The power of us: breaking barriers and bridging the gap of ultrasound in rheumatology to empower a new generation.

OBJECTIVE: This study assesses musculoskeletal ultrasound (MSUS) knowledge, attitudes, and practices among young rheumatologists in Mexico, aiming to identify barriers and facilitators to its clinical use. METHODS: An online survey distributed to a network of young rheumatologists captured demographics, institutional, and personal MSUS information. Multivariable analysis identified factors associated with positive MSUS attitudes. RESULTS: Ninety-six rheumatologists (39.18% national response rate) completed the survey. Of respondents (54.2% females, median age 35.1 years), 81.2% deemed MSUS necessary in clinical rheumatology. The main barriers included limited training access (56.2%) and required training time (54.1%). Lack of scientific evidence was not a major barrier (60.4%). Positive MSUS attitudes were associated with learning from conferences (p = 0.029) and colleagues (p = 0.005), formal (p = 0.043), and in-person training (p = 0.020), MSUS use in practice (p = 0.027), and use by radiologists in their institute (p < 0.001). Interest in learning MSUS (88.5%) was significantly higher in those with positive attitudes (94.4%, p < 0.001). Elastic net analysis identified key drivers, including learning MSUS from conferences, colleagues, and in residency; using MSUS in practice; respondent-performed MSUS; and MSUS use by radiologists. Statistically significant associations were found with using MSUS for synovitis/inflammatory joint disease (OR = 1.43, 95% CI 1.00-2.05) and MSUS use by radiologists in respondent's institutes (OR = 1.70, 95% CI 1.20-2.90). CONCLUSION: Most young rheumatologists in Mexico recognize the necessity of MSUS in clinical practice. By addressing identified barriers, encouraging rheumatologist-radiologist collaboration, and establishing a regulatory body to certify rheumatologist's MSUS experience, there is an opportunity to empower them with the necessary skills for effective MSUS use, ultimately benefiting patient care.

MuTATE-an R package for comprehensive multi-objective molecular modeling.

MOTIVATION: Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine-learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine-learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation. RESULTS: We developed Multi-Target Automated Tree Engine (MuTATE) for automated and comprehensive molecular modeling, which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level. AVAILABILITY AND IMPLEMENTATION: MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.

Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis.

PURPOSE: Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. METHODS: We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. RESULTS: A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (߈ = 0.66; P = .048) on survival (߈ = 0.37; P = .008). CONCLUSION: Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.