Background:Androgen-deprivation therapy (ADT) for prostate cancer (PCa) is associated with vasomotor symptoms (VMS; hot flashes and night sweats). However, the impact of VMS has not been well characterized. This systematic literature review analyzed patient-reported outcome (PRO) instruments used to assess the quality of life (QoL) impact of ADT-induced VMS as well as the associated humanistic burden and epidemiology. Methods:MEDLINE In-Progress, Embase, Cochrane Database of Systematic Reviews (2011-2024), and major oncology and health economics and outcomes research conferences (2023-2024) were searched. Results:In total, 52 studies were included. Of 15 PRO instruments identified, the most common were the Hot Flash-Related Daily Interference Scale, the Expanded Prostate Index Composite-26, and hot flash daily diaries. No PRO instrument was specifically validated to assess the effect of ADT-induced VMS on QoL in PCa. VMS severity (including subjective burden) was significantly associated with QoL, whereas VMS frequency was not. However, no consensus exists on VMS severity definitions. VMS negatively affected sexual functioning, interpersonal relationships, and/or work productivity, and the associated anxiety, fatigue, insomnia, and poor sleep quality were found to impair QoL. VMS additionally appeared to mediate ADT's impact on sleep disturbance. ADT-induced VMS rates ranged from 1.4% to 68.9%, with higher frequencies reported within 3-6 months of ADT initiation and when adverse events were actively monitored or participants asked to report/record VMS events (30.7%-68.9%). Lower frequencies were seen when VMS were spontaneously captured or in retrospective analyses without active VMS monitoring/reporting (1.4%-15.8%) and with enzalutamide, apalutamide, and abiraterone as add-on therapy to ADT (13.9%-15.8%). Conclusions:VMS severity in men with PCa is more consistently associated with QoL impact versus VMS frequency. A need for PRO instruments specific to treatment-related VMS in PCa and a lack of consensus on definitions of severity for ADT-induced VMS in PCa were identified.
Introduction:Prostate cancer (PCa) is one of the most common cancers among men and a major cause of cancer-related mortality worldwide. In the Democratic Republic of the Congo (DRC), PCa represents an important public health challenge, with limited data available on environmental factors that may be associated with disease occurrence. Recent evidence suggests that trace element exposure may influence prostate carcinogenesis. This study aimed to compare urinary trace element concentrations between PCa patients and age-matched controls in the DRC. Methodology:This case-control analytical study was conducted between April 5, 2018, and April 5, 2023, in Haut-Katanga Province, DRC. The study included 94 PCa patients and 94 controls matched according to age and sociodemographic characteristics. Urinary concentrations of 12 selected trace elements were measured using inductively coupled plasma optical emission spectrometry. Statistical comparisons were performed using the Wilcoxon-Mann-Whitney U test due to the non-normal distribution of trace element concentrations. Results:Compared with controls, PCa patients showed significantly higher urinary concentrations of arsenic, cadmium, cobalt, chromium, copper, manganese, selenium and zinc (p < 0.05), whereas aluminium concentrations were significantly lower. No significant differences were observed for nickel, lead or strontium. These findings indicate distinct urinary trace element profiles among PCa patients and controls. Conclusion:Altered urinary concentrations of several trace elements, including toxic and essential elements, were associated with PCa status in this Congolese population. Because urinary concentrations mainly reflect recent exposure, further studies using complementary biological matrices and longitudinal designs are needed to clarify the biological significance and potential role of trace elements in PCa development.
Background:Prostate-specific antigen (PSA) levels are influenced by demographic and ethnic factors, but data from Azerbaijani men remain limited. PSA screening for prostate cancer must consider regional and ethnic variations. This study aimed to describe PSA levels and examine their clinical correlates in a hospital-based cohort of Azerbaijani men presenting to a urology clinic. Methods:This retrospective cross-sectional study included 1175 Azerbaijani men aged 40-75 years who presented to a urology clinic without a known diagnosis of prostate cancer between January 2023 and December 2024. Data collected included age, total PSA, digital rectal examination (DRE)-estimated prostate size category, prostate volume (via ultrasound), maximum urinary flow rate (Q max), and International Prostate Symptom Score (IPSS). Multivariate logistic regression was used to identify predictors of PSA elevation. Results:The median PSA level was 1.15 ng/mL (range: 0.001-112.1), with 85.1% of patients exhibiting levels below 4 ng/mL. PSA levels increased with age: 0.86 ng/mL in men aged 40-49, 1.2 ng/mL in those 50-64, and 1.44 ng/mL in men 65-75 years (p < 0.001). PSA correlated positively with age, IPSS, and prostate volume and negatively with Q max (all p < 0.001). PSA levels varied with DRE-estimated prostate size category, showing significant differences between Categories 2 and 3 (p = 0.002). Multivariate analysis identified age as an independent predictor of PSA ≥ 4 ng/mL. Conclusions:In this hospital-based cohort of Azerbaijani men, PSA levels were positively associated with age, and age was independently associated with PSA ≥ 4 ng/mL. These findings provide important regional data and support further investigation of age-specific PSA patterns in the Azerbaijani population.
