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Abstract Background The Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only. Results We identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects. Conclusions Across heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations.

PURPOSE Randomized clinical trials (RCTs) have shown progression-free survival (PFS) benefits of metastasis-directed therapy (MDT) without androgen deprivation therapy for oligometastatic castration-sensitive prostate cancer (omCSPC). Most patients with bone metastatic (BM) omCSPC recur with additional bone disease after MDT. We hypothesized the BM-targeting alpha-emitter radium-223 dichloride (Ra223) could target subclinical bone disease and delay progression. METHODS This is an investigator-initiated, multicenter, open-label phase II RCT. Eligible men with recurrent omCSPC with ≥one bone metastasis (≤three on conventional imaging and/or ≤five on molecular imaging) were randomly assigned (1:1) to stereotactic ablative radiation (SABR) MDT alone or SABR MDT with Ra223 (six cycles). Primary end point was composite PFS. RESULTS From August 9, 2019, to March 2, 2023, 64 patients were randomly assigned, 33 to SABR MDT and 31 to SABR MDT/Ra223 balancing for key covariates. Most SABR MDT/Ra223 patients (87%) received six cycles of Ra223. The median PFS was 11.8 months with SABR MDT and 10.5 months with SABR MDT/Ra223 (adjusted hazard ratio [aHR], 1.42 [95% CI, 0.79 to 2.56]; P = .24). Seven patients (11%) experienced grade 3 treatment-related adverse events (no grade 4 or 5), 2 of 33 (6%) with SABR and 5 of 30 (17%) with SABR MDT/Ra223. Patients with high-risk (HiRi) pathogenic mutations in ATM , BRCA1/2 , RB1 , or TP53 had worse PFS (HR, 5.95 [95% CI, 1.83 to 19.3]; P = .003). Greater T-cell receptor (TCR) unique productive rearrangements were prognostic for improved PFS independent of the treatment arm (aHR, 0.45 [95% CI, 0.21 to 0.96]; P = .04). CONCLUSION Adding Ra223 to SABR MDT in BM omCSPC does not delay progression of disease. We provide evidence for an HiRi mutational signature and TCR repertoire as prognostic biomarkers in omCSPC treated with SABR MDT, highlighting the importance of collecting biological correlates in RCTs for omCSPC.

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.