Citations

ABSTRACT One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

Background Tumor-infiltrating lymphocytes (TILs) are established determinants of clinical outcomes in breast cancer. 1–6 While stromal TILs (sTILs) are commonly evaluated due to easier scoring and higher interobserver reproducibility, intraepithelial TILs may provide additional insights into anti-tumor immunity. 7 We aimed to comprehensively characterize immunotype-associated cellular landscapes in triple-negative breast cancer (TNBC) and identify features associated with clinical outcomes. Methods 212 TNBC tumors were classified into three immunotypes based on immune infiltrate location and density: immune desert (ID), immune excluded (IE), and immune inflamed (IN). 8 Single-cell spatial transcriptomics was performed on 75 tumors to define the cellular composition and functional states of the tumor microenvironment across immunotypes. Findings were validated and integrated with bulk RNA sequencing data and clinical outcome datasets from FinXX, CALGB-40603, I-SPY 2, and Real-World Clinico-Genomic Data (RWCGD) cohorts. Results Patients with IN tumors had significantly improved outcomes compared with ID tumors. Despite high sTILs, IE tumors showed poor outcomes similar to ID tumors. Single-cell spatial analysis revealed that ID and IE tumors exhibited reduced major histocompatibility complex I/II expression and fewer tumor-resident plasmacytoid dendritic cells (pDCs) compared with IN tumors, which were enriched for interferon-alpha (IFNα) and interferon-gamma (IFNγ) responses. High IFN response scores were associated with favorable outcomes across multiple therapy types in independent datasets. Deconvolution of RWCGD bulk RNA-seq data confirmed that pDC abundance correlated with improved outcomes specifically in hormone receptor–negative subtypes. Conclusions Our study highlights pDCs and IFN signaling as hallmarks of effective anti-tumor immunity in TNBC. Immunotype-based profiling underscores the limited prognostic value of sTILs alone in immune-excluded tumors and supports pDCs and IFN pathways as potential biomarkers for prognosis and therapeutic development in TNBC.

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Purpose of review Ophthalmology has emerged as the proving ground for medical artificial intelligence (AI) because of its imaging-centric workflows, standardized data acquisition, and well defined clinical endpoints. Recently, the field has started transitioning from narrowly trained, task-specific algorithms toward multimodal models that are trained on large datasets and can be adapted to many tasks. These models may integrate information from images, text, and clinical data and are increasingly trialed in real-world settings. This review examines recent advances and ongoing controversies in the use of AI as a basic science, translational, and clinical research tool in ophthalmology. Recent findings Recent literature highlights the growing role of self-supervised and foundation models for scalable phenotyping, biomarker discovery, and cross-task generalization, particularly in retinal imaging and optical coherence tomography (OCT). In parallel, large-scale clinical deployments, including cloud-based OCT platforms and autonomous diabetic retinopathy screening, demonstrate clinical impact while revealing persistent challenges related to workflow integration and patient generalizability. Regulatory and ethics-focused studies underscore increasing attention to validation rigor, bias, and postdeployment monitoring, exposing heterogeneity in research standards and physician practices. Summary Ophthalmic AI is maturing toward real-world clinical implementation. Future progress will depend less on performance gains alone and more on clinical alignment, rigorous validation, equity-aware evaluation, and postdeployment surveillance, alongside continued investment in basic science to advance disease understanding and inform robust, generalizable model development.

Objective The United Nations defines sustainability as meeting needs of the present without compromising future generations. While often equated to “saving the planet”, sustainability is a diverse topic. Healthcare generates >5% of CO 2 emissions worldwide, yet sustainability efforts in urology are limited. The North Central Section of the American Urological Association (NCSAUA) recently convened a Sustainability Task Force to address this topic. This study queried members on sustainability in urologic practice. Methods Following literature review on sustainability in surgery/urology, a 19-item anonymous REDCap survey was developed and distributed via email to NCSAUA members (N = 1607). The survey assessed current best practice, institutional/personal sustainability, and demographics. Responses were compiled and summarized using descriptive statistics. Results Survey response rate was 4.9% (79/1607). Respondents were most commonly from metropolitan areas (82%), male (71%), and in practice for 5–14 years (29%). Sustainability topics were prioritized as: (1) financial, (2) future workforce, (3) environmental. Over half (56%) reported no institutional sustainability programs or were unaware of them. Instrument (67%), computer (68%), and paper (65%) recycling were supported by a majority of respondents. However, only 28% and 29% of respondents reported current institutional use of instrument and computer recycling, respectively. Telemedicine was infrequent, with most urologists (58%) conducting 0-5% of visits virtually. Conclusions Current institutional and urologic practice emphasis on sustainability is low. Respondents ranked highest sustainability priorities as financial and future workforce. Efforts to address environmental sustainability were also supported, namely reduction of disposables and waste. The findings support the development of targeted programs to advance sustainability priorities widely.

Background: There is growing support for high-efficacy disease-modifying therapy (DMT) in multiple sclerosis (MS), but escalation (ESC) approaches remain common. Objective: To describe decision-making in a pragmatic trial of early high-efficacy treatment (EHT) versus ESC. Methods: DELIVER-MS is a multi-center, pragmatic, randomized controlled trial (RCT) with a parallel observational study (OBS), which enrolled treatment-naïve people with RRMS in 31 UK/US sites. Primary outcome was as follows: 36-month brain volume loss according to initial treatment approach (EHT vs. ESC). Stepwise multivariable logistic regression was used to predict participation in RCT versus OBS and choice of EHT versus ESC within the OBS cohort. Results: In total, 816 people with MS were enrolled. Participants declined randomization due to preference for a particular DMT (85%), efficacy concerns (20%), and safety concerns (9%). RCT versus OBS participation was associated with lower relapse rate ( p = 0.043) and greater brain parenchymal fraction ( p = 0.002). Among 374 in the OBS cohort, 125 (33%) chose ESC and 249 (67%) chose EHT. People commencing EHT had higher education attainment ( p < 0.001) and relapse rate ( p = 0.025). Conclusion: Baseline DELIVER-MS data demonstrate that participants with milder disease are more likely to participate in RCT. The choice of EHT versus ESC was associated with demographic factors and disease activity. Clinical trial registration: NCT03535298

Over 37% of pregnant women report excessive daytime sleepiness or hypersomnia during the first trimester, and commonly throughout the remaining trimesters of pregnancy and the postpartum period. The clinical presentation and symptoms of central disorders of hypersomnolence (CDH) such as narcolepsy, idiopathic hypersomnia, or excessive daytime sleepiness secondary to shift work or obstructive sleep apnea may be confounded by physiological changes in sleep architecture and hormonal fluctuations occurring during pregnancy. These overlapping features may cause delays in diagnosis and treatment, impacting quality of life, maternal functioning, and overall health. Management of CDH during pregnancy and lactation requires careful consideration of maternal benefits, potential side effects of medications and fetal and neonatal safety. Here, we present a case of idiopathic hypersomnia and detail management and outcomes during pregnancy and lactation. We briefly review the existing literature on prevalence, pathophysiological mechanisms, diagnostic challenges, objective findings, and risks posed by CDH in pregnancy and lactation. In this narrative review for primary care clinicians, we also summarize currently available evidence regarding the efficacy, teratogenic and other risks of pharmacologic agents used in the treatment of CDH in pregnancy and lactation. Lastly, we provide a clinical algorithm for management of CDH during pregnancy and lactation.

Due to increasingly nuanced Crohn disease therapy selection and disease evaluation, radiologic assessment of transmural healing, although aspirational, may be more complete than endoscopic assessment of mucosal healing, which is the current treatment target.