Current Urology最新文献

{"title":"Interleukin-11: A pivotal player and potential therapeutic target in prostate cancer.","authors":"Jinghua Zhong, Xiaolu Duan, Ziyi Wei, Wei Zhu, Zhijian Zhao, Guohua Zeng","doi":"10.1097/CU9.0000000000000323","DOIUrl":"10.1097/CU9.0000000000000323","url":null,"abstract":"<p><p>Interleukin-11 (IL-11), a pleiotropic cytokine belonging to the interleukin-6 (IL-6) family, is implicated in the initiation and progression of various malignancies. Recent studies revealed that IL-11 plays multifaceted roles in prostate cancer, contributing to tumor cell proliferation, castration resistance, bone metastasis, and chemotherapeutic resistance. Interleukin-11 has emerged as a promising target for both diagnostic and therapeutic strategies. This review outlines the molecular structure and biological functions of IL-11; summarizes its role in early diagnosis, prognostic evaluation, tumor progression, and therapeutic intervention for prostate cancer; and explores its potential as a novel therapeutic target.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"174-179"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From ChatGPT to UroGPT: A guideline-trained artificial intelligence model for male infertility.","authors":"Elie Kaplan-Marans, Yitzchak E Katlowitz, Michael West, Navid Leelani, Christopher Edwards, David Silver, Jacob Khurgin","doi":"10.1097/CU9.0000000000000328","DOIUrl":"10.1097/CU9.0000000000000328","url":null,"abstract":"<p><strong>Background: </strong>ChatGPT is not yet sufficiently reliable for answering clinical questions relevant to direct patient care. We hypothesized that a GPT model trained exclusively on expert guidelines would provide more accurate, guideline-concordant responses.</p><p><strong>Materials and methods: </strong>With permission from the European Association of Urology, we developed UroGPT, a custom GPT model trained solely on the European Association of Urology guidelines. We posed 25 clinical questions derived from the Male Infertility Guidelines and expert opinions to both the standard ChatGPT (GPT-4o) and UroGPT. Responses were anonymized and graded by 2 blinded reviewers as \"complete and accurate,\" \"incomplete but accurate,\" and \"incorrect or misleading.\" Guideline concordance was compared using the chi-square test.</p><p><strong>Results: </strong>UroGPT demonstrated significantly greater concordance with guideline-based responses than ChatGPT (<i>p</i> < 0.001). UroGPT provided 94% (47/50) complete and accurate responses, whereas ChatGPT provided only 38% (19/50). ChatGPT also produced a significantly higher rate of incorrect or misleading responses (52% vs. 4%). Inter-reviewer agreement was higher for UroGPT (88% vs. 48%), suggesting that its answers were clearer and more consistent with the guidelines. ChatGPT frequently overgeneralized, recommended unsupported interventions, or offered non-guideline-based lifestyle advice. However, both models failed to answer correctly 2 high-stakes questions regarding orchiectomy in patients with undescended testes.</p><p><strong>Conclusions: </strong>UroGPT markedly outperformed ChatGPT in guideline concordance. Training artificial intelligence models on expert-authored content represents a meaningful step toward developing clinically useful large language models. However, UroGPT is not yet appropriate for direct patient care and should currently be used only for research and academic purposes.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"135-140"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonfunctional ectopic adrenocortical adenoma dorsal to the kidney.","authors":"Wenxu Ma, Ziyuan Zhang, Liqiang Guo, Xuexing Zheng, Xigao Liu","doi":"10.1097/CU9.0000000000000324","DOIUrl":"10.1097/CU9.0000000000000324","url":null,"abstract":"","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"192-193"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of a deep learning-based predictive model for delayed graft function in kidney transplantation.","authors":"Yuhui He, Wenting Sun, Yisen Deng, Zhenshan Ding, Changyu Ma, Shuzhan Sun, Ying Zhao, Jianfeng Wang","doi":"10.1097/CU9.0000000000000336","DOIUrl":"10.1097/CU9.0000000000000336","url":null,"abstract":"<p><strong>Background: </strong>Delayed graft function (DGF) is a major complication of kidney transplantation that adversely affects long-term graft survival. This study aimed to develop and validate deep learning-based predictive models for DGF risk assessment in deceased donor kidney transplant recipients.