Citations

Importance The NRG Oncology research organization and NSABP B-35 randomized clinical trial prospectively collected margin width data on postmenopausal women with hormone receptor (HR)–positive ductal carcinoma in situ (DCIS) who underwent lumpectomy, whole-breast irradiation (WBI), and randomly assigned adjuvant anastrozole or tamoxifen therapy. This permitted analysis of outcomes per margin width. Objective To analyze the effect of margin width on ipsilateral breast tumor recurrence (IBTR). Design, Setting, and Participants NSABP B-35 was a phase 3, double-blind, randomized clinical trial in which patients were randomized to either 5 years of tamoxifen or anastrozole. Postmenopausal women with HR-positive DCIS and tumor-free margins were eligible. Enrollment was from January 6, 2003, to June 15, 2006, in academic and community hospital members of the NSABP. Study data were analyzed from July 2024 to April 2025. Interventions There were no specific interventions based on lumpectomy margin width. Main Outcomes and Measures Lumpectomy margin width data were prospectively collected within 3 months of randomization. A pathology form classified margins as positive (ink on tumor), close (<1 mm), or negative (≥1 mm). For the negative margin subgroup, closest margin width was stated separately. Thus, an ancillary analysis using 1-mm and 2-mm margin width partitions was performed. Results A total of 3104 postmenopausal women (mean [SD] age, 61 [7.8] years) were enrolled in NSABP B-35. In an ancillary analysis, 2707 patients were included in the 1-mm margin width partition group, and 2546 patients were included in the 2-mm margin width partition group. IBTR was the most common first event, occurring in 90 of 2707 patients (3.3%): 24 of 502 patients (4.8%) with a margin width less than 1 mm and 66 of 2205 patients (3.0%) with a margin width greater than or equal to 1 mm. Ten-year unadjusted cumulative incidence of IBTR events was 5.6% vs 4.0% for margins less than 1 mm vs margins greater than or equal to 1 mm ( P = .04). Using 2 mm as the discriminant threshold for margin width, 39 of 879 patients (4.4%) with margins less than 2 mm and 49 of 1667 patients (2.9%) with margins greater than or equal to 2 mm experienced an IBTR first. Ten-year unadjusted cumulative incidence of IBTR events with margins less than or equal to 2 mm was 5.3% vs 3.8% for those with margins greater than 2 mm ( P = .05). In models adjusting for other patient and tumor factors, margin width was not a significant predictor of IBTR risk (2-mm threshold hazard ratio, 1.33; 95% CI, 0.86-2.06). Conclusions and Relevance Results of this ancillary analysis of the NSABP B-35 trial show that absolute differences in IBTR rates using margin width groupings of less than 1 mm or greater than or equal to 1 mm and margin width groupings of less than 2 mm or greater than or equal to 2 mm in postmenopausal women with HR-positive DCIS receiving lumpectomy, WBI, and adjuvant endocrine therapy were small. Omission of reexcision lumpectomies based on margin widths of less than 1 mm or less than 2 mm can be reconsidered in appropriate patients. Trial Registration ClinicalTrials.gov Identifier: NCT00053898

Background: Smokers with multiple sclerosis (MS) experience worse disease, yet underlying mechanisms remain unknown. Smoking disrupts bile acid and tryptophan metabolism in non-MS populations; both pathways involve host–microbiome co-metabolism and have been linked to MS. Objective: Determine whether smoking perturbs these metabolic pathways in MS and whether such alterations statistically mediate smoking’s effect on MS severity. Methods: We analyzed serum bile acid, tryptophan, and tobacco-related metabolites across four independent MS cohorts ( N = 266) using discovery-replication analyses. Mixed-effects regression assessed replicating associations with current smoking and nicotine exposure. Mediation analyses tested if replicating metabolites were potential mediators between smoking and MS severity. Hypothesis-generating metagenomic analyses explored smoking-associated gut–microbial shifts and metabolite correlations. Results: Current smokers and nicotine-exposed MS subjects had reductions in bile acids and tryptophan metabolites, notably indolepropionate, a neuroprotective, anti-inflammatory gut–microbial metabolite. Lower indolepropionate statistically mediated ~20% of smoking’s adverse effect on MS severity. Metagenomic analyses identified potential smoking-enriched MS-linked taxa, and that indolepropionate broadly co-occurs with microbial networks (e.g. Lachnoclostridium appeared inversely associated with indolepropionate in smokers with MS). Conclusion: Tobacco exposure disrupts host–microbiome tryptophan and bile acid metabolism in persons with multiple sclerosis, with indolepropionate depletion partially mediating disease severity, highlighting a potential mechanistic pathway warranting further investigation in MS smokers.

