Publications

When using real-world data to construct an external comparator arm for a single-arm trial, there may be differences in how and when patients are assessed for disease between trial and real-world settings. Such differences can generate outcome measurement error when comparing time to event endpoints and lead to biased findings. Recent methods have been developed to mitigate measurement error bias in real-world endpoints; however, they rely on the existence of a validation sample, ie, data on a set of patients where both the "true" trial-like and "mis-measured" real-world measures are collected. We demonstrate how novel statistical methods can be leveraged as quantitative bias analyses (QBA) to contextualize real-world evidence findings when outcome measurement error is of concern, but validation samples are infeasible to collect. QBA allows researchers to set plausible ranges for the amount of error when not directly measurable. We highlight how to conduct QBA with two recent methods, Cumulative Incidence Curve Correction and Survival Regression Calibration, and illustrate how to generate plausible parameter values through simulation. We provide an illustrative QBA example in a cohort of real-world patients with Newly Diagnosed Multiple Myeloma and provide practical guidance to apply QBA for outcome measurement error and interpret results.

Anaplastic lymphoma kinase (ALK) rearrangements have emerged as a defining molecular alteration across a wide spectrum of cutaneous mesenchymal and melanocytic neoplasms with diverse clinical, histologic, immunophenotypic, and biologic features. This article describes several distinct ALK-rearranged cutaneous neoplasms: epithelioid fibrous histiocytoma, superficial ALK-rearranged myxoid spindle cell neoplasm, nonneural granular cell tumor, Spitz melanocytic neoplasms, and other emerging entities. ALK overexpression by immunohistochemistry and confirmatory molecular testing, when necessary, plays a critical diagnostic role. The expanding spectrum of ALK-rearranged cutaneous tumors underscores the value of an integrated diagnostic approach to ensure accurate diagnosis and guide clinical management.

This article explores pigmented conjunctival lesions, emphasizing their clinical and histopathologic features. It highlights the diagnostic challenges posed by the overlapping clinical morphologies of benign, premalignant, and malignant melanocytic proliferations; biopsies of such lesions may be evaluated by dermatopathologists. This review discusses the distinct origins and molecular genetics of conjunctival melanocytic neoplasms, especially in sun-exposed areas like the bulbar conjunctiva, which share similarities with their cutaneous counterparts. Uveal melanoma, which is not the topic of this paper, is completely distinct molecular genetically and biologically. Understanding terminology used by ophthalmologists allows for more accurate communication regarding the managment and prognosis of patients with ocular melanocytic tumors.

Microvascular dysfunction plays a pivotal role in numerous diseases, often preceding clinical symptoms and structural changes. Ultrasound localization microscopy (ULM) is an emerging ultrasound imaging modality that enables in vivo visualization of microvascular structures with unprecedented resolution. This narrative review aimed to examine the recent clinical applications of ULM and its role in biomarker development. It was conducted following PRISMA 2020 guidelines and included 33 articles published up to November 2025, focusing on ULM in human studies. Inclusion criteria targeted studies evaluating ULM's clinical applications and biomarkers. Data extraction encompassed imaging protocols, biomarkers and outcomes, with study quality assessed using the Newcastle-Ottawa Scale. ULM demonstrates significant promise across various organs. In kidney applications, ULM and its novel variant, sensing ULM, identified glomeruli and microvascular density as biomarkers for kidney disease and allograft dysfunction. In the brain, transcranial ULM enabled microvascular mapping with a resolution of 25 μm, aiding the evaluation of Moyamoya disease. ULM has also shown potential in detecting inflammatory changes in the carotid artery, myocardial microcirculation and testicular vascular architecture. Oncology applications include monitoring tumor vascularity and therapy response, revealing early microvascular changes undetectable by conventional imaging. Future technical improvements, such as higher-frame-rate clinical scanners, real-time data processing and clinical 3D imaging capabilities, are necessary to overcome current limitations. To conclude, ULM is on the verge of clinical translation, offering significant potential for developing microvascular biomarkers across various tissues and diseases. The medical community must now adopt and refine ULM applications and establish their role in routine clinical practice.

Patients with multiple myeloma (MM) often use cardiovascular medications due to their increased risk of cardiovascular diseases. This study investigated the associations of baseline use of these drugs with survival and adverse events in MM patients initiating daratumumab, lenalidomide, or bortezomib combination treatments. Data from Phase III trials (CASTOR, MAIA, and POLLUX) were analysed, focusing on beta-blockers, calcium channel blockers, ACE inhibitors (ACEI), angiotensin II receptor blockers (ARBs), diuretics, and statins. Cox proportional hazard analysis and logistic regression were used to assess associations with survival and grade ≥ 3 adverse events. Among 1804 patients, ACEI/ARBs were most common (31%), followed by beta-blockers (23%), statins (21%), calcium channel blockers (17%), and diuretics (16%). ACEI/ARBs was associated with better progression-free survival (adjusted hazard ratio (aHR) [95% CI] = 0.84 [0.71-0.99], P = 0.034) but also higher odds of grade ≥ 3 adverse events (adjusted odds ratio (aOR) = 1.45 [1.06-1.97], P = 0.019). Diuretics were similarly associated with grade ≥ 3 adverse events (aOR = 1.53 [1.01-2.34], P = 0.047). Other cardiovascular drugs showed no significant associations. While ACEI/ARBs may improve progression-free survival, they pose safety concerns. It is reassuring that other cardiovascular drugs were not significantly associated with MM treatment outcomes. Further research is essential to fully understand the implications of these medications.

