European urology oncology最新文献

Background and objective: The role of anatomical factors in predicting outcomes after radical prostatectomy (RP) remains unclear. This review aims to evaluate the impact of various anatomical factors on the perioperative outcomes of patients undergoing RP for localized prostate cancer (PCa).

Methods: A comprehensive literature search was conducted through January 2024 using the PubMed/Medline, Embase, and Web of Science databases. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed to identify eligible studies. Data were extracted and pooled for a meta-analysis, with outcomes including operative time, blood loss, transfusion rates, overall complications, and positive surgical margins (PSMs). Heterogeneity was assessed using Cochrane Q test, and subgroup analyses were conducted to explore the influence of surgical approach.

Key findings and limitations: A total of 91 studies met our inclusion criteria. Among the anatomical factors, prostate volume (PV), prostate weight, and median lobe (ML) were suitable for the meta-analysis. Larger prostates were associated with increased operative time, blood loss, and complication rates, but with fewer PSMs (all p < 0.05). ML presence was not associated with a higher risk of complications. Heterogeneity was high across studies (Cochrane Q tests <0.05), reflecting inconsistent definitions and methods. In subgroup analyses, the open approach was associated with a longer operative time than robotic surgery for large prostates (p = 0.03) and a lower PSM rate (p < 0.001).

Conclusions and clinical implications: Anatomical factors, particularly PV, play a significant role in RP outcomes. Larger prostates are associated with higher complication rates but fewer PSMs. Further research with standardized outcome measures is necessary to clarify these relationships and guide clinical decision-making.

Patient summary: In this study, we examined how a patient's individual anatomy might affect the results of prostate surgery for cancer. We found that larger prostates tend to lead to longer surgeries and increased blood loss, but these also have a lower risk of leaving cancer cells behind. These findings could help doctors in better planning surgeries and improving patient outcomes.

Background and objective: The use of blood-based risk prediction tools has been proposed to improve prostate cancer screening, but data on repeated screening are lacking. Our aim was to compare outcomes using the blood tests prostate-specific antigen (PSA) and Stockholm3 for repeat prostate cancer screening.

Methods: In the population-based screening-by-invitation STHLM3-MRI trial, men aged 50-74 yr were invited to participate in screening. At 2-3 yr after the initial round, men with PSA ≥1.5 ng/ml at trial inclusion who were randomized to magnetic resonance imaging (MRI)-enhanced screening and were not diagnosed with prostate cancer after the initial round were invited for repeat screening involving analysis of PSA and Stockholm3. Biparametric 1.5-T MRI was performed in cases with PSA ≥3 ng/ml or Stockholm3 ≥0.11. Men with Prostate Imaging0Reporting and Data System ≥3 lesions were referred for targeted plus systematic biopsies. The primary outcome was Gleason ≥7 cancer. Secondary outcomes included the number of MRI scans and biopsy procedures, and detection of Gleason 6 and Gleason ≥4 + 3 cancer. Outcomes were compared using the relative positive fractions (RPF).

Key findings and limitations: Of 7609 men from the initial screening round, 2078 were eligible for repeat screening and 1500 (72%) participated. For detection of Gleason ≥7 prostate cancer, the area under the receiver operating characteristic curve was 0.765 (95% confidence interval [CI] 0.725-0.805) for Stockholm3 and 0.651 (95% CI 0.601-0.701) for PSA. Stockholm3 ≥0.15 was associated with 41% fewer MRI scans in comparison to PSA ≥3 ng/ml (RPF 0.59, 95%CI 0.54-0.64), while the detection of GS ≥4 + 3 cancers was similar (RPF 1.00, 95% CI 0.78-1.29). Stockholm3 ≥0.15 detected fewer Gleason ≥7 (RPF 0.75, 95% CI 0.59-0.95) and Gleason 6 (RPF 0.73, 95% CI 0.46-1.16) cancers. Stockholm3 ≥0.11 was associated with no decrease in the number of MRI scans, but an increase of the number of cancer cases detected. Limitations include the lack of long-term outcomes.

Conclusions and clinical implications: Use of the Stockholm3 test for repeated prostate cancer screening could reduce the need for MRI while maintaining detection rates for high-risk cancer.

