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.

Radical nephroureterectomy (NUE) is the gold standard treatment for high-risk urothelial cancer of the upper urinary tract (UTUC). Besides sarcopenia and frailty, fat distribution is moving increasingly into focus. Components of body composition were assessed in patients undergoing NUE due to UTUC. The study cohort included 142 patients. By using CT-based measurements, the skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), and visceral adipose tissue index (VATI) were measured at the height of the third lumbar vertebra. Overall survival (OS) and cancer-specific survival (CSS) were estimated using univariable und multivariable Cox regression models. The prevalence of sarcopenia in the study population (n = 142) was 37%. OS and CSS were significantly reduced in sarcopenic patients. In the multivariable cox regression analysis, including age, ACE-27, T-stage, R-stage, LVI and necrosis, sarcopenia remained a significant risk factor of OS (HR, 1.77; 95% CI 1.02–3.07; p = 0.042) and CSS (HR, 2.17; 95% CI 1.18–3.99; p = 0.012). High visceral adipose tissue seems to be protective, although not statistically significant. Sarcopenia is a comorbidity-independent risk factor in patients who underwent NUE due to UTUC. Visceral fat represents a potentially protective factor. These results suggest that specific factors of body composition can be used for better risk stratification.

Clear and accessible lay summaries are essential for enhancing the public understanding of scientific knowledge. This study aimed to evaluate whether ChatGPT-4 can generate high-quality lay summaries that are both accurate and comprehensible for prostate cancer research in Current Oncology. To achieve this, it systematically assessed ChatGPT-4’s ability to summarize 80 prostate cancer articles published in the journal between July 2022 and June 2024 using two distinct prompt designs: a basic “simple” prompt and an enhanced “extended” prompt. Readability was assessed using established metrics, including the Flesch–Kincaid Reading Ease (FKRE), while content quality was evaluated with a 5-point Likert scale for alignment with source material. The extended prompt demonstrated significantly higher readability (median FKRE: 40.9 vs. 29.1, p < 0.001), better alignment with quality thresholds (86.2% vs. 47.5%, p < 0.001), and reduced the required reading level, making content more accessible. Both prompt designs produced content with high comprehensiveness (median Likert score: 5). This study highlights the critical role of tailored prompt engineering in optimizing large language models (LLMs) for medical communication. Limitations include the exclusive focus on prostate cancer, the use of predefined prompts without iterative refinement, and the absence of a direct comparison with human-crafted summaries. These findings underscore the transformative potential of LLMs like ChatGPT-4 to streamline the creation of lay summaries, reduce researchers’ workload, and enhance public engagement. Future research should explore prompt variability, incorporate patient feedback, and extend applications across broader medical domains.