Prostate Cancer and Prostatic Diseases最新文献

Purpose: Genomic classifiers are endorsed by guidelines and commonly used to inform prognosis in prostate cancer. We aimed to understand the distribution of genomic risk within the validated staging collaboration for cancer of the prostate (STAR-CAP) and propose a system integrating genomic and clinicopathologic risk. We hypothesized that genomic heterogeneity would have implications on risk estimates and may inform treatment decisions.

Materials and methods: Genomic risk was assessed using the Decipher genomic classifier in two separate multi-institutional, prospectively collected population-based registries: (1) Decipher Genomics Resource for Intelligent Discovery (GRID) [n = 50,891] and (2) Michigan Urological Surgery Improvement Collaborative (MUSIC-Decipher) [n = 1602]. The primary endpoint was estimated prostate cancer-specific mortality (PCSM), and secondary endpoint was distant metastasis (DM). Marginal risk estimates provided by STAR-CAP were combined with hazard ratios of Decipher to calculate integrated risk estimates.

Results: Median age and PSA was 68 years and 6.2 ng/mL in GRID, and 66 years and 10.5 ng/mL in MUSIC. The GRID population had 50.2%, 18.5%, and 31.4% with low-, intermediate-, and high-Decipher risk, compared to 48.0%, 16.2%, and 35.8% in MUSIC. Decipher-based genomic risk varied across STAR-CAP stages in both registries. Estimates of 10-year PCSM (0.1% to 48.8%) and DM (0.3%-72.9%) varied widely after integration of clinical-genomic risk. Use of an integrated Decipher-STAR-CAP system led to significant stage reclassification, including 23.4% upstaging and 45.6% downstaging at least one stage.

Conclusions: These findings suggest integration of genomic and clinicopathologic risk may lead to more nuanced risk assessment in prostate cancer and may help individualize treatment consideration.

Background: Despite the proven efficacy of androgen deprivation therapy (ADT) combined with androgen receptor pathway inhibitors (ARPIs) in metastatic castration-sensitive prostate cancer (mCSPC), many patients still receive ADT monotherapy due to safety concerns. This reliance on ADT monotherapy underscores the need for education on the comparative effectiveness and safety of available therapies versus ADT. We evaluated the efficacy and safety of alternative treatment combinations, incorporating final data from the recent ARANOTE Phase III trial.

Methods: We conducted network meta-analysis (NMA) to evaluate progression-free survival (PFS) and overall survival (OS), incorporating heterogeneity assessment through subgroup analyses. Additionally, we performed a separate class effect NMA. We analysed grade 3-5 adverse events (AEs), serious AEs, and discontinuation due to AEs. We estimated hazard ratios (HRs) for efficacy, rate ratios (RRs) for safety, 95% credible intervals (CrI), and the surface under the cumulative ranking area (SUCRA) to rank treatments by efficacy and safety.

Results: Darolutamide (DAR) + docetaxel (DOC) + ADT showed the highest effect size [HR of 0.27 (95% CrI: 0.18, 0.39)] and the highest ranking (SUCRA: 0.97) across the base case and several subgroups on the PFS outcome. On OS, DAR + DOC + ADT similarly achieved the lowest HR of 0.52 (0.43, 0.64) and the highest ranking (SUCRA of 0.95). Safety analyses showed that grade 3-5 AEs were more frequent with docetaxel combinations, with ABI + DOC + ADT having the highest risk of grade 3-5 AEs. DAR + ADT was ranked best on all safety outcomes, outperforming other doublets and comparable to ADT monotherapy.

Conclusions: This NMA supports the superior efficacy of ARPI combinations against ADT monotherapy, for both OS and PFS. While DAR + ADT demonstrated comparable efficacy to other doublet combinations, it offered a superior safety profile, making it an effective and safe option for managing patients with mCSPC.

Background: This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [¹⁸F]PSMA-1007 PET/CT to predict extracapsular extension (ECE) in prostate cancer (PCa), and to explore easy models integrating DL with clinical expertise.

Methods: A total of 388 patients who underwent radical prostatectomy were enrolled from centers A, B and C. Three DL models based on mpMRI, PET/CT, and a combined MPC model were developed and compared with a manual model based on the ECE grading system. Additionally, three combined models (mpMRI-M, PET/CT-M, and MPC-M) were constructed by integrating the DL models with the Manual model. To enhance clinical applicability, an easy model (E-MPC-M) was developed. Model performance was evaluated using the area under the receiver-operating-characteristic curve (AUC) and metrics derived from the confusion matrix. Gradient-weighted class-activation-mapping (Grad-CAM) was employed to visualize model interpretability.

