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Importance Stratifying patients with biochemical recurrence (BCR) after primary treatment for prostate cancer based on the risk of prostate cancer–specific mortality (PCSM) is essential for determining the need for further testing and treatments. Objective To evaluate the association of BCR after radical prostatectomy or radiotherapy and its current risk stratification with PCSM. Design, Setting, and Participants This population-based cohort study included a total of 16 311 male patients with 10 364 (64%) undergoing radical prostatectomy and 5947 (36%) undergoing radiotherapy with curative intent (cT1-3, cM0) and PSA follow-up in Stockholm, Sweden, between 2003 and 2019. Follow-up for all patients was until death, emigration, or end of the study (ie, December 31, 2018). Data were analyzed between September 2022 and March 2023. Main Outcomes and Measures Primary outcomes of the study were the cumulative incidence of BCR and PCSM. Patients with BCR were stratified in low- and high-risk according to European Association of Urology (EAU) criteria. Exposures Radical prostatectomy or radiotherapy. Results A total of 16 311 patients were included. Median (IQR) age was 64 (59-68) years in the radical prostatectomy cohort (10 364 patients) and 69 (64-73) years in the radiotherapy cohort (5947 patients). Median (IQR) follow-up for survivors was 88 (55-138) months and 89 (53-134) months, respectively. Following radical prostatectomy, the 15-year cumulative incidences of BCR were 16% (95% CI, 15%-18%) for the 4024 patients in the low D’Amico risk group, 30% (95% CI, 27%-32%) for the 5239 patients in the intermediate D’Amico risk group, and 46% (95% CI, 42%-51%) for 1101 patients in the high D’Amico risk group. Following radiotherapy, the 15-year cumulative incidences of BCR were 18% (95% CI, 15%-21%) for the 1230 patients in the low-risk group, 24% (95% CI, 21%-26%) for the 2355 patients in the intermediate-risk group, and 36% (95% CI, 33%-39%) for the 2362 patients in the high-risk group. The 10-year cumulative incidences of PCSM after radical prostatectomy were 4% (95% CI, 2%-6%) for the 1101 patients who developed low-risk EAU-BCR and 9% (95% CI, 5%-13%) for 649 patients who developed high-risk EAU-BCR. After radiotherapy, the 10-year PCSM cumulative incidences were 24% (95% CI, 19%-29%) for the 591 patients in the low-risk EAU-BCR category and 46% (95% CI, 40%-51%) for the 600 patients in the high-risk EAU-BCR category. Conclusions and Relevance These findings suggest the validity of EAU-BCR stratification system. However, while the risk of dying from prostate cancer in low-risk EAU-BCR after radical prostatectomy was very low, patients who developed low-risk EAU-BCR after radiotherapy had a nonnegligible risk of prostate cancer mortality. Improving risk stratification of patients with BCR is pivotal to guide salvage treatment decisions, reduce overtreatment, and limit the number of staging tests in the event of PSA elevations after primary treatment.

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Although music has been shown to alleviate pain and anxiety during transrectal PBx, limited evidence is available regarding its impact in the transperineal (TP) setting. This study aimed to investigate the effect of music on pain perception during office-based TP PBx. This prospective, comparative, non-randomized study enrolled biopsy-naïve patients undergoing TP PBx between March 2024 and March 2025. A total of 200 patients were equally assigned to the intervention group (listening to music during PBx) or the control group. Pain was assessed using the Visual Analog Scale (VAS) at six predefined timepoints (T0–T5). A differential pain score (D-VAS) was calculated at each timepoint, using the VAS score at T0 as the baseline. Changes over time between the two groups were analyzed using independent t-tests and a linear mixed-effects model. An interaction term between music and timepoints was included to test whether the effect of music on pain perception varied across the different stages of the procedure. Compared with controls, D-VAS was significantly lower in the music group at the first core (T3, p  = 0.025), last core (T4, p  < 0.001) and at discharge (T5, p  < 0.001). No significant differences were observed during local anesthesia (T1-T2). The Mixed-effects model confirmed these findings and revealed that both pain perception and the relative analgesic effect of music varied accross different stages of the procedure. Listening to music was associated with lower pain perception during TP PBx, particularly during biopsy sampling phases, and resulted in lower pain levels at discharge.

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

The increase in practices related to enhancing penile size can be attributed to the belief that an improved genital appearance contributes to a man’s virility, coupled with an altered self-perception of his body. It is crucial to tailor interventions to meet the genuine needs of patients by thoroughly assessing their history, psychological state, and potential surgical benefits, all while considering the associated risks of complications. This systematic review aims to summarize the available evidence on outcomes, complications, and quality of life after penile augmentation surgery, examining both minimally invasive and more radical techniques. A search of the PubMed and Scopus databases, focusing on English-language papers published in the last 15 years, was performed in December 2023. Papers discussing surgery in animal models and case reports were excluded from the present study unless further evaluated in a follow-up case series. The primary outcomes were changes in penile dimensions, specifically in terms of length and girth, as well as the incidence of surgical complications and the impact on quality of life. A total of 1670 articles were retrieved from the search and 46 were included for analysis. Procedures for penile length perceived enhancements include lipoplasty, skin reconstruction plasty, V-Y and Z plasty, flap reconstruction, scrotoplasty, ventral phalloplasty, and suspensory ligament release; techniques for increasing corporal penile length include penile disassembly, total phalloplasty, and sliding elongation. Finally, penile girth enhancement may be performed using soft tissue fillers, grafting procedures, biodegradable scaffolds, and Penuma®. In conclusion, while penile augmentation surgeries offer potential solutions for individuals concerned about genital size, the risks and complexities need to be accounted for.