Patient summary: In this study, we invited men to a second round of prostate cancer screening. We found that use of a new blood test called Stockholm3 can make screening programs more efficient by using fewer resources while still detecting aggressive cancers.

Background and objective: The etiology of prostate cancer (PC) is multifactorial and poorly understood. It has been suggested that colibactin-producing Escherichia coli positive for the pathogenicity island pks (pks⁺) initiate cancers via induction of genomic instability. In PC, androgens promote oncogenic translocations. Our aim was to investigate the association of pks⁺E. coli with PC diagnosis and molecular architecture, and its relationship with androgens.

Methods: We quantified the association of pks⁺E. coli with PC diagnosis in a volunteer-sampled 235-person cohort from two institutional practices (UT San Antonio). We then used colibactin 742 and DNA/RNA sequencing to evaluate the effects of colibactin 742, dihydrotestosterone (DHT), and their combination in vitro.

Key findings and limitations: Colibactin exposure was positively associated with PC diagnosis (p = 0.04) in our clinical cohort, and significantly increased replication fork stalling and fusions in vitro (p < 0.01). Combined in vitro exposure to colibactin 742 and DHT induced more somatic mutations of all types than exposure to either alone. The combination also elicited kataegis, with a higher density of somatic point mutations. Laboratory analyses were conducted using a single cell line, which limited our ability to fully recapitulate the complexity of PC etiology.

Conclusions and clinical implications: Our findings are consistent with synergistic induction of genome instability and kataegis by colibactin 742 and DHT in cell culture. Colibactin-producing pks⁺ E. coli may plausibly contribute to PC etiology.

Patient summary: We investigated whether a bacterial toxin that is linked to colon cancer can also cause prostate cancer. Our results support this idea by showing a link between the toxin and prostate cancer diagnosis in a large patient population. We also found that this toxin causes genetic dysfunction in prostate cancer cells when combined with testosterone.

Background and objective: It is unclear whether "total therapy" (androgen deprivation therapy [ADT] with or without an androgen receptor pathway inhibitor [ARPI], metastasis-directed therapy, and local therapy to the prostate if de novo) may lead to long-term durable remission in oligometastatic hormone-sensitive prostate cancer (omHSPC). This study aims to evaluate the outcomes after the completion of total therapy in patients with omHSPC.

Methods: A retrospective single-institution cohort of consecutive patients with omHSPC identified on conventional or molecular imaging treated with total therapy was assembled. All patients had prostate-specific antigen ≤0.1 ng/ml at the completion of systemic therapy. Kaplan-Meier and Cox regression models were used to evaluate the key outcomes of interest: clinical progression-free survival (cPFS), eugonadal progression-free survival (PFS), and time to restart of ADT (TTrADT).

Key findings and limitations: Eighty-nine patients were included, of whom 23 were with de novo omHSPC; the median number of metastases was 1, and detection of disease by molecular imaging was performed in 43 patients (48%). Forty-nine patients (55%) received ADT + ARPI doublet and 40 (45%) received ADT alone. At a median follow-up of 37 mo, there were 46 cPFS events; 3-yr cPFS rate was 45% (95% confidence interval 33-56) and the median eugonadal PFS was 12 mo. The median TTrADT was 47 mo, and 60% had not restarted ADT at 3 yr. Duration of systemic therapy ≥12 mo was the only significant predictor of better outcomes.

Conclusions and clinical implications: Of the patients receiving total therapy for omHSPC, 45% remained progression free at 3 yr after completing therapy, hinting at the potential for long-term remission and possible cure with this strategy in a subset of patients with omHSPC. Prospective trials evaluating this approach are needed.

Patient summary: In this report, we looked at outcomes in men who had received a fixed duration of hormonal therapy along with radiotherapy to metastatic sites (and prostate radiotherapy or surgery in those with newly diagnosed disease) for oligometastatic prostate cancer. We found that nearly half of the patients had no evidence of cancer recurrence at 3 yr after completing therapy, and 60% had not resumed any therapy at this time point.

Background and objective: Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.

Methods: A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.

Key findings and limitations: Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.

Conclusions and clinical implications: DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.