Results: In the internal cohort, the Manual, MPC, and MPC-M models achieved AUCs of 0.752, 0.897, and 0.907, respectively; corresponding sensitivities were 0.616, 0.896, and 0.915, and specificities were 0.791, 0.740, and 0.802. In the external validation cohort, these models achieved AUCs of 0.665, 0.824, and 0.849; sensitivities of 0.318, 0.955, and 0.955; and specificities of 0.960, 0.600, and 0.640, respectively. The E-MPC-M model also showed robust performance, with an AUC of 0.862 in the internal cohort and 0.775 in the external cohort. Grad-CAM visualizations highlighted the model's focus on tumor-relevant regions, confirming effective learning of tumor features.

Conclusions: The MPC-M model demonstrated strong predictive performance for PCa ECE across internal and external cohorts, while the E-MPC-M model retained much of this performance with enhanced clinical practicality. However, these models should be considered as preliminary, and larger prospective multicenter studies are required to confirm their robustness and generalizability.

Background: The need for simple, non-invasive biomarkers for prostate cancer (PCa) diagnosis is increasing. Urinary PSA (uPSA) and Zinc (uZinc) are emerging as potential PCa risk indicators. This study aimed to develop a rapid urine test combining uPSA and uZinc, assessing its diagnostic value alone and with Standard of Care (SOC) parameters, including age, serum PSA, DRE, and multiparametric Magnetic Resonance Imaging (mpMRI).

Methods: We enrolled 260 men undergoing prostate biopsy. Post-massage urine samples were analyzed using a rapid urine test combining lateral flow immunoassay (uPSA) and colorimetric dipstick assay (uZinc) with confirmatory testing via ELISA and colorimetric in vitro assay. Logistic regression models (SOC, uPSA, uZinc, Urine test [uPSA + uZinc], SOC + Urine test) and mpMRI models were tested. Diagnostic accuracy was evaluated using AUCs from ROC analysis. A decision-making algorithm targeting patients with increased PSA up to 10 ng/mL, negative DRE, and PIRADS ≤ 3 was proposed to assess the number of unnecessary biopsies potentially avoided with the urine test.

Results: Among 242 evaluable patients, 146 (59%) had PCa. The rapid Urine test provided intensity scores inversely proportional to biomarker concentration. uPSA strongly correlated with clinical stage, D'Amico risk, and Gleason score, while uZinc showed a weaker trend. The Urine test reached an AUC of 0.769, which improved performance to 0.789 with SOC + Urine test (p = 0.0002). Combining urine markers with mpMRI yielded AUCs of 0.868 (mpMRI+Urine test) and 0.875 (mpMRI+SOC+Urine test; p < 0.0001). The decision-making algorithm integrating urine test demonstrated that 51% of men could safely avoid biopsy, with a 13% detection rate of only low-grade PCas (ISUP < 2) in this group.

Conclusions: This uPSA/uZinc urine test is a promising adjunct to current diagnostic pathways, improving accuracy in detecting clinically significant PCa while reducing unnecessary biopsies. Its integration with mpMRI and SOC parameters could refine risk assessment and personalize patient management.

Background: Prostate cancer is the second most commonly diagnosed cancer worldwide. Prostate biopsy, essential for definitive diagnosis, has evolved significantly with new technologies and techniques. Transrectal ultrasound-guided biopsy (TRUS-Bx) has been the gold standard but carries substantial infectious risks due to rectal mucosal penetration. Rising antibiotic resistance, emerging safety protocols, and novel imaging-guided methods have driven a shift toward safer alternatives.

Methods: Following PRISMA guidelines, we systematically searched PubMed and PubMed Central for studies published between 2014 and 2025 on prostate puncture complications. Eligible articles included original studies with ≥2 patients, emphasizing infectious complications, antibiotic prophylaxis, and modern innovations. From 639 records screened, 78 met inclusion criteria. Thematic synthesis was used to classify findings into complication types, prophylaxis approaches, and technological advancements.

Results: Infectious complications after TRUS-Bx ranged from 0.5 to 9.4% for sepsis and 0.3 to 4.9% for febrile urinary tract infections, largely driven by multidrug-resistant organisms and increased sampling density. Transperineal biopsy (TP-Bx), bypassing rectal flora, consistently reported infection rates <1%. Targeted prophylaxis based on rectal cultures, combination antibiotic regimens (e.g., fluoroquinolone with fosfomycin or ceftriaxone), and adjunct measures such as rectal cleansing significantly reduced post-biopsy infections. Technological innovations such as MRI-ultrasound fusion, robotic-assisted approaches, and PSMA PET/CT-guided techniques improved cancer detection rates (up to 71.8%) while maintaining low complication rates ( < 5%). Emerging non-antibiotic TP protocols and advanced anesthetic techniques further enhanced safety and patient tolerance.

