Khan, R

8021 Background: Lung cancer is the leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and diagnostic variability in histopathological assessment. Advances in deep learning applied to medical imaging offer the potential to enhance early detection accuracy, reduce interobserver variability, and improve diagnostic efficiency in lung cancer. Methods: In this retrospective study, we evaluated DAVINCI using a dataset of 8,962 patients with confirmed lung malignancies. The model utilizes a specialized architecture combining convolutional neural networks (CNNs) and residual blocks to optimize feature extraction from high-resolution pathological slides. Performance was measured via detection accuracy, recall, specificity, and Area Under the Curve (AUC). Results: A total of 8,962 patients were included in the analysis. The DAVINCI model correctly identified lung cancer in 93% of cases, corresponding to 8,334 of 8,962 patients, with a 95% confidence interval (CI) of 86%–100% (7,707–8,962 patients). Model specificity was also 93% (95% CI, 86%–100%), indicating a high true-negative rate. Sensitivity (recall) was 87%, with correct detection in 7,797 patients and a 95% CI of 80%–94% (7,170–8,425 patients). The model demonstrated strong discriminatory performance, achieving an area under the curve (AUC) of 0.91 (95% CI, 0.84–0.98). In comparison, active pathologists achieved a mean diagnostic accuracy of 83%, corresponding to 7,438 of 8,962 cases, with a 95% CI of 76%–90% (6,811–8,066 cases). In terms of efficiency, DAVINCI processed whole-slide images in 16–19 seconds, substantially faster than conventional manual review, which typically requires several minutes per slide. Conclusions: DAVINCI demonstrated superior accuracy, robust discriminatory performance, and markedly faster processing compared with human review, supporting its potential role in early lung cancer detection at scale.

Abstract Rationale Obstructive sleep apnea (OSA) is a highly prevalent disorder characterized by recurrent upper airway collapse during sleep, leading to intermittent hypoxemia, sleep fragmentation, and substantial cardiovascular and neurocognitive morbidity. Continuous positive airway pressure (CPAP) therapy remains the gold standard for management, yet adherence is suboptimal due to discomfort, noise, and device intolerance. This has generated growing interest in pharmacologic alternatives that target upper airway physiology. The combination of atomoxetine, a selective norepinephrine reuptake inhibitor, and oxybutynin, an antimuscarinic agent, has emerged as a promising non-device therapeutic option by enhancing pharyngeal muscle tone and stabilizing ventilatory control. This meta-analysis systematically evaluates the efficacy and safety of atomoxetine plus oxybutynin in adults with OSA who are intolerant to CPAP. Methods A comprehensive search was conducted across PubMed, Embase, Scopus, Web of Science, and Cochrane Central from inception through June 2025. Eligible studies were randomized controlled trials (RCTs) comparing atomoxetine plus oxybutynin with placebo or control therapy in adults diagnosed with OSA. Data were extracted and pooled using Stata version 18.0. Mean differences and relative risks (RR) were calculated for continuous and dichotomous outcomes, respectively. Methodologic quality and risk of bias were assessed using the Cochrane Risk of Bias 2.0 tool. Results Eleven RCTs encompassing 196 adults (141 males, 55 females) were included, with a mean treatment duration of 1.38 days. Compared with control, atomoxetine plus oxybutynin significantly reduced the apnea-hypopnea index (AHI) (mean difference −10.16 events/hour; 95% CI − 20.32 to − 0.01; p = 0.05). Improvements were also observed in oxygen desaturation index (ODI), arousal index, and loop gain, though these did not achieve statistical significance. The treatment group experienced fewer adverse events (RR = 0.66; 95% CI 0.37-0.90; p = 0.01) and no increase in sleep disturbances (RR = 0.87; 95% CI 0.83-1.31). Conclusions Combination therapy with atomoxetine and oxybutynin significantly improves AHI and demonstrates a favorable safety and tolerability profile in adults with OSA who are unable to tolerate CPAP. These findings highlight the therapeutic potential of targeting upper airway muscle tone pharmacologically. Larger, long-term RCTs are warranted to confirm sustained efficacy, determine optimal dosing strategies, and clarify the role of this regimen within the evolving landscape of OSA management. This abstract is funded by: None