Prostate Cancer and Prostatic Diseases最新文献

Background: Current National Comprehensive Cancer Network guidelines define brachytherapy monotherapy as a suitable treatment for favorable intermediate risk (FIR) and unfavorable intermediate risk (UIR) prostate cancer. Our objective is to define the subgroup of patients suitable for brachytherapy monotherapy.

Methods: We conducted a single-institutional retrospective analysis of intermediate risk prostate cancer, treated with brachytherapy with or without androgen deprivation therapy (ADT) and/or external beam radiation therapy (EBRT). The primary endpoint was biochemical failure (BF), defined as prostate specific antigen (PSA) > 0.4 ng/mL. For monotherapy, multivariate Fine-Gray analysis was used to identify risk factors associated with BF. Univariate analysis was performed to evaluate whether ADT and/or EBRT were associated with BF for patients without and with such factors.

Results: Among 1622 patients, the median follow-up was 10.4 years. For monotherapy, PSA ≥ 10 ng/mL (adjusted sHR 3.01; 95% CI: 1.10-8.27; p = 0.032) and cT2b-c disease (adjusted sHR 4.52; 95% CI: 1.85-11.07; p = 0.001) were associated with BF. The 10-year incidences of BF after monotherapy for patients without and with these risk factors were 5.8% (3.8% FIR, 8.8% UIR) versus 17.2% (9.3% FIR, 23.9% UIR), respectively. For the cT1-T2a/PSA < 10 risk group, neither the addition of ADT (sHR 0.90; 95% CI: 0.38-2.1; p = 0.82) nor EBRT (sHR 0.65; 95% CI: 0.36-1.18; p = 0.16) was associated with biochemical failure. For the cT2b-T2c and/or PSA ≥ 10 subgroup, ADT (sHR: 0.33; 95% CI: 0.14-0.74; p = 0.007) but not EBRT (sHR 0.66; 95% CI: 0.34-1.31; p = 0.24) was associated with BF.

Conclusions: Brachytherapy monotherapy is suitable for all FIR, and UIR disease meeting cT1-T2a/PSA < 10 criteria.

Introduction: 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.

Methods: 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).

Results: 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.

Conclusion: 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.

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.