</p><p><strong>Materials and methods: </strong>We retrospectively analyzed 670 consecutive patients who underwent deceased donor kidney transplantation at a single center between March 2018 and November 2023. The cohort was randomly divided into training (70%) and validation (30%) datasets. The class imbalance in the training set was addressed using a Synthetic Minority Oversampling Technique. Five deep learning algorithms were employed: bidirectional gated recurrent unit (BiGRU), Convolutional bidirectional long short-term memory, convolutional gated recurrent unit, convolutional neural network (CNN)-BiGRU, and CNN-bidirectional long short-term memory. The model performance was evaluated using receiver operating characteristic curve analysis with area under the curve (AUC), Matthews correlation coefficient, and F1 score metrics. Internal validation was performed using 1000 bootstrap iterations.</p><p><strong>Results: </strong>The study population comprised 670 deceased donor kidney transplant recipients with a mean age of 47.7 ± 11.2 years and a median preoperative serum creatinine of 907.1 (702.5-1113.8) μmol/L. The overall incidence of DGF was 21.8% (n = 146). Synthetic minority oversampling technique successfully addresses class imbalance in the training dataset. Among the 5 models evaluated, the CNN-BiGRU hybrid architecture demonstrated superior predictive performance with an AUC of 0.848 (95% confidence interval [CI] 0.798-0.899), Matthews correlation coefficient of 0.614 (95% CI, 0.609-0.619), and F1 score of 0.816 (95% CI, 0.789-0.843). The model achieved balanced discrimination, with 82.1% sensitivity and 83.5% specificity.</p><p><strong>Conclusions: </strong>The CNN-BiGRU-based prediction model demonstrated excellent performance in identifying patients at a high risk of DGF development. This artificial intelligence-powered tool offers the potential to assist the entire nephrology, urology, and transplant service communities in implementing personalized risk stratification and optimizing posttransplant management strategies to improve patient outcomes.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"148-154"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurobiology of urological diseases.","authors":"Boris Mravec, Jozef Dubravicky","doi":"10.1097/CU9.0000000000000330","DOIUrl":"10.1097/CU9.0000000000000330","url":null,"abstract":"<p><p>Knowledge obtained mainly in the last 2 decades has provided a better understanding of the mechanisms and pathways through which the nervous system regulates the function of tissues and organs whose diseases fall under the care of urologists. It has been demonstrated that the nervous system is involved not only in the maintenance of homeostasis in urological organs but also in the reparative and regenerative processes that take place in them. In addition, the nervous system is involved in the activation of compensatory and adaptive mechanisms in response to pathological processes occurring in these organs, but it may also potentiate the progression of urological diseases. Investigation of the mechanisms and pathways through which the nervous system exerts these influences was historically the domain of neuro-urology. Since it is now clear that the nervous system exerts more complex influences on urological organs, and that these organs in turn influence the functions of the nervous system, the term neurobiology of urological diseases is more appropriate for this area of research, which lies at the intersection of urology and neuroscience. The aim of this review is to introduce the concept of the neurobiology of urological diseases and to describe its implications in urology.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"165-173"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence, biomarkers, and imaging.","authors":"Helena Margot Flôres Soares da Silva, Juan Gómez Rivas, Paula Mata Déniz, María Jesús Marugan, Claudia González-Santander, Lorena Fernández Montarroso, Isabel Galante, Jesús Moreno Sierra","doi":"10.1097/CU9.0000000000000326","DOIUrl":"10.1097/CU9.0000000000000326","url":null,"abstract":"<p><strong>Background: </strong>Prostate cancer (PCa) diagnosis has historically relied on prostate-specific antigen (PSA) testing. Although PSA screening significantly reduces mortality rates, it is limited by its low specificity and the risk of overdiagnosis and overtreatment. These limitations highlight the need for more accurate diagnostic approaches that can be combined with PSA testing. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification.</p><p><strong>Materials and methods: </strong>This review comprehensively analyzes the current literature on the use of AI, machine learning, novel biomarkers, and imaging tools, particularly multiparametric magnetic resonance imaging and digital pathology, for the diagnosis of PCa. Studies on AI-driven image interpretation, lesion segmentation, radiomics, genomic classifiers, and multimodal data integration were evaluated. This study also considers the technical, regulatory, and ethical challenges related to the clinical implementation of AI technologies.