estrogen alone experienced a statistically significantly decreased risk of breast cancer (hazard ratio = 0.37, 95% CI = 0.24 to 0.57). No protective or adverse effect was associated with the use of estrogen plus progestogen (hazard ratio = 0.94, 95% CI = 0.54 to 1.63). Conclusions Our findings suggest no substantial increase in the risk of breast cancer in BRCA carriers with the use of HRT and that estrogen alone might be protective.

Abstract Background: For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmarks capable of automated grading and designed specifically to measure the performance of LLMs for medication tasks are required. The purpose of this study was to design a suite of benchmarking tests reflective of Comprehensive Medication Management (CMM; the standard of care for medication optimization) and quantify the baseline performance of the latest LLMs. Methods: We established six benchmarks representing critical stages of the CMM process: drug formulation matching, drug order (sig) generation, drug route matching, drug-drug interaction identification, renal dose identification, and drug-indication matching. For each benchmark, we curated a clinician-annotated dataset comprising 250 standardized input-output pairs including both inpatient and outpatient medications. We evaluated the clinical knowledge retrieval capabilities of three LLMs: GPT-4o-mini, MedGemma-27B, and LLaMA3-70B. We employed a zero-shot prompting strategy, excluding in-context examples, to assess the models’ internal clinical knowledge rather than their few-shot learning potential. To check reliability, each model was run three times using a temperature of 0.7 (a mid-range value of an LLM setting controlling text generation randomness). Performance was assessed using task-specific evaluation metrics including precision (positive predictive value), recall (sensitivity), F1-score, accuracy, and correctness consistency across trials. Results: Across six benchmarks, LLaMA3-70B demonstrated the highest performance in four tasks: drug-formulation matching (F1, 54.0% [95 CI: 50.1-58]), drug-order generation (accuracy, 88.0%), drug-route identification (F1, 74.3% [95 CI: 71-78]), and drug-indication identification (accuracy, 97.6% [95 CI: 95.6-99.2]). In the drug–drug interaction task, GPT-4o-mini achieved the highest overall accuracy (70.4% [95 CI: 64.8-75.7]). For renal dose–adjustment identification, GPT-4o-mini demonstrated the highest F1 score (83.3% [95 CI: 77.6-88]). Correctness-consistency scores ranged from 8.0% to 97.6% across benchmarks, with no model exhibiting uniformly superior consistency. Conclusions: Model performance varied substantially across medication-related tasks. LLaMA3-70B demonstrated promising baseline performance in tasks involving formulation, ordering, route, and indication. GPT-4o-mini showed potential advantages in drug–drug interaction detection and renal dose adjustment. These findings underscore the need for task-specific evaluation when deploying models for medication-focused clinical decision support.

Abstract Background The purpose of this study was to develop a series of benchmarks specifically for large language model (LLM) performance in the identification of drug-drug interactions (DDIs). Methods We evaluated six LLMs using a clinician-designed dataset of 750 DDI scenarios. Tasks included: (1) a pointwise two-drug classification task (2) a pairwise three-drug discrimination task and (3) a listwise 4–6 drug selection task. Zero-shot prompting was applied and performance was assessed using precision, recall, F1 score, accuracy, and self-consistency. Results Model performance varied by task structure and interaction severity. GPT-5-Chat demonstrated the highest recall and F1 score in the pointwise task (80.7% and 80.2%), whereas DrugGPT achieved superior accuracy in the pairwise (93.7%) and listwise task (92.6%). All models had improved performance with more severe compared to less severe DDIs (accuracy 72.0-99.2% vs. 58.3-86.7%). All models tended to have higher self-consistency in the pairwise and listwise experiments compared to the pointwise experiment (68.8-93.6% vs. 31.0-74.2%). No model exhibited uniformly high reliability across all tasks. Conclusions Current LLMs show inconsistent capabilities in identifying DDIs. Performance degrades as reasoning space expands with limited stability. These findings emphasize the need for multi-format evaluation frameworks and reliability-aware assessment when considering LLMs for medication-safety applications.

