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PubMed2026

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans↗
PubMed2026

Recombinant vs Standard Influenza Vaccine in Adults With Severe Obesity: A Randomized Clinical Trial.

BACKGROUND: Individuals with severe obesity are at increased risk of severe influenza and may have impaired immune responses to vaccination. Recombinant influenza vaccine (RIV) may provide enhanced protection compared with egg-based standard-dose influenza vaccine (SD), but data in this high-risk population are limited. METHODS: The AP-HP FLUO trial (NCT05409612) was an open-label, randomized clinical trial conducted in 15 centers in France (November 2022-March 2023) with 6 months of follow-up. Adults with BMI &#x2265;35 kg/m2 were randomized 1:1 to receive RIV or SD, using minimization by center, age (<50 vs &#x2265;50 years), and BMI (<40 vs &#x2265;40 kg/m2). The primary outcome was the ratio (RIV/SD) of geometric mean hemagglutinin-inhibition (HAI) titers (GMTs) for 4 influenza strains 28 days after vaccination. Safety and reactogenicity were also assessed. RESULTS: A total of 206 participants were included (104 RIV, 102 SD). Median age was 50 years, 60.2% were women, and median BMI was 41.0 kg/m2. At Day 28, GMT ratios favored RIV for A/H1N1 (1.6; 95% CI, 1.1-2.3), A/H3N2 (2.0; 95% CI, 1.3-3.2), and B/Yamagata (1.3; 95% CI, 1.0-1.8), but not for B/Victoria (0.9; 95% CI, 0.6-1.3). The effect did not vary significantly across the different age and BMI groups. By Day 180, titers did not differ significantly. Reactogenicity and safety profiles were similar between groups. CONCLUSIONS: In adults with severe obesity, RIV elicited stronger short-term humoral immune responses than an egg-based standard-dose vaccine, suggesting potential additional benefit for influenza prevention in this vulnerable population.

Humans↗