</p><p><strong>Results: </strong>Artificial intelligence demonstrated significant utility in multiparametric magnetic resonance imaging interpretation, enhancing lesion detection, segmentation, and Gleason grading with high accuracy and reproducibility. In pathology, AI algorithms improve the diagnostic consistency of digital slides and assist with automated Gleason scoring. Genomic tools, such as Oncotype DX, when combined with AI, allow for individualized risk prediction. Multimodal models that integrate imaging, clinical, and molecular data outperform traditional PSA-based strategies and reduce unnecessary biopsies.</p><p><strong>Conclusions: </strong>The transition from PSA-centered to AI-driven, biomarker-supported, image-enhanced diagnosis marks a critical evolution in PCa care. While these technologies promise improved diagnostic accuracy compared with that with PSA alone, PSA will remain a foundation for model construction and risk stratification. Personalized treatment strategies and the successful clinical integration of AI depend on harmonized regulations, large-scale validation, equitable access, and transparent algorithm design. Future screening and treatment pathways for PCa are likely to be shaped by these multimodal precision diagnostic frameworks.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"127-134"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches to optimize the integration of sociodemographic factors for predicting cancer-specific survival among patients with high-risk prostate cancer.","authors":"Ismail Ajjawi, Isaac Elijah Kim, Shayan Smani, Peter Palencia, Gabriela M Diaz, William H Lee, Isaac Y Kim, Preston Sprenkle, Michael S Leapman","doi":"10.1097/CU9.0000000000000335","DOIUrl":"10.1097/CU9.0000000000000335","url":null,"abstract":"<p><strong>Background: </strong>Sociodemographic factors influence the outcomes of prostate cancer (PCa); however, they are rarely incorporated into clinical risk prediction models. This study aimed to assess whether machine learning approaches could optimize the integration of sociodemographic variables to improve the prediction of cancer-specific survival among patients with high-risk PCa.</p><p><strong>Materials and methods: </strong>Data from the Surveillance, Epidemiology, and End Results database were retrospectively analyzed to identify patients diagnosed with high-risk PCa from 2010 to 2020. Two random forest models were developed: one using clinical and pathological variables (age, stage, prostate-specific antigen level, Gleason grade, time to treatment, and year of diagnosis) and another incorporating available sociodemographic features (race, income, marital status, region, and urbanicity). Five-fold cross-validation was performed to evaluate the model performance and minimize overfitting. Hyperparameter tuning via a grid search optimized the model structure. Performance was assessed using the area under the receiver operating characteristic curve (AUC), Brier scores, sensitivity, and specificity. Parallel analyses were conducted using the XGBoost software. Clinical utility was evaluated using decision curve analysis.</p><p><strong>Results: </strong>We identified 80,858 patients with high-risk PCa. The clinical-only random forest model (AUC, 0.54) significantly improved with the addition of sociodemographic variables (AUC, 0.72; <i>p</i> < 0.001). The Brier score, sensitivity, and specificity were also superior in the combined model (all <i>p</i> < 0.001). Similar results were obtained for XGBoost. Gleason grade was the most predictive factor, whereas sociodemographic variables, particularly income and geographic region, were highly informative. Decision curve analysis demonstrated a higher net clinical benefit with the combined model.</p><p><strong>Conclusions: </strong>Incorporating sociodemographic variables into machine learning models significantly improved the prediction of cancer-specific survival in high-risk PCa, supporting their inclusion in risk stratification tools.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"141-147"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re: Patients with frailty, benign prostatic hyperplasia and indwelling bladder catheter: What are the 1-year outcomes after Rezūm therapy?","authors":"Sho Nonoyama","doi":"10.1097/CU9.0000000000000317","DOIUrl":"10.1097/CU9.0000000000000317","url":null,"abstract":"","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"194"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of male stress urinary incontinence treatments: A review of efficacy, safety, and clinical outcomes.","authors":"Sindhu Kosuru, Cosku Ozcelik, Thomas R Wong, Isaac Mordukhovich, Narmina Khanmammadova, Ilaha Isali","doi":"10.1097/CU9.0000000000000321","DOIUrl":"10.1097/CU9.0000000000000321","url":null,"abstract":"<p><strong>Background: </strong>Male stress urinary incontinence (MSUI) is a distressing condition that often results from radical prostatectomy, transurethral resection of the prostate, or other pelvic procedures. Despite the limitations of artificial urinary sphincters they have historically been considered the gold standard. Emerging treatments offer varying efficacies and safety levels. This study aimed to evaluate and compare the efficacy, safety, and clinical outcomes of current MSUI treatment modalities, including artificial urinary sphincters, adjustable transobturator male system (ATOMS), Pro Adjustable Continence Therapy, AdVance Non-adjustable Male Sling System, and duloxetine.