Prostate Cancer and Prostatic Diseases最新文献

{"title":"Re: Evaluating PI-RADS lesions and clinically significant prostate cancer in Black and Asian men: a PREVENT randomized clinical trial secondary analysis.","authors":"Laith Baqain, Mohammed Shahait","doi":"10.1038/s41391-025-01020-4","DOIUrl":"10.1038/s41391-025-01020-4","url":null,"abstract":"","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcomes of robot-assisted radical prostatectomy with novel robotic platforms vs da Vinci multiport systems: a systematic review and network meta-analysis.","authors":"Francesco Chierigo, Giuseppe Fallara, Massimiliano Depalma, Marco Tozzi, Alberto Quistini, Roberto Bianchi, Martina Maggi, Guglielmo Mantica, Cosimo De Nunzio, Rocco Damiano, Alessandro Veccia, Alessandro Antonelli, Francesco Porpiglia, Pierre Karakiewicz, Riccardo Autorino, Bernardo Rocco, Matteo Ferro","doi":"10.1038/s41391-025-01023-1","DOIUrl":"https://doi.org/10.1038/s41391-025-01023-1","url":null,"abstract":"<p><strong>Introduction: </strong>The introduction of novel robotic platforms has expanded surgical options for robot-assisted radical prostatectomy (RARP). However, comparative outcomes with da Vinci multiport (MP) system remain unclear. This systematic review and network meta-analysis aimed to compare perioperative, early oncological, and functional outcomes of RARP performed with novel robotic platforms versus the da Vinci MP system.</p><p><strong>Methods: </strong>A systematic literature search was conducted in PubMed, Scopus, and Embase (updated December 22, 2024) following PRISMA guidelines. Eligible studies compared RARP performed with alternative robotic platforms versus da Vinci MP, reporting perioperative, oncological, or functional outcomes. A network meta-analysis was conducted using a random-effects model. Outcomes were expressed as mean differences for continuous variables and odds ratios (OR) for dichotomous variables, with 95% confidence intervals (CI).</p><p><strong>Results: </strong>Thirty-three studies for a total of 5987 patients were included. Compared to da Vinci MP, da Vinci SP had lower odds of lymph node dissection (OR 0.39, 95% CI 0.26-0.61) and nerve-sparing (OR 0.11, 95% CI 0.02-0.61) but was associated with shorter catheterization (-1.18 days, 95% CI -2.05 to -0.31) and hospital stay (-0.68 days, 95% CI -1.05 to -0.31). Versius, KangDuo, and SHURUI SP had significantly longer operative times (MD 74.00, 95% CI 42.49-105.51; MD 53.96, 95% CI 18.26-89.67; MD 103.88, 95% CI 69.99-137.78, respectively). Hugo RAS had higher intraoperative malfunction rates (OR 6.53, 95% CI 2.17-19.63). Positive surgical margin rates were lower for da Vinci SP (OR 0.70, 95% CI 0.53-0.92) but higher with the perineal approach (OR 6.30, 95% CI 1.53-25.94). PSA persistence, biochemical recurrence, continence and erectile function rates were comparable across platforms.</p><p><strong>Conclusion: </strong>This is the first network meta-analysis comparing robotic platforms for RARP. While perioperative differences exist, oncological and functional outcomes appear comparable. Future studies should address learning curve effects, cost-effectiveness, and long-term functional outcomes to optimize robotic platform selection.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the indication for adjuvant radiation therapy according to EAU guidelines among patients with high-risk prostate cancer: a novel multivariable model.","authors":"Carolin Siech, Helge von Kriegstein, Mike Wenzel, Cristina Cano Garcia, Quynh Chi Le, Pierre Tennstedt, Felix Preisser, Tobias Maurer, Maximilian Kriegmair, Felix K H Chun, Markus Graefen, Derya Tilki, Philipp Mandel","doi":"10.1038/s41391-025-01018-y","DOIUrl":"https://doi.org/10.1038/s41391-025-01018-y","url":null,"abstract":"<p><strong>Background: </strong>To develop a novel model for preoperatively predicting the indication for adjuvant radiation therapy after radical prostatectomy according to current guideline recommendations of the European Association of Urology (EAU) based on patient and clinical tumor characteristics in high-risk prostate cancer patients.</p><p><strong>Methods: </strong>Within a high-volume center database (01/2010-08/2024), we identified high-risk prostate cancer patients. Univariable logistic regression models addressed indication for adjuvant radiation therapy. Multivariable logistic regression models included the most informative, statistically significant preoperative predictors. Harrell's concordance index (c-index) quantified accuracy after 2000 bootstrap resamples for internal validation.