Introduction: Lymphocele (LC) formation is a common complication which may cause severe symptoms after robot-assisted radical prostatovesiculectomy (RARP) with concomitant pelvic lymph node dissection (PLND). Compared to open radical prostatectomy, the amount of data on potential risk factors for LC formation is still limited. The aim of the present study was to identify risk factors for symptomatic LC formation (sLC) after RARP with PLND. Methods: We used the data of a prospective multicentre series of 232 RARP patients which were treated between March 2017 and December 2017. The primary endpoint was the presence of sLC within 90 days. Asymptomatic LC (aLC) formation was also recorded. We evaluated clinical, perioperative, and histopathological criteria and compared their distribution in patients with and without post-operative sLC. Uni- and multivariable logistic regression analyses (MVAs) were performed to identify potential predictors for LC formation. Regarding the influence of patients’ BMI, 2 models were calculated: BMI continuously (model 1) and BMI dichotomized with cut-off 30 kg/m² (WHO definition, model 2). Results: Post-operative sLC was present in 21 patients (9.1%), while aLC was detected in 49 patients (21.1%) 90 days after RARP with PLND. Patients with sLC showed higher median baseline PSA levels (9.8 vs. 8.1 ng/mL), higher prevalence of obesity (BMI >30; 42.9 vs. 19.9%), and longer median console time (180 vs. 165 min) compared to patients without sLC. On MVA higher BMI {model 1: OR 1.145 (confidence interval [CI] 1.025–1.278); model 2: OR 2.761 (1.045–7.296)}, longer console time (model 1: OR 1.013 [1.005–1.021]; model 2: OR 1.013 [1.005–1.020]) and an ISUP grade ≥3 (model 1: OR 3.247 [1.182–8.917]; model 2: OR 2.791 [1.050–7.423]) were identified as independent predictors for sLC development. Conclusion: Patients with aggressive tumours and higher BMI should be informed about a potentially increased risk for sLC formation. In case of a long console time, a close and regular follow-up should be considered to check for LC development.

Introduction: Transurethral resection of the prostate (TURP) is the most frequently used treatment of benign prostate hyperplasia with a prostate volume of <80 mL. A long‐term complication is bladder neck contracture (BNC). The aim of the present study was to identify the risk factors for BNC formation after TURP.Methods: We conducted a retrospective analysis of all TURP primary procedures which were performed at one academic institution between 2013 and 2018. All patients were analyzed and compared with regard to postoperative formation of a BNC requiring further therapy. Uni‐ and multivariable logistic regression analyses (MVAs) were performed to identify possible risk factors for BNC development.Results: We included 1368 patients in this analysis. Out of these, 88 patients (6.4%) developed BNC requiring further surgical therapy. The following factors showed a statistically significant association with BNC development: smaller preoperative prostate volume (p = 0.001), lower resected prostate weight (p = 0.004), lower preoperative levels of prostate‐specific antigen (PSA, p < 0.001), shorter duration of the surgery (p = 0.027), secondary transurethral intervention (due to urinary retention or gross hematuria) during inpatient stay (p = 0.018), positive (≥100 CFU/mL) preoperative urine culture (p = 0.010), and urethral stricture (US) formation requiring direct visual internal urethrotomy (DVIU) postoperatively after TURP (p < 0.001), in particular membranous (p = 0.046) and bulbar (p < 0.001) strictures. Preoperative antibiotic treatment showed a protective effect (p = 0.042). Histopathological findings of prostate cancer (PCA) in the resected prostate tissue were more frequent among patients who did not develop BNC (p = 0.049). On MVA, smaller preoperative prostate volume (p = 0.046), positive preoperative urine culture (p = 0.021), and US requiring DVIU after TURP (p < 0.001) were identified as independent predictors for BNC development.Conclusion: BNC is a relevant long‐term complication after TURP. In particular, patients with a smaller prostate should be thoroughly informed about this complication.

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: Lymphoceles are a common postoperative complication after radical prostatectomy with pelvic lymphadenectomy. Therapeutic options include cannulation and drainage (CD), drainage and instillation (DI), or laparoscopic fenestration (LF). The aim of this study was to investigate the epidemiology of symptomatic lymphoceles (SLC) and evaluate the treatment options. Methods: We retrospectively analysed all patients who underwent robot-assisted radical prostatectomy (RARP) at our clinic from January 1, 2014 to December 31, 2018. All documented lymphoceles of these patients were recorded and analysed with regard to symptoms, possible infection and the treatment option (or options) chosen. Results: We were able to include all 1,029 patients who underwent RARP in the aforementioned period of time. Of these, 18.1% were diagnosed with a lymphocele either when discharged or when readmitted and 6.9% experienced an SLC requiring treatment. Thirteen-point-seven percent of patients readmitted with SLC showed an accompanying thrombosis. Due to recurring or bilateral SLCs receiving different treatment options for each side, there was a total of 115 SLCs treated. CD was carried out in 102 cases. Twenty-point-six percent of patients were sufficiently treated this way, the rest required further treatment or experienced recurrences not requiring further treatment. DI was carried out in 56 cases. Of those patients, 46.4% were sufficiently treated. LF was carried out in 54 cases (either after CD, or after DI, or primarily). Of those patients, 98.1% were treated sufficiently. LF had a statistically significant higher success rate compared to CD and DI (P<0.001 respectively). Conclusions: The study confirmed the significance of SLC as a common complication after RARP. LF turned out to be the most effective treatment option for SLC, while CD as well as DI have not been proven effective.

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.

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.