Conclusions: Modern evidence supports a paradigm shift toward TP-Bx combined with targeted or multidrug prophylaxis to mitigate infectious risks. Imaging-guided and robotic-assisted techniques enhance diagnostic accuracy and safety but remain limited in resource-constrained settings. TRUS-Bx retains utility where TP access is unavailable; however, adapting infection prevention strategies is critical. Future large-scale trials and cost-effectiveness analyses are needed to optimize biopsy protocols globally.

Purpose: Non-White patients are poorly represented in prostate cancer trials. MRI PI-RADS scoring was developed in primarily White populations, but prostate cancer differs in non-White men. We aimed to explore differences in PI-RADS calibration for Asian and Black men.

Materials and methods: This is a secondary analysis of PREVENT, a multi-institutional study of infection rates for transrectal vs. transperineal biopsy. We compared cancer detection for self-identifying Asian and Black men. We compared detection rates on a per-person basis, stratified by index PI-RADS lesion, to White men, using Fisher's exact and logistic regression.

Results: Of 665/752 trial patients with PI-RADS 3-5 lesions, 88 (13%) were Black and 36 (6%) were Asian. Black men were younger at diagnosis with increased rates of overall (70% vs. 43%%, P = 0.004) and clinically significant prostate cancer (60% vs. 27%, P = 0.003) and Asian men had decreased rates of overall (0% vs. 47%, P = 0.004) and clinically significant prostate cancer (0% vs. 27%, P = 0.003) in PI-RADS 3 lesions compared to White men. On multivariable regression, Black men with PI-RADS 3/4 lesions had higher odds of overall (OR 1.17, P = 0.009) and clinically significant prostate cancer (OR 1.20, P = 0.004) and Asian men had lower odds of overall (OR 0.79, P = 0.01) but not clinically significant prostate cancer (OR 0.94, P = 0.5).

Conclusions: Black men with PI-RADS 3/4 lesions had 20% higher odds of clinically significant prostate cancer than White men while all PI-RADS 3 lesions in Asian men were negative. These findings suggest PI-RADS may require differential interpretation when assessing prostate cancer risk in non-White men.

Trial registration: Registered at ClinicalTrials.gov ( NCT04843566 , https://clinicaltrials.gov/study/NCT04843566 ).

Background: AI is increasingly integrated within prostate cancer diagnosis pathway.

Purpose: To provide estimates of diagnostic accuracy of AI assistance for clinically significant prostate cancer (csPCa) via MRI.

Materials and methods: A systematic search of PubMed, Embase, Cochrane, Scopus and Web of Science from January 2017 to October 2024 was performed for studies on the diagnostic utility of AI for prostate MRI. Diagnostic performance metrics were synthesized through hierarchical summary receiver operating characteristic modeling with random-effects assumptions. Specially, to test inferiority and potential superiority of AI, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), cancer detection rate (CDR), and accuracy was pairwisely compared between AI and radiologists in study level using odds ratios (ORs) with Z-statistics.

Results: 7398 patients from 29 studies with AI-vs-human pairwise comparison were included. When acting as an assistant to human readers, AI demonstrated superior performance compared to stand-alone human readers in diagnosing csPCa via MRI, specifically with higher sensitivity (86.5% vs 82.6%, P = 0.001), specificity (57.8% vs 50.0%, P = 0.028), PPV (64.3% vs 58.9%, P = 0.001), and NPV (82.9% vs 76.5%, P = 0.001) while maintaining comparable CDR (40.5% vs 38.6%, P = 0.093). When used as standalone readers, AI exhibited higher specificity (58.7% vs 48.7%, P = 0.026) but at the cost of reduced sensitivity (87.2% vs 90.1%, P = 0.017). Subgroup analysis indicated that readers of varying experience levels could all improve their diagnostic performance with AI assistance.

Conclusion: Integrating AI as an assistant in csPCa diagnostic workflows could enhance accuracy, particularly for less experienced readers.

Clinical trial registration information: Trial Name: The efficiency comparison of radiologists with or without assistance of artificial intelligence in prostate cancer diagnosis: a meta-analysis. Registration date: April 17, 2024.

Registration number: CRD42024533016. Registration information available at: https://www.crd.york.ac.uk/PROSPERO/ .