</p><p><strong>Materials and methods: </strong>PubMed, covering prospective and retrospective analyses of MSUI treatments in postprostatectomy patients, was searched for relevant studies published between 2006 and 2024. Data on treatment efficacy (dry matter rate, improvement rate, and reduction in incontinence severity) and safety outcomes (complication and explanation rates) were extracted.</p><p><strong>Results: </strong>A total of 46 studies comprising 7841 patients were analyzed. Artificial urinary sphincters demonstrated the highest dryness rate (72.03%), whereas ATOMS had the highest improvement rate (85.56%) and the lowest surgical explantation risk (9.45%). Pro Adjustable Continence Therapy and AdVance yielded moderate efficacy, whereas duloxetine had the lowest complication rate (18.79%).</p><p><strong>Conclusions: </strong>Artificial urinary sphincters may be the most effective treatment for dryness, whereas ATOMS could offer high improvement rates with a lower risk of explantation and complications. Duloxetine demonstrated a strong safety profile as a pharmacological option, although the evidence remains limited. Given the heterogeneity of the existing studies, future prospective randomized trials are needed to refine treatment selection and optimize MSUI management.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"155-164"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intermediate-term follow-up and analysis of related factors associated with urinary incontinence and overactive bladder after laparoscopic radical prostatectomy-A multicenter cross-sectional study in China.","authors":"Le Shu, Dafei Weng, Yue Chen, Luwei Xu, Yiran Wang, Ziyang Liu, Gefei Chen, Ziwen Guo, Yanyan Feng, Huixing Pan, Peng Xue, Zhongqing Wei, Min Gu, Xiaobing Niu, Liucheng Ding","doi":"10.1097/CU9.0000000000000278","DOIUrl":"10.1097/CU9.0000000000000278","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to evaluate the incidence of urinary incontinence (UI) and overactive bladder (OAB) in prostate cancer patients 12 months after laparoscopic radical prostatectomy (LRP), through a multicenter follow-up. Additionally, the study sought to analyze the association between potential risk factors and the occurrence of these complications.</p><p><strong>Methods: </strong>This retrospective study included 382 patients who underwent LRP across 9 institutions in Jiangsu Province, China, between January 2019 and March 2020. Clinical data, including the Overactive Bladder Symptom Score, the International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form, magnetic resonance imaging findings, and the number of pads used, were collected 12 months postoperatively to assess the incidence of UI and OAB. Univariate and multivariate analyses were conducted to identify factors associated with UI and bladder overactivity at the 12-month follow-up.</p><p><strong>Results: </strong>Among the 382 patients included in the analysis, 135 (35.3%) patients remained affected by UI 12 months after LRP. Multivariate statistical analysis identified membranous urethral length (MUL), body mass index (BMI), and age as significant predictors of UI at the 12 - month postoperative mark. Additionally, 139 (36.4%) patients reported the presence of overactive bladder (OAB) 12 months post - operation. UI and OAB were found to be risk factors for each other.</p><p><strong>Conclusions: </strong>In our multicenter retrospective study, the prevalence of UI and OAB 12 months after LRP was significantly higher than previously reported in the literature. Age, BMI, and MUL were found to be associated with postoperative UI. Notably, preoperative MUL served as a protective factor against UI at 12 months post-surgery, while UI and OAB were identified as mutual risk factors. These findings underscore the importance of preventing post-prostatectomy UI to mitigate or avoid the occurrence of postoperative OAB. It should be noted that the loss-to-follow-up rate in this study was 39.7%, which may limit the interpretation and generalizability of the results.</p>","PeriodicalId":39147,"journal":{"name":"Current Urology","volume":"20 3","pages":"180-184"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13068483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}