</p><p><strong>Results: </strong>Of 5691 patients, 2137 (38%) had indication for adjuvant radiation therapy according to current EAU guidelines (2025). Indication for adjuvant radiation therapy was associated with higher prostate volume (> 45 cm³ and 25-45 cm³) and advanced tumor characteristics, namely higher prostate-specific antigen value (>20 ng/ml and 10-20 ng/ml), advanced clinical tumor stage (cT3/4 and cT2), lower number of sampled biopsy cores (≤ 12), higher proportion of positive cores (continuous), and higher Gleason Grade Group in biopsy (5, 4, and 3). No association was observed for age and body-mass index and indication for adjuvant radiation therapy. Multivariable model c-index for the prediction of the indication for adjuvant radiation therapy was 0.761 (95% confidence interval 0.749-0.776).</p><p><strong>Conclusions: </strong>Clinical tumor characteristics can be used for preoperatively predicting the indication for adjuvant radiation therapy after radical prostatectomy according to current EAU guideline recommendations in high-risk prostate cancer patients. Prior to clinical application, the present multivariable model should be externally validated within an independent cohort.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to correspondence regarding \"The association between statin use, genetic variation, and prostate cancer risk\".","authors":"Ali Amiri, Robert J Hamilton","doi":"10.1038/s41391-025-01019-x","DOIUrl":"https://doi.org/10.1038/s41391-025-01019-x","url":null,"abstract":"","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical impact of MRI-based risk calculators for prostate cancer diagnosis: a systematic review and meta-analysis.","authors":"Ciarán Courtney O'Toole, Nancy Fosua Boakye, Ailish Hannigan, Amirhossein Jalali","doi":"10.1038/s41391-025-01014-2","DOIUrl":"https://doi.org/10.1038/s41391-025-01014-2","url":null,"abstract":"<p><strong>Background: </strong>Prostate cancer (PCa) is the second most common cancer among men worldwide. Current diagnostic methods often lack sufficient sensitivity and specificity, leading to unnecessary biopsy. With growing use of MRI and EAU guideline recommendations, this review synthesised evidence on MRI-based risk calculators (RCs) for PCa diagnosis and compared their performance with traditional clinical RCs.</p><p><strong>Methods: </strong>A systematic search of Embase, Medline, Scopus, Cochrane Library, and Web of Science databases assessed the discriminatory ability of MRI-based RCs using Area Under the Curve (AUC). A meta-analysis was conducted to pool AUC estimates, assess heterogeneity, and compare the differences in discriminatory ability.</p><p><strong>Results: </strong>Of 2049 papers, 16 met the inclusion criteria. MRI-based RCs showed increased discrimination, with an AUC of 0.84 (95% CI: 0.81-0.86) for clinically significant PCa (csPCa), compared to 0.76 (95% CI: 0.73-0.79) for clinical models, and an AUC of 0.81 (95% CI: 0.78-0.84) for all PCa, compared to 0.74 (95% CI: 0.68-0.79). The pooled logit(AUC) difference was 0.49 units for csPCa and 0.37 units for all PCa. High heterogeneity was noted, likely due to PCa variability, and 31% of the studies had a high or unclear risk of bias, potentially affecting generalisability.</p><p><strong>Conclusions: </strong>MRI-based RCs improve the diagnostic accuracy for PCa with the potential to reduce unnecessary biopsies and optimise healthcare resources, thereby supporting their integration into clinical practice.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re: Endoscopic enucleation of the prostate versus transurethral resection of the prostate for benign prostatic hyperplasia: a systematic review and meta-analysis.","authors":"Rachana Mehta, Ranjana Sah","doi":"10.1038/s41391-025-01016-0","DOIUrl":"https://doi.org/10.1038/s41391-025-01016-0","url":null,"abstract":"","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Re: does biodegradable peri-rectal spacer mitigate treatment toxicities in radiation therapy for localized prostate cancer-a systematic review and meta-analysis.\"","authors":"Yung-Chi Shih, Shang-Ju Hsieh","doi":"10.1038/s41391-025-01012-4","DOIUrl":"https://doi.org/10.1038/s41391-025-01012-4","url":null,"abstract":"","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.","authors":"Renning Zheng, Nadine A Friedrich, Michael Luu, Rebecca Gale, Dmitry Khodyakov, Stephen J Freedland, Brennan Spiegel, Timothy J Daskivich","doi":"10.1038/s41391-025-01011-5","DOIUrl":"https://doi.org/10.1038/s41391-025-01011-5","url":null,"abstract":"<p><strong>Background: </strong>AUA guidelines for shared decision making (SDM) in prostate cancer recommend discussion of five content areas in consultations: (1) cancer severity (tumor risk (TR), pathology results (PR)); (2) life expectancy (LE); (3) cancer prognosis (CP); (4) baseline urinary and erectile function (UF and EF); and (5) treatment side effects (erectile dysfunction (ED), urinary incontinence (UI), and irritative urinary symptoms (LUTS)). However, patient retention of information after the visit and inconsistent risk communication by physicians are barriers to informed SDM. We sought to develop natural language processing (NLP) models based on recorded consultations to provide key information to patients and audit quality of physician communication.