Abstract Importance Respiratory syncytial virus (RSV) hospitalization rates are highest among children <2 years of age. RSV immunization with infant monoclonal antibody or maternal vaccine is recommended to protect all U.S. infants in their first RSV season. For certain high-risk children aged 8−19 months entering their second RSV season, the monoclonal antibody nirsevimab is recommended. Little is known regarding preexisting health conditions as risk factors for RSV-associated respiratory failure in children during their second season. Objectives To describe children admitted to the pediatric intensive care unit (PICU) for RSV during their second RSV season by preexisting health conditions, and to compare demographic and clinical characteristics across groups. Design, Setting, and Participants Surveillance registry of children 8-<24 months old admitted to the PICU in 30 pediatric hospitals in the 2023-2024/2024-2025 RSV seasons. All children had an RSV-positive respiratory sample and received respiratory support with high flow nasal cannula, noninvasive ventilation, or invasive mechanical ventilation (IMV). Exposure Preexisting health conditions potentially increasing risk of severe RSV disease. Main Outcomes and Measures Patients were classified into four mutually exclusive groups by preexisting health conditions: 1) U.S. nirsevimab eligible criteria, 2) other identified RSV risk conditions (with some evidence of increased risk for severe RSV), 3) other preexisting conditions, and 4) no preexisting conditions. Patient demographic characteristics and level of respiratory support received were compared. Results Among 574 children: 47 (8.2%) had U.S. nirsevimab eligibility criteria, 76 (13.2%) had other RSV risk conditions, 96 (16.7%) had other preexisting conditions, and 355 (61.8%) had none. A higher proportion of children with nirsevimab eligibility factors (40.4%) than those with other identified RSV risk conditions (17.1%) required IMV, which was higher than other (10.4%) or no (5.9%) preexisting health conditions (p trend <0.001). Conclusions and Relevance Approximately 20% of children admitted to the PICU with severe RSV were in the defined groups that met U.S. nirsevimab-eligibility criteria or that had an identified RSV risk condition associated with known risk for severe RSV. A considerable proportion of both groups of children required IMV for respiratory support. These findings may help inform future deliberations regarding U.S. second season nirsevimab-eligibility recommendations. Key Points Question What preexisting health conditions are present in children aged 8–<24 months admitted to the pediatric intensive care unit for severe RSV disease in their second RSV season and how do clinical outcomes differ? Findings Among 574 children, 8% were eligible for nirsevimab in the U.S. and 13% had other preexisting conditions with known risk for severe RSV. Children with preexisting conditions, particularly those eligible for nirsevimab, were more likely to require invasive mechanical ventilation than those without. Meaning These findings may inform future deliberations regarding which children in their second RSV season might benefit from RSV preventive products.

Racial and ethnic survival disparities exist in pediatric and young adult acute lymphoblastic leukemia (ALL), with differential treatment toxicity and setting of care as possible drivers. Whether treatment cost and utilization disparities exist, or how they may contribute to differences in ALL survival, is unknown. Patients with ALL diagnosed between 2000 and 2019 at ages 1 to 24 years were identified using ICD-9/10 codes in the administrative claims data from the OptumLabs Data Warehouse and the Surveillance, Epidemiology, and End Results program. Sex, race and ethnicity, age, year, and subtype of ALL diagnosis were collected. Reimbursed treatment cost and utilization (number of inpatient and outpatient days) were computed over the initial 8 months of treatment. Regression models assessed associations with demographic and clinical characteristics. Survival analyses assessed associations between race and ethnicity and overall survival. Among 374 individuals with ALL, treatment costs were 57% lower for Black patients than for White patients (estimate 0.43, 95% CI: 0.19-0.96; P =0.04). Utilization was similar across groups, though Black patients had 24% fewer outpatient visits (estimate 0.76, 95% CI: 0.58-0.99; P =0.04). No significant differences in survival were detected, but observations are consistent with previous reports. Despite a small sample size, the statistically significantly lower cost and utilization among Black relative to White ALL patients are concerning and warrant further research.