Abstract Pancreatic cancer, projected to become the second-deadliest malignancy in Western societies by 2030, requires urgent innovations in early detection and risk stratification. Intraductal papillary mucinous neoplasms (IPMNs) represent critical precursor lesions, but current clinical guidelines demonstrate suboptimal accuracy in malignancy prediction, leading to either unnecessary surgeries or missed opportunities for early intervention. In this paper, we present Cyst-X, an artificial intelligence (AI) framework that accurately predicts IPMN malignant transformation using multicenter magnetic resonance imaging (MRI) data. Unlike most previous approaches that rely on computed tomography (CT), our method capitalizes on the superior soft tissue contrast of MRI, allowing for more precise identification of subtle imaging biomarkers. We developed and validated deep learning models on 723 T1-weighted and 738 T2-weighted MRI scans from 764 patients among seven international institutions, demonstrating significantly superior performance (AUC=0.82) compared to current clinical (Kyoto) guidelines (AUC=0.75) and expert radiologists. AI-based imaging features correlate strongly with clinically recognized malignancy markers, providing potential biologically relevant insights. This approach holds promise to significantly refine clinical decision-making, reduce unnecessary surgeries, and better identify high-risk IPMN patients for timely intervention. Our approach integrates a novel pancreas segmentation network with robust classification models that identify subtle imaging biomarkers associated with malignancy risk. Importantly, we demonstrate that these models retain high performance in a privacy-preserving federated learning setting, where institutions collaboratively train AI models without exchanging patient data to address key regulatory and ethical barriers. We publicly release the Cyst-X dataset--the first large-scale, multi-center pancreatic cyst MRI collection--to accelerate research in this field. This study addresses a critical clinical need while establishing technical foundations for privacy-preserving AI in radiology that could transform pancreatic cancer management through earlier intervention and reduced unnecessary procedures. The dataset can be accessed at https://osf.io/74vfs/, and the source code for our deep learning segmentation and classification models is available at https://github.com/NUBagciLab/Cyst-X.

Background: Endometriosis affects an estimated 10% of reproductive-aged women and is associated with increased ovarian cancer risk. While BRCA1/2 mutations are established risk factors for ovarian cancer, their association with endometriosis remains unclear. This study aimed to characterize the prevalence and clinical features of endometriosis within a large cohort of BRCA mutation carriers. Methods: A descriptive analysis was conducted using data from a multi-center longitudinal cohort of women with pathogenic BRCA variants. Reproductive history and related factors were collected through self-reported questionnaires and compared. Results: Among 16,950 BRCA carriers, the prevalence of endometriosis was 2.4%. Compared to BRCA carriers without endometriosis, those with endometriosis were more likely to carry a BRCA2 mutation, have post-secondary education, and experience earlier menarche. BRCA carriers with endometriosis had a lower ovarian cancer prevalence than those without (10% vs. 15%, p < 0.001). Conclusions: This is the first study of this scale to report the prevalence of endometriosis among BRCA mutation carriers, which was lower than previously reported in the general population. The association between endometriosis and ovarian cancer does not appear to be generalizable to this population. Further prospective studies are warranted to clarify this association among BRCA mutation carriers.

Abstract Wiskott-Aldrich syndrome (WAS), an X-linked disorder characterized by immunodeficiency, thrombocytopenia, autoimmunity, and malignancy, can be effectively treated with allogeneic hematopoietic cell transplantation (HCT). Older age at HCT and mismatched donors are known to affect overall survival (OS). However, the influence of specific clinical manifestations or WAS variant class on OS and factors associated with event-free survival (EFS) remain incompletely defined. We analyzed outcomes of 308 patients with WAS who underwent HCT at 37 institutions of the Primary Immune Deficiency Treatment Consortium from 1990 to 2018. With a median follow-up of 5.3 years, the 5-year OS and EFS were 87.2% and 79.7%, respectively. Age ≥5 years, donor type, and a pre-HCT history of severe infection had a negative impact on OS and EFS, whereas pre-HCT autoimmunity had no impact. Reduced-intensity regimens were associated with lower T-cell and myeloid donor chimerism, particularly when non–busulfan-based regimens were used. Low myeloid donor chimerism was associated with lower platelet counts. Mixed chimerism was not consistently associated with post-HCT autoimmunity. Patients with class I (exon 1-2 missense and intron 5 hot spot variants) and class II variants (all others) had similar pre-HCT clinical symptom severity and no difference in OS, EFS, or platelet recovery post-HCT. In conclusion, our study showed excellent long-term OS and EFS after HCT for WAS, highlighting the importance of early HCT, before the development of severe infections. We confirmed that HCT using busulfan-based conditioning was associated with improved donor chimerism and platelet recovery. This trial was registered at www.clinicaltrials.gov as NCT02064933.