Background and Objectives: Despite advancements in the diagnosis and treatment of testicular germ cell tumours (TGTCs), challenges persist in identifying reliable biomarkers for early detection and precise disease management. This narrative review addresses the role of microRNAs (miRNAs) as potential diagnostic tools and therapeutic targets in the treatment of TGCTs. Materials and Methods: Three databases (PubMed®, Web of Science™, and Scopus®) were queried for studies investigating the utility of miRNA as diagnostic tools, assessing their prognostic significance, and evaluating their potential to guide TGCT treatment. Different combinations of the following keywords were used, according to a free-text protocol: “miRNA”, “non-coding RNA”, “small RNA”, “Testicular Cancer”, “seminomatous testicular germ cell”, “non-seminomatous testicular germ cell”. Results: The potential of miRNAs as possible biomarkers for a non-invasive diagnosis of TGCT is appealing. Their integration into the diagnostic pathway for TGCT patients holds the potential to enhance the discriminative power of conventional serum tumour markers (STMs) and could expedite early diagnosis, given that miRNA overexpression was observed in 50% of GCNIS cases. Among miRNAs, miR-371a-3p stands out with the most promising evidence, suggesting its relevance in the primary diagnosis of TGCT, particularly when conventional STMs offer limited value. Indeed, it demonstrated high specificity (90–99%) and sensitivity (84–89%), with good positive predictive value (97.2%) and negative predictive value (82.7%). Furthermore, a direct relationship between miRNA concentration, disease burden, and treatment response exists, regardless of disease stages. The initial evidence of miRNA decrease in response to surgical treatment and systemic chemotherapy has been further supported by more recent results suggesting the potential utility of this tool not only in evaluating treatment response but also in monitoring residual disease and predicting disease relapse. Conclusions: MiRNAs could represent a reliable tool for accurate diagnosis and disease monitoring in the treatment of TGCT, providing more precise tools for early detection and treatment stratification. Nevertheless, well-designed clinical trials and comprehensive long-term data are needed to ensure their translation into effective clinical tools.

Background The detection of prostate cancer requires histological confirmation in biopsy core. Currently, number of unnecessary prostate biopsies are being performed in the United States. This is due to the absence of appropriate biopsy decision‐making protocol. Aim To develop and validate a 4K score/multiparametric magnetic resonance imaging (mpMRI)‐based nomogram to predict prostate cancer (PCa), clinically significant prostate cancer (csPCa), and unfavorable prostate cancer (uPCa). Methods and Results Retrospective, single‐center study evaluating a cohort of 574 men with 4K score test >7% or suspicious digital rectal examination (DRE) or Prostate Imaging Reporting and Data System (PI‐RADS) scores 3, 4, or 5 on mpMRI that underwent systematic and/or mpMRI/ultrasound fusion–targeted prostate biopsy between 2016 and 2020. External cohort included 622 men. csPCa and uPCa were defined as Gleason score ≥3 + 4 and ≥4 + 3 on biopsy, respectively. Multivariable logistic regression analysis was performed to build nomogram for predicting PCa, csPCa, and uPCa. Validation was performed by plotting the area under the curve (AUC) and comparing nomogram‐predicted probabilities with actual rates of PCa, csPCa, and uPCa probabilities in the external cohort. 4K score, a PI‐RADS ≥4, prostate volume and prior negative biopsy were significant predictors of PCa, csPCa, and uPCa. AUCs were 0.84, 0.88, and 0.86 for the prediction of PCa, csPCa, and uPCa, respectively. The predicted and actual rates of PCa, csPCa, and uPCa showed agreement across all percentage probability ranges in the validation cohort. Using the prediction model at threshold of 30, 30% of overall biopsies, 41% of benign biopsies, and 19% of diagnosed indolent PCa could be avoided, while missing 9% of csPCa. Conclusion This novel nomogram would reduce unnecessary prostate biopsies and decrease detection of clinically insignificant PCa.

Background: Adrenal lesions, often incidentally detected, present diagnostic challenges in distinguishing benign from malignant or hormonally active lesions. Conventional imaging (computed tomography/magnetic resonance imaging (CT/MRI)) has limitations, driving interest in artificial intelligence (AI) and radiomics for enhanced accuracy. Objectives: To systematically evaluate AI and radiomics applications in adrenal lesion characterization, focusing on diagnostic performance, methodologies, and clinical utility. Design: PRISMA-guided systematic review of studies published up to June 2024. Data sources and methods: PubMed, Scopus, Web of Science, and Google Scholar were searched using the keywords: adrenal lesions, AI, radiomics, and machine learning. Inclusion followed PICO criteria: patients with indeterminate lesions, AI/radiomics interventions, comparisons to standard diagnostics, and diagnostic accuracy. Two reviewers screened studies, resolving discrepancies via consensus. Eleven retrospective studies (996 patients) met eligibility. Results: CT-based radiomics (eight studies) achieved a mean AUC of 0.88 (range: 0.84–0.94) in differentiating benign/malignant or functional/non-functional lesions. Top-performing models identified aldosterone-producing adenomas (AUC: 0.99). MRI-based radiomics (three studies) yielded mean AUC: 0.82 (0.72–0.92), with test-set performance declines (e.g., AUC: 0.72) suggesting overfitting. Nuclear medicine (four studies) demonstrated that hybrid 18F-FDG PET/CT models (SUVmax + texture features) achieved an AUC of 0.97 for metastatic versus benign lesions. AI applications extended to intraoperative navigation (AUC: 0.93) and prognostic prediction. Conclusion: CT-based radiomics outperformed MRI, aligning with guidelines favoring CT for adrenal assessment. AI-enhanced models show promise in refining diagnostics and reducing invasive procedures. However, retrospective designs, small cohorts, and protocol variability limit generalizability. Future work requires multicenter collaboration, standardized protocols, and prospective validation to translate AI/radiomics into clinical practice.