Background/Objectives: Novel vision-language models (VLMs) can integrate patient textual data with image data to support medical diagnosis. Recent studies reported conflicting results regarding the performance of multimodal VLMs compared to other models and physician performance. This systematic review aims to assess the diagnostic performance of multimodal VLMs integrating both patient textual and image data across diverse real-world hospital settings. Methods: We performed comprehensive searches of eight resources, including Embase, MEDLINE, and SCOPUS, on 17 December 2025. Eligible studies reporting diagnostic performance of VLMs integrating both image and patient history textual data from real-world adult patients compared to that of other models and physicians were included. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Prediction model study Risk Of Bias Assessment Tool + AI (PROBAST + AI) was used to assess the quality and risk of bias. The study protocol was registered in the PROSPERO database (CRD420251244054). This review received no external funding. Results: We screened 11,026 records, of which 18 studies met the inclusion criteria. Six studies comparing multimodal and unimodal models demonstrated the consistent superiority of the multimodal models. Four studies evaluating VLM accuracy as standalone agents compared with physician performance reported conflicting evidence. One study assessing VLMs as a clinical copilot demonstrated higher accuracy from the group of physicians using VLM assistance. A meta-analysis could not be performed due to the heterogeneity across study populations and outcomes. The majority of the studies were assessed as having a high risk of bias due to dataset quality. Primary limitations identified across studies include small sample size, a lack of external validation, and the need for prospective clinical deployment studies. No study provided documented considerations regarding model safety or data security. Conclusions: This systematic review suggests that multimodal VLMs consistently outperform unimodal models with access to only image or text. While model performance as standalone agents compared to humans remains inconclusive, a copilot model has demonstrated high diagnostic accuracy. Given substantial methodological concerns across studies, cautious interpretation is required, No firm clinical recommendation can be made regarding the use of standalone VLMs. Further research employing high-quality datasets is needed to ensure the reliability and clinical applicability of future VLMs.

Abstract One of the most significant unmet clinical challenges in paediatric oncology is the development of novel therapeutic strategies for recurrent medulloblastoma (R-MB). MYC-driven MBs are defined as classically cold tumours with a low incidence of infiltrating immune cells, resulting in a therapeutic challenge. The identification of cell-cell interactions between tumour and immune cells may provide insight into critical intercellular communications and manipulations occurring within the tumour immune microenvironment (TME). We hypothesised that the key cell-cell interactions in MYC-driven primary and recurrent MB may reveal dominant immune-suppressive mechanisms and uncover targetable therapeutic vulnerabilities. Paired primary-recurrent bulk RNA-sequencing data, confirmed myeloid cells as the most infiltrating immune cell type in group3-MB and group4-MB. Comprehensive spatial phenotypic and cell-cell communication analyses corroborated this discovery, validating an increased incidence of macrophages in the matched-recurrent tumours. Subsequently, we used innovative algorithms for 10X MB single-cell data to predict interactions between tumour-cell ligands and immune-cell receptors within the TME; macrophages emerged as the core immune-cells involved in interactions throughout the TMEs, with the most significant ligand-receptor interaction and inflammatory response between MIF and CD74. In-depth immunohistochemistry analyses of primary and recurrent group3 and group4 tumours, and exhaustive tissue microarrays demonstrated expression of CD74 and MIF, with limited expression of CD74 within the brain. To investigate the therapeutic potential of CD74, we developed recurrent, immune competent MYC-driven medulloblastoma mouse models. Comprehensive deconvolution analysis confirmed the TME integrity of our models to mirror that of the human disease. Locoregional delivery and repeat dosing of a bioactive-CD74 peptide demonstrated complete tumour clearance in our immune-competent mouse models of primary and recurrent-MB, demonstrating the significant therapeutic potential of targeting the CD74-MIF axis in MYC-driven primary and recurrent MB. Key therapeutic vulnerabilities within the TME of MYC-driven MB have been identified, highlighting the CD74-MIF axis as a target for next-generation immunotherapies.