BACKGROUND: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.

METHODS: We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation ( r ) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS).

RESULTS: Here we show that models infer scores with moderate accuracy ( r  = 0.245-0.648; MAE = 1.6-12.0 points; mean MAE = 6.3). Highest correlations include Digit Span ( r  = 0.65; p < 0.001), Verbal Comprehension Index ( r  = 0.594; p < 0.001), and Matrix Reasoning ( r  = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199).

CONCLUSIONS: A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.

Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to low prevalence and limited clinician familiarity. Computational phenotyping offers a scalable approach to improving rare disease detection, but algorithm development is constrained by scarce high-quality labeled data. Expert-labeled datasets from chart reviews and registries are highly accurate but limited in scope, whereas labels derived from electronic health records (EHRs) provide broader coverage but are often noisy or incomplete. To efficiently leverage both sources, we propose WEST (WEakly Supervised Transformer) for rare disease diagnosis and subphenotyping from EHRs. At its core, WEST employs a weakly supervised transformer trained on a limited set of expert-validated labels and extensive probabilistic silver-standard labels-derived from structured and unstructured EHR features-that are iteratively refined across training rounds to improve model calibration. We evaluate WEST on two rare pulmonary conditions using EHR data from Boston Children's Hospital and show that it outperforms existing methods in phenotype classification, identification of clinically relevant subphenotypes, and prediction of disease progression. By reducing reliance on manual annotation, WEST enables label-efficient representation learning that supports accurate rare disease diagnosis and reveals deeper clinical insights from routine EHR data.

BACKGROUND AND OBJECTIVES: Alzheimer's disease and related dementias (ADRDs) are prevalent conditions that are stressful and elevate emotional distress in couples after diagnosis. Without treatment, emotional distress may become chronic and negatively affect couples' quality of life. We report results from an NIH Stage 1A open pilot of Resilient Together for Dementia (RT-ADRD), a novel, dyadic, skills-based intervention aimed at preventing chronic emotional distress in couples early after diagnosis. We describe results from our mixed-methods single arm feasibility study, including preliminary feasibility and acceptability of the intervention, and qualitative feedback from exit interviews. We also present exploratory analyses for change in outcomes and mechanisms of action.

METHODS: Six couples (N = 12 individuals) were recruited within six months of ADRD diagnosis by their diagnosing providers. Participants completed baseline assessments, participated in weekly RT-ADRD sessions together, then completed post-intervention assessments and one 60-min exit interview together.

RESULTS: RT-ADRD exceeded all a-priori feasibility and acceptability benchmarks (> 70%). Feedback from exit interviews suggested that participants had favorable impressions of the program and found the skills useful and relevant. Participants also offered perspectives on barriers and facilitators of engagement and program enhancement. In exploratory analyses, persons living with dementia exhibited significant reductions in perceived stress at post-intervention (p < .05; Cohens d > 0.8). Both persons living with dementia and their care partners exhibited statistically significant improvements in positive dyadic interactions measured by the Dyadic Relationship Scale (ps < .05); Cohens ds > 0.8).

CONCLUSIONS: RT-ADRD shows promise as a feasible and acceptable dyadic intervention delivered early after diagnosis. Results support a future NIH Stage 1B trial of RT-ADRD to establish definitive feasibility markers of both intervention and control before formal efficacy testing.

TRIAL REGISTRATION: This open pilot was registered on ClinicalTrials.gov (NCT06421545) on 05/20/2024.

OBJECTIVE: To synthesize magnetic resonance imaging (MRI) features and their reported diagnostic performance that differentiate benign from malignant soft-tissue tumors in alignment with the 2020 World Health Organization classification.

MATERIALS AND METHODS: A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Embase, Scopus, and the Cochrane Central Register of Controlled Trials were searched through July 2024. Eligible studies reported MRI feature frequencies or diagnostic accuracy for common soft-tissue tumor subtypes. Reviews, case reports, duplicates, non-English publications, and studies outside the scope were excluded. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).

RESULTS: Seventy-six studies met inclusion criteria. In lipomatous tumors, homogeneous fat signal and thin septa supported lipoma, whereas thick or nodular septa and enhancement favored atypical or well-differentiated liposarcoma. Myxofibrosarcoma often demonstrated an infiltrative fascial "tail." Vascular lesions included angioleiomyoma with a reticular T2 pattern and glomus tumor with marked T2 hyperintensity and avid enhancement. In peripheral nerve sheath tumors, lower apparent diffusion coefficient values and peritumoral edema favored malignancy. Heterogeneity in imaging protocols precluded meta-analysis; results were summarized descriptively by subtype.

CONCLUSION: Consolidated MRI patterns-such as septal morphology in lipomatous tumors, the fascial tail in myxofibrosarcoma, characteristic T2 patterns in vascular lesions, and diffusion and edema cues in nerve sheath tumors-support differentiation of benign and malignant entities, enhance reader confidence, and inform biopsy and management. Standardized prospective studies are needed to validate these thresholds and improve generalizability.