Patient summary: In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care.

Background and objective: Long-term (LT) androgen deprivation therapy (ADT) has been found to be beneficial to patients with high-risk prostate cancer (PCa). However, administration of LT-ADT to all patients with high-risk PCa may lead to overtreatment. Enhanced risk stratification using genomic classifiers (such as the recently developed prostate subtyping classifier [PSC]) might be useful. This study aims to characterize the prognostic and predictive ability of the PSC in patients with high-risk PCa undergoing radiotherapy long-term (LT; 24-28 mo) versus short-term (ST; 4 mo) ADT.

Methods: Biopsy samples from three randomized, phase 3 trials-NRG/RTOG 9202, 9413, and 9902-were classified as either PSC basal or luminal. The prognostic and predictive values of PSC for each oncologic endpoint (biochemical failure [BF], distant metastasis [DM], metastasis-free survival [MFS], PCa-specific mortality [PCSM], overall survival [OS]) and other cause-mortality (OCM) were assessed with Cox proportional hazards (MFS, OCM, and OS), Fine-Gray (BF, DM, and PCSM), and restricted mean survival time (RMST) models.

Key findings and limitations: On a multivariable analysis, the basal subtype was found to have a worse prognosis for MFS (hazard ratio [HR] 1.8 [1.3-2.5], p < 0.001), PCSM (subdistribution HR 2.4 [95% confidence interval {CI} 1.4-4.1], p = 0.001), and OS (HR 1.8 [1.3-2.6], p < 0.001). Ten-year PCSM was 15% better for the luminal subtype than for the basal subtype (11% [95% CI 6-15%] vs 26% [95% CI 17-35%]). A significant interaction between ADT duration (LT vs ST) and PSC subtype (basal vs luminal) was observed for PCSM (p_interaction = 0.008), leading to the observation that 10-yr PCSM was improved with LT-ADT only in patients with basal-type tumors (5% [95% CI 0-11%] vs 42% [29-56%], p < 0.001). Improvements in 10-yr RMST with LT-ADT were greater for basal tumors for oncologic endpoints with the exception of OCM.

Conclusions and clinical implications: PSC is both a prognostic and a predictive biomarker for patients who benefit from LT-ADT. PSC subtypes may be used to personalize ADT recommendations for patients with high-risk PCa, pending further validation in a prospective study.

Patient summary: In this study, we tried to understand the usefulness of a new genomic test in patients with high-risk, nonmetastatic prostate cancer who underwent radiation therapy and hormonal therapy (HT). We found that this test can help determine a patient's prognosis (eg, a patient's chance of having the cancer return) and, more importantly, personalize treatment decisions by understanding which patients may benefit from long-term HT. This has the potential to save many patients who may not benefit from prolonged HT from "overtreatment" or the unnecessary side effects of such treatment.

Although obesity has been associated with better overall survival (OS) among patients with metastatic castration-resistant prostate cancer, its association with OS has not been extensively explored in metastatic hormone-sensitive prostate cancer (mHSPC). We conducted a post hoc exploratory analysis of patient-level data from the SWOG-1216 trial to determine whether baseline body mass index (BMI) is associated with better OS among patients with mHSPC. SWOG-1216 was an open-label, phase 3 trial that randomized patients newly diagnosed with mHSPC 1:1 to either androgen deprivation therapy (ADT) with orteronel (experimental arm) or ADT with bicalutamide (control arm). Of 1279 patients included in the analysis, 12 (0.9%) were underweight, 252 (19.7%) had normal BMI, 958 (74.9%) were overweight, and 57 (4.5%) were obese. Age, Gleason score, extent of disease burden, the incidence of visceral metastases, and treatment allocation were similar among the groups (p > 0.05), while differences in baseline prostate-specific antigen and Zubrod performance status were observed (p < 0.05). Median OS was 2.4, 5.5, 6.6, and 6.8 yr in the underweight, normal, overweight, and obese groups, respectively. After adjusting for prognostic variables, high BMI was associated with better OS (HR for each increment in BMI category: 0.829, 5% CI 0.68-0.98; p = 0.029). These findings need to be validated in other phase 3 trials. PATIENT SUMMARY: We analyzed data from a clinical trial to evaluate the association between body mass index (BMI) and overall survival among patients with metastatic hormone-sensitive prostate cancer. We found that in this group of patients, the risk of death was lower for patients with higher BMI.