</p><p><strong>Methods: </strong>We used 50 consultation transcripts to train and validate NLP models to identify sentences related to key concepts. We then tested whether communication quality across entire consultations could be determined by sentences with the highest model-predicted topic concordance in 20 separate consultation transcripts.</p><p><strong>Results: </strong>Our development dataset included 28,927 total sentences, with 75% reserved for training and 25% for internal validation. The Random Forest model had the highest accuracy for identifying topic-concordant sentences, with area under the curve 0.98, 0.94, 0.89, 0.92, 0.84, 0.96, 0.98, 0.97, and 0.99 for TR, PR, LE, CP, UF, EF, ED, UI, and LUTS compared with manual coding across all concepts in the internal validation dataset. In 20 separate consultations, the top 10 model-identified sentences correctly graded communication quality across entire consultations with accuracies of 100%, 90%, 95%, 95%, 80%, 95%, 85%, 100%, and 95% for TR, PR, LE, CP, UF, EF, ED, UI, and LUTS compared with manual coding, respectively.</p><p><strong>Conclusions: </strong>NLP models accurately capture key information and grade quality of physician communication in prostate cancer consultations, providing the foundation for scalable quality assessment of risk communication.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.","authors":"Khi Yung Fong, Vineet Gauhar, Thomas R W Herrmann, Carlotta Nedbal, Dmitry Enikeev, Jeremy Yuen-Chun Teoh, Sarvajit Biligere, Steffi Kar Kei Yuen, Daniele Castellani, Bhaskar Kumar Somani, Patrick Juliebø-Jones, Valerie Huei Li Gan, Edwin Jonathan Aslim, Ee Jean Lim","doi":"10.1038/s41391-025-01015-1","DOIUrl":"10.1038/s41391-025-01015-1","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) and artificial intelligence (AI) have demonstrated powerful functionality in the healthcare setting thus far. We aimed to construct an AI model to predict postoperative incontinence after enucleation surgery for benign prostatic hyperplasia (BPH).</p><p><strong>Methods: </strong>Data were taken from two BPH registries and split into training and validation datasets. The following characteristics were used as predictors of incontinence: age, prostate volume, preoperative IPSS, QoL score, Qmax and post-void residual; presence of preoperative indwelling catheter, early apical release (EAR), enucleation type (2-lobe, 3-lobe, or en-bloc), and laser energy type. Six types of ML models were constructed using the training dataset and applied to the validation dataset to assess their accuracy.</p><p><strong>Results: </strong>3828 patients from both databases were analyzed. Median age was 68, median prostate volume was 85.5 cc. 5.4% had a preoperative indwelling catheter. The commonest enucleation type was 2-lobe, the commonest energy type was Thulium fiber laser, and EAR was performed in 34.0%. Of the six ML models tested, extreme gradient boosting with manual fine tuning was the best-performing with an accuracy of 86.2%, sensitivity of 96.8%, specificity of 23.7%, PPV of 88.2%, and NPV of 55.9%.</p><p><strong>Conclusions: </strong>We hereby present an ML model for incontinence prediction post-surgery for BPH. Its main strengths are high sensitivity and PPV, meaning that if a patient is predicted to be incontinent using this model, this is likely to reflect the eventual outcome. This allows clinicians to pay closer attention on follow-up to detect and manage postoperative incontinence expediently.</p>","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144883545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Natural killer cell infiltration in prostate cancers predict improved patient outcomes.","authors":"Nicholas A Zorko, Allison Makovec, Andrew Elliott, Samuel Kellen, John R Lozada, Ali T Arafa, Martin Felices, Madison Shackelford, Pedro Barata, Yousef Zakharia, Vivek Narayan, Mark N Stein, Kevin K Zarrabi, Akash Patnaik, Mehmet A Bilen, Milan Radovich, George Sledge, Wafik S El-Deiry, Elisabeth I Heath, Dave S B Hoon, Chadi Nabhan, Jeffrey S Miller, Justin H Hwang, Emmanuel S Antonarakis","doi":"10.1038/s41391-025-01009-z","DOIUrl":"https://doi.org/10.1038/s41391-025-01009-z","url":null,"abstract":"","PeriodicalId":20727,"journal":{"name":"Prostate Cancer and Prostatic Diseases","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144837481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}