Background: We assessed a wide array of body composition parameters to identify those most relevant as prognostic tools for patients undergoing radical cystectomy (RC) due to bladder cancer (BC). Methods: In this retrospective, single-center study, preoperative computed tomography (CT) scans of 657 patients were measured at the level of the 3rd lumbar vertebra (L3) to determine common body composition indices including sarcopenia, myosteatosis, psoas muscle index (PMI), subcutaneous and visceral fat index (SFI and VFI), visceral-to-subcutaneous fat ratio (VSR), and visceral obesity. Predictors of overall survival (OS) and cancer-specific survival (CSS) were identified in univariate and multivariate survival analysis. Results: Sarcopenia and a low PMI were independently associated with shorter OS (Sarcopenia: HR 1.30; 95% CI 1.02–1.66; p = 0.04 and a low PMI: HR 1.32; 95% CI 1.02–1.70; p = 0.03) and CSS (Sarcopenia: HR 1.64; 95% CI 1.19–2.25; p < 0.01 and a low PMI: HR 1.41; 95% CI 1.02–1.96; p = 0.04). Myosteatosis, measured as decreasing average Hounsfield units of skeletal muscle, was an independent risk factor for OS (HR 0.98; 95% CI 0.97–1.00; p = 0.01) and CSS (HR 0.98; 95% CI 0.96–1.00; p < 0.05). The assessed adipose tissue indices were not significant predictors for OS and CSS. Conclusions: Sarcopenia, a low PMI, and myosteatosis are independent predictors for OS and CSS in patients undergoing radical cystectomy for bladder cancer.

Our aim was to provide a comprehensive analysis of the prevalence of potentially targetable activating FGFR3 alterations and their impact on oncological outcomes across different urothelial carcinoma (UC) stages. We retrospectively analyzed clinical data and FGFR3 results for 1509 formalin-fixed, paraffin-embedded tissue specimens. Actionable activating FGFR3 mutations were assessed using a well-established multiplex SNaPshot polymerase chain reaction approach. FGFR3 fusion testing was performed with a Qiagen Therascreen kit. In the study population of 1509 patients, 202 (13%) had stage pTa, 380 (25%) had stage pT1, 258 (17%) had localized muscle-invasive bladder cancer (MIBC), 556 (37%) had locally advanced MIBC, 91 (6.0%) had metastatic UC of the bladder (mUCB), and 22 (1.5%) had metastatic upper tract UC (mUTUC). Of the FGFR3 alterations detected in 373 patients (25%), 104 (52%) were in stage pTa, 158 (42%) were in pT1, 42 (16%) were in localized MIBC, 53 (9.5%) were in locally advanced MIBC, nine (9.9%) were in mUCB, and seven (32%) were in mUTUC. FGFR3 alterations were associated with better progression-free survival and overall survival in the overall population (p < 0.001), but not in subgroup analyses for different disease stages. Study limitations include the retrospective design and heterogeneous patient cohort. FGFR3 alterations occur at a stage-dependent frequency and are more prevalent in lower tumor stages. We were unable to demonstrate an independent prognostic effect of FGFR3 alterations on oncological outcomes after adjusting for tumor stage. We analyzed a protein called fibroblast growth factor receptor 3 (FGFR3) in patients with cancer of the urinary tract. We found that more aggressive tumors had fewer genetic changes in FGFR3 in comparison to less aggressive tumors. However, genetic changes in FGFR3 were not related to survival for these patients.

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This preparatory study establishes a robust methodological foundation for the forthcoming CONCORDIA trial, including the validation of the System Causability Scale (SCS) and its modified version (mSCS), as well as the selection of LLMs for urological cancer treatment recommendations based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen’s K > 0.74), and internal consistency (all Cronbach’s Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability (p < 0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials.

Background: The increasing volume and complexity of biomedical literature pose challenges for making scientific knowledge accessible to lay audiences. Lay summaries, now widely encouraged or required by journals, aim to bridge this gap by promoting health literacy, patient engagement, and public trust. However, many are written by scientists without formal training in plain-language communication, often resulting in limited clarity, readability, and consistency. Generative large language models such as ChatGPT-4 offer a scalable opportunity to support lay summary creation, though their effectiveness within specific clinical domains has not been systematically evaluated at scale. Objective: This study aimed to assess ChatGPT-4’s performance in generating lay summaries for prostate cancer studies. A secondary objective was to evaluate how prompt design influences summary quality, aiming to provide practical guidance for the use of generative artificial intelligence (AI) in scientific publishing. Methods: A total of 204 consecutive articles on prostate cancer were extracted from a high-ranking oncology journal mandating lay summaries. Each abstract was processed with ChatGPT-4 using 2 prompts: a simple prompt based on the journal’s guidelines and an extended prompt refined to improve readability. AI-generated and original summaries were evaluated using 3 criteria: readability (Flesch-Kincaid Reading Ease [FKRE]), factual accuracy (5-point Likert scale, blinded rating by 2 clinical experts), and compliance with word count instructions (120‐150 words). Summaries were classified as high-quality as a composite outcome if they met all 3 benchmarks: FKRE >30, accuracy ≥4 from both raters, and word count within range. Statistical comparisons used Wilcoxon signed-rank and paired 2-tailed t tests ( P <.05). Results: ChatGPT-4-generated lay summaries showed an improvement in readability compared to human-written versions, with the extended prompt achieving higher scores than the simple prompt (median FKRE: extended prompt 47, IQR 42-56; simple prompt 36, IQR 29-43; original 20, IQR 9.5‐29; P <.001). Factual accuracy was higher for the AI-generated lay summaries compared to originals (median factual accuracy score: extended prompt 5, IQR 5-5; simple prompt 5, IQR 5-5; original 5, IQR 4-5; P <.001) in this dataset. Compliance with word count instructions was greater for both AI-generated summaries in comparison to originals (wrong number of words; extended prompt 39 (19%), simple prompt 40 (20%), original 140 (69%) ; P <.001). Between simple and extended prompts, there were no significant differences in accuracy ( P =.53) and word count compliance ( P =.87). The proportion rated as high-quality was 79.4% for the extended prompt, 54.9% for the simple prompt, and 5.4% for original summaries ( P <.001). Conclusions: With optimized prompting, ChatGPT-4 produced lay summaries that, on average, scored higher than author-written versions in readability, factual accuracy, and structural compliance within our dataset. These results support integrating generative AI into editorial workflows to improve science communication for nonexpert audiences. Limitations include focus on a single clinical domain and journal, and absence of layperson evaluation.