Background and objective: Prostate-specific membrane antigen (PSMA) molecular imaging is widely used for disease assessment in prostate cancer (PC). Artificial intelligence (AI) platforms such as automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) identify and quantify locoregional and distant disease, thereby expediting lesion identification and standardizing reporting. Our aim was to evaluate the ability of the updated aPROMISE platform to assess treatment responses based on integration of the RECIP (Response Evaluation Criteria in PSMA positron emission tomography-computed tomography [PET/CT]) 1.0 classification.

Methods: The study included 33 patients with castration-sensitive PC (CSPC) and 34 with castration-resistant PC (CRPC) who underwent PSMA-targeted molecular imaging before and ≥2 mo after completion of treatment. Tracer-avid lesions were identified using aPROMISE for pretreatment and post-treatment PET/CT scans. Detected lesions were manually approved by an experienced nuclear medicine physician, and total tumor volume (TTV) was calculated. Response was assessed according to RECIP 1.0 as CR (complete response), PR (partial response), PD (progressive disease), or SD (stable disease). KEY FINDINGS AND LIMITATIONS: aPROMISE identified 1576 lesions on baseline scans and 1631 lesions on follow-up imaging, 618 (35%) of which were new. Of the 67 patients, aPROMISE classified four as CR, 16 as PR, 34 as SD, and 13 as PD; five cases were misclassified. The agreement between aPROMISE and clinician validation was 89.6% (κ = 0.79).

Conclusions and clinical implications: aPROMISE may serve as a novel assessment tool for treatment response that integrates PSMA PET/CT results and RECIP imaging criteria. The precision and accuracy of this automated process should be validated in prospective clinical studies.

Patient summary: We used an artificial intelligence (AI) tool to analyze scans for prostate cancer before and after treatment to see if we could track how cancer spots respond to treatment. We found that the AI approach was successful in tracking individual tumor changes, showing which tumors disappeared, and identifying new tumors in response to prostate cancer treatment.

Background and objectives: We assessed the agreement between prostate-specific antigen (PSA) and imaging responses using whole-body magnetic resonance imaging (wbMRI). Our aim was to explore the potential prognostic value of PSA and wbMRI responses in metastatic hormone-naïve prostate cancer (mHNPC) and castration-resistant PC (mCRPC).

Methods: wbMRI was prospectively performed in 37 patients with mHNPC and 51 with mCRPC before and after 6-12 mo of androgen deprivation therapy and an androgen receptor pathway inhibitor (ARPI). Imaging responses were defined according to the Metastasis Reporting and Data System for PC (MET-RADS-P) criteria. A PSA response was defined as PSA ≤0.2 ng/ml in mHNPC and a ≥50% decrease from the pretreatment level in mCRPC. Agreement between PSA and wbMRI responses was assessed using Cohen's κ. The association between time to subsequent treatment and overall survival (OS) was analyzed using Cox regression analysis.

Key findings and limitations: Agreement between PSA and wbMRI responses was fair in mHNPC (κ = 0.30) but none to slight in mCRPC (κ = 0.15). In mHNPC, patients with a PSA or wbMRI response were less likely to receive subsequent treatments; wbMRI progression was associated with a significantly higher risk of death (hazard ratio 8.59; p = 0.002). In mCRPC, two-thirds of patients with a PSA response showed progression on wbMRI; neither PSA nor wbMRI progression changed the likelihood of starting a subsequent treatment or the risk of death.

Conclusions and clinical implications: In mHNPC, wbMRI progression was associated with a higher risk of needing subsequent treatment and shorter OS.

Patient summary: We evaluated the agreement between routine PSA (prostate-specific antigen) test results and whole-body MRI (magnetic resonance imaging) scans for assessing the response of metastatic prostate cancer to treatment. There was disagreement between the PSA and MRI results, mainly for patients with cancer that was resistant to hormone-based treatment. Combining PSA with whole-body MRI might provide a more accurate picture of the response of advanced prostate cancer to treatment.