Abstract Amyotrophic lateral sclerosis (ALS) is a fatal, rapidly progressive neurodegenerative disease of motor neurons for which therapeutics are limited. Improved biomarkers are imperative to improve patient care and therapeutic development. Here, we employed 35-plex isobaric tandem mass tag labeling based on isobutyl-proline reporter group (TMTpro) to perform unbiased proteomic analysis of cerebrospinal fluid (CSF) and plasma from control (n= 28, n= 31) and sporadic ALS (sALS) (n= 39, n= 41), from the Target ALS Global Natural History Study (TALS GNHS). We identified 2,875 proteins in CSF and 1,118 proteins in plasma and identified known and novel differentially expressed proteins (DEPs) between controls and sALS, some of which were orthogonally validated using immunoassay. Comparison of TMTpro-MS and Olink proximity extension assay proteomics revealed common and non-overlapping differentially expressed proteins illustrating strengths unique to each platform. This initial cross-sectional proteomic study of biofluids from the TALS GNHS, with unrestricted availability of study results to the research community, highlights the potential of this resource as a potent platform for ALS biomarker discovery.

Abstract Background Inflammatory bowel disease (IBD) can lead to a hypercoagulable state, particularly at initial diagnosis. Consequently, venous thromboembolism (VTE) represents a serious complication associated with IBD. For children, the risk of VTE near the time of IBD diagnosis has not yet been quantified, and guidance for VTE prophylaxis remains unclear. Methods This multisite matched case-control study examined pediatric patients with a new diagnosis of IBD from 2019-2024. Cases were those who developed VTE ≤30 days prior or ≤90 days post IBD diagnosis. Controls were newly diagnosed IBD patients without VTE. Each case was matched to 2-6 controls based on IBD subtype and age at IBD diagnosis. Demographics, laboratory values, prothrombotic risk factors and treatments were compared between matched cases and controls using conditional logistic regression stratified, by case sets. Results Study subjects included 15 cases and 52 controls, resulting in 70 matched pairs. Factors associated with early VTE included lower hemoglobin (P = .003), lower albumin (P = .002), corticosteroids use (P = .01), central line (P = .002), and any surgery ≤90 days post IBD diagnosis (P = .001). No association with VTE was detected for platelets, C-reactive protein, fecal calprotectin, family history of IBD or body mass index. Conclusions Albumin, hemoglobin, steroid use, central line, and any surgery ≤90 days post IBD diagnosis were associated with early VTE. These risk factors may be used in a future prediction model to stratify newly diagnosed pediatric IBD patients by their risk for early VTE development.

Abstract Background and Aims Primary sclerosing cholangitis (PSC) increases the risk of developing cholangiocarcinoma (CCA), which is the leading cause of premature death in these patients. Current diagnostic methods for early detection of either condition remain largely suboptimal. This study investigates serum metabolites as potential non-invasive diagnostic biomarkers. Methods This multicenter international study analyzed 459 serum samples from 13 international centers, including patients with PSC (n = 216), PSC who developed CCA during follow-up (PSC to CCA; n = 24), PSC with concomitant CCA (PSC-CCA; n = 88), ulcerative colitis (UC; n = 14), and healthy individuals (n = 79). Serum metabolomics was evaluated by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and the accuracy of the single candidate metabolite biomarkers was further assessed. Machine learning was used to generate the best diagnostic and predictive algorithms. Results Fifty-two serum metabolites were identified as markers of PSC, independent of age, biological sex, cirrhosis, UC, or ursodeoxycholic acid (UDCA) treatment. A diagnostic model based on only six lipids distinguished PSC patients from healthy controls with high accuracy (AUC 0.952 discovery, 0.918 validation), independently of the levels of transaminases or cholestasis markers (i.e., GGT, AP, bilirubin) in blood. Subgroup analyses were performed in PSC patients with and without inflammatory bowel disease (IBD), confirming the model’s consistent performance across both subgroups when compared with their respective control groups – UC and healthy controls (AUC 0.959 and 0.939, respectively). The PSC model also confirmed its diagnostic performance in patients with established PSC-CCA or progression to CCA within 2 years, when compared to UC and healthy controls, showing AUC values ranging from 0.911 to 0.976. Additionally, 20 metabolites were significantly altered in PSC patients with CCA (PSC-CCA), regardless of demographics, cirrhosis, UDCA treatment, IBD status, or CCA subtype, and independently of serum transaminases and cholestatic markers levels. A model incorporating six lipids accurately identified PSC-CCA cases compared to PSC patients (AUC = 0.912 discovery; 0.896 validation), showing excellent performance in early-stage tumor detection (AUC 0.921) and outperforming CA19-9 (AUC 0.708). Importantly, the model retained high accuracy even in CCA patients with low CA19-9 levels (AUC 0.905) and in PSC patients who developed CCA within a year of clinical diagnosis (AUC 0.792). Conclusion The PSC and PSC-CCA blood tests provide a non-invasive approach for diagnosing PSC and PSC-CCA, addressing an important need in this population. Their integration into clinical practice could enhance risk stratification, early detection, personalized surveillance, and treatment decision-making in PSC patients.