Abstract Introduction The International Prognostic Index (IPI) is widely used to stratify risk in large B-cell lymphoma (LBCL) across all ages, with age-adjusted (aa)IPI applied to younger patients. While there have been efforts to develop prognostic indices (PIs) tailored to older patients, few are adopted in clinical practice due to their complexity and reliance on functional assessments, which are not routinely collected in real-world registry datasets. Recently proposed SENIOR-IPI (Dubois, ASH 2024) was developed for older patients with LBCL receiving anthracycline (A)-based therapy. We assessed the performance of standard and novel PIs in patients aged 80+ years and externally validated the SENIOR-IPI. Methods We harmonized and pooled data of patients aged 80+ years with newly diagnosed LBCL from a national registry and two prospective cohorts: NiHiL (Czech Republic, enrolled 2010–2023), LEO (USA; n=222, 2015–2020), and MER (USA; n=77, 2010–2015), forming a cohort of 819 individuals. We included 536 individuals with complete data on IPI components and albumin, including 485 treated with chemotherapy and 397 with A-based regimens (primary cohort). Following PIs were evaluated: IPI, NCCN-IPI, (aa)IPI, and SENIOR-IPI (age per year, aaIPI components, LDH &gt;3x ULN as extra point, and albumin, all weighted six-fold; extranodal involvement excluded due to lack of prognostic significance). Multivariable hazard ratios (HR) were estimated using Cox proportional hazards models; discriminative ability was assessed using C-index and calibration plots. The primary endpoint was overall survival (OS); event-free survival results were consistent (230/357 events were deaths) and are not reported here. Results Among the 397 A-treated patients (median age 82 years, range 80–99), ECOG performance status (PS) 2–4 was observed in 35% of patients, clinical stage III–IV in 67%, elevated LDH in 65% (8% with &gt;3x ULN), &gt;1 extranodal (EN) site in 32%, and albumin levels &lt; 3.5 g/dL in 27%. In multivariable modeling, only age per year (HR=1.07, P&lt;0.01), and clinical stage (HR=1.36, P=0.048) were independent predictors of OS, whereas ECOG PS (HR=1.27, P=0.12), trichotomized LDH by ULN (HR=1.23, P=0.08) and albumin (HR=1.17, P=0.35) were not significant. SENIOR-IPI had the highest discrimination (C-index 0.631) in A-treated patients, outperforming IPI (0.617), NCCN-IPI (0.591), and aaIPI (0.620), with consistent results across cohorts: NiHiL (0.633), LEO (0.619), and MER (0.621). SENIOR-IPI stratified patients into low- (L-Risk, 0–9 points, 28%), low-intermediate (L-Int, 10–19 points, 42%), high-intermediate (H-Int, 20–29 points, 23%), and high-risk (H-Risk, 30–40 points, 7%) categories, with OS medians of 6.3, 3.8, 2.4, and 1.4 years, respectively (P&lt;0.01), and significant separation between L-Int and H-Int (HR=1.47, P=0.02). By comparison, IPI categorized patients as L-Risk (12%), L-Int (24%), H-Int (27%), and H-Risk (37%), with median OS of 8.7, 4.6, 4.8, and 1.9 years, resp. (P&lt;0.01). aaIPI yielded following distributions: 13%, 29%, 37%, and 21%; and median OS: 8.7, 5.3, 3.4, and 1.5 years; P&lt;0.01. NCCN-IPI stratified patients into three groups: L-Int (13%), H-Int (49%), and H-Risk (39%), with respective OS medians of 8.7, 4.1, and 2.5 years (P&lt;0.01). All PIs showed modest overestimation of mortality (calibration slopes: SENIOR-IPI 1.36; IPI 1.24; aaIPI 1.30; NCCN-IPI 1.26; intercept: -0.97, -0.87, -0.92, -0.87, resp.). In the entire cohort (n=536), SENIOR-IPI showed highest C-index (0.651 vs IPI 0.622, aaIPI 0.631, NCCN-IPI 0.606), and stratified patients into L-Risk (26%, 2-year OS: 6.4 years), L-Int (40%, 3.2 years), H-Int (24%, 1.5 years), and H-Risk (10%, 0.6 years). Calibration slope was 1.18 (intercept -0.33). Among patients not receiving chemotherapy, SENIOR-IPI also performed best (C-index 0.759 vs IPI 0.738, aaIPI 0.757, NCCN-IPI 0.728). Conclusion SENIOR-IPI enables effective risk stratification of patients aged 80+ years with LBCL into four prognostically distinct groups, outperforming traditional indices (IPI, NCCN-IPI, aaIPI) in patients receiving A-based therapy, as well as in the entire cohort and in patients not receiving chemotherapy. While its discrimination is superior, modest miscalibration suggests that future recalibration may improve precision. These findings support SENIOR-IPI as a clinically relevant prognostic tool for the elderly LBCL patient population. Funding: NU21-03-00411, P50 CA97274, U01 CA195568.