Background:mpMRI is now widely used before prostate biopsy and has improved the detection of clinically significant prostate cancer (csPCa). However, the subsequent risk of csPCa among men with both negative mpMRI and negative systematic biopsy (SBx) remains uncertain. Purpose:To evaluate the observed medium-term risk of csPCa in biopsy-naïve men with initially negative mpMRI and negative SBx. Methods:This post hoc longitudinal follow-up subanalysis included biopsy-naïve men from the prospective multicenter MULTIPROS trial who had negative baseline mpMRI (PI-RADS ≤ 2) and negative SBx. Of the 582 enrolled men, 169 had negative mpMRI; 138 underwent baseline SBx, of whom 8 had csPCa, and 31 had < 12 months of follow-up, leaving 99 men for analysis. Follow-up was PSA-based, with repeat mpMRI and biopsy performed when clinically indicated. Kaplan-Meier analysis estimated csPCa-free survival. Results:During a mean follow-up of 61.8 ± 22.1 months, four patients were diagnosed with csPCa (4/99; 4.0%). The mean time from initial negative mpMRI to csPCa diagnosis was 68.3 ± 22.4 months. Repeat biopsy was performed in 16 patients. csPCa-free survival was 100% at 24 months and 97.3% at 60 months (95% CI: 93.7%-100%). Conclusion:In this selected cohort of biopsy-naïve men with both negative mpMRI and negative SBx, the observed incidence of csPCa during medium-term follow-up was low but based on only four events. Because repeat imaging and biopsy were selectively triggered rather than systematically performed, the true incidence of csPCa may be underestimated. Continued PSA-based monitoring, with repeat mpMRI and biopsy when clinically indicated, remains important.
Background and Objective:Accurate risk stratification in localized prostate cancer is essential for guiding treatment decisions. Conventional National Comprehensive Cancer Network (NCCN) risk groups rely on prostate-specific antigen (PSA), Gleason grade group, and clinical stage, while artificial intelligence (AI) methods, including radiomics, digital pathology, and multimodal prediction models, are increasingly being evaluated as alternatives or complements. This systematic review aims to assess studies published between 2020 and 2025 that directly compare AI-based models with traditional NCCN risk stratification methods in localized prostate cancer. Methods:A systematic search of PubMed/MEDLINE, Scopus, and Cochrane Library (January 2020-September 2025) was conducted. Eligible studies focused on localized prostate cancer, used AI-based models, compared them with NCCN risk stratification or its components, and reported outcomes such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration, or decision-curve analysis. Case reports, nonhuman studies, and abstracts without full data were excluded. Title/abstract and full-text screening was performed independently by two reviewers. Risk of bias was assessed using PROBAST, and reporting transparency was benchmarked against TRIPOD-AI. Results:Out of 686 records, 100 duplicates were removed. Of the 586 screened, 95 were excluded. A total of 491 records mapped to NCCN components and compared AI with conventional methods; 43 full-text studies met inclusion criteria. AI approaches combining MRI radiomics, PET imaging, and histopathology whole-slide analysis consistently showed higher discrimination than NCCN models, especially for predicting adverse pathology and biochemical recurrence. However, calibration, external validation, and reporting quality were addressed inconsistently. Conclusions:AI-based methods show promise in improving NCCN risk stratification for localized prostate cancer, delivering better prognostic accuracy than traditional approaches. However, variability in methods, limited external validation, and gaps in transparent reporting highlight the need for larger, multiinstitutional prospective studies before these methods can be widely adopted in clinical practice.