Abstract Background and Aims Cholangiocarcinoma (CCA) is a rare and aggressive malignancy originating from the bile ducts, characterized by a poor prognosis and limited treatment options. Potentially curative interventions include surgical resection with negative margins (R0) and liver transplantation. However, despite these treatments, the likelihood of post-surgical recurrence remains high, with 50–70% of patients experiencing disease recurrence within five years. This highlights the urgent need for reliable prognostic biomarkers to guide clinical decision-making and personalize postoperative care. Numerous prognostic biomarkers identified in tumor tissue for CCA have been proposed, but many studies suffer from limitations such as single-center designs, small patient cohorts, and semi-quantitative techniques such as immunohistochemistry. These methods often rely on observer interpretation and antibody specificity, which can compromise objectivity and reproducibility. To overcome these limitations, we aimed to identify and validate robust quantitative prognostic biomarkers for CCA by analyzing tumor mRNA expression, integrating them using machine learning (ML), and developing a web-based tool to predict tumor recurrence and overall survival (OS). Method A systematic literature review was conducted to identify candidate prognostic tissue biomarkers for CCA, which were further validated using Cox proportional hazards (PH) OS analyses in five independent transcriptomic cohorts. An international multicenter cohort of 221 patients undergoing tumor resection was assembled, and tumor mRNA expression was analyzed using high-throughput TaqMan OpenArray RT-qPCR. Associations between clinical and molecular features and recurrence-free survival (RFS) and OS were assessed using Cox PH analyses. Six ML survival models were trained and evaluated using nested cross-validation, followed by feature reduction. Shapley Additive exPlanations (SHAP) values were applied to assess feature importance and enhance model interpretability. Results Among 496 candidate CCA prognostic biomarkers identified from the literature, 52 showed prognostic value in Cox PH analyses across transcriptomic cohorts. In the multicenter validation cohort, integrative Cox PH analyses incorporating clinical variables and gene expression identified 11 genes significantly associated with RFS and 25 with OS. The 11 RFS-associated genes, combined with five clinical variables, were used to train six ML survival models, among which the random survival forest (RSF) model demonstrated the highest predictive accuracy. Following training of RSF models across all combinations of four genes and five clinical variables, and subsequent feature importance–based selection, a final model incorporating three genes (FSCN1, LDHA, and SLC2A1) and four clinical variables (CCA subtype, resection margin status, biological sex, and age at surgery) achieved a testing time-dependent AUC of 0.82. This model significantly stratified patients into high- and low recurrence-risk groups, with a median RFS (mRFS) of 8 months and 70 months, respectively. In addition, an OS prediction model using the same feature set achieved a testing AUC of 0.775 and significantly stratified patients according to 5-year OS risk, with a median OS (mOS) of 23 months in the high-risk group and 77 months in the low-risk group. Conclusion We identified and validated a robust panel of prognostic tissue biomarkers in patients with resected CCA. Integration of these biomarkers with clinical variables into ML-based prognostic models enabled accurate prediction of RFS and OS. The resulting web-based tool allows clinicians to input RT-qPCR results for the validated biomarkers and obtain precise, individualized prognostic predictions for postoperative recurrence and survival.