Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on ¹H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with area under the curve (AUC) > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

Lee CH, et al. Investig Clin Urol. 2025 Oct;66:e64. https://doi.org/10.4111/icu.20250313

Kang B, et al. World J Mens Health. 2021 Jan;39:e4. https://doi.org/10.5534/wjmh.200174

Woo Seop Seong, Byeong Jin Kang, A Reum Kim, Kyung Hwan Kim, Hong Koo Ha. Urogenit Tract Infect 2023;18:64-9. https://doi.org/10.14777/uti.2023.18.2.64

Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. While deep learning-based image analysis (DLIA) shows promise in enhancing diagnostic accuracy, its application to radical prostatectomy (RP) specimens remains underexplored. In this study, we evaluated the clinical feasibility and prognostic value of a DLIA algorithm for Gleason grading and tumor quantification on whole RP specimens. Using 29,646 digitized H&E-stained slides from 992 patients who underwent RP, we compared the case-level algorithm results with pathologist assessments for the International Society of Urological Pathology grade groups (GG), tumor volumes (TV), and percent tumor volumes (PTV). We also evaluated their prognostic performance in predicting biochemical progression-free survival (BPFS). Pathologists identified cancer in 986 cases and assigned GG in 980, while the DLIA algorithm identified cancer and assigned GG to all cases without omission. DLIA-assigned GG showed fair concordance with pathologist assessments (linear-weighted Cohen’s kappa: 0.374) and demonstrated similar efficacy in predicting BPFS (c-index: 0.644 for DLIA vs. 0.654 for pathologists; p = 0.52). In tumor quantification, DLIA-measured TV and PTV were strongly correlated with pathologist-based measurements (Pearson’s correlation coefficient: 0.830 and 0.846, respectively), but showed stronger efficacy in BPFS prediction, with c-index values of 0.657 and 0.672 compared to 0.622 and 0.641, respectively. Incorporating DLIA-derived PTV into the CAPRA-S score significantly improved its predictive accuracy for BCR (p = 0.006), increasing the c-index from 0.704 to 0.715. Our findings indicate that DLIA algorithms can enhance the accuracy of Gleason grading and tumor quantification in RP specimens, providing valuable support in clinical decision-making for PCa management.

Purpose: Myosteatosis, defined as fat infiltration in muscle tissue, has been linked to poor outcomes in various cancers. However, the prognostic impact of myosteatosis on renal cell carcinoma (RCC) remains poorly understood. This study evaluated the predictive value of myosteatosis based on an artificial intelligence (AI)-driven computed tomography (CT) analysis in patients with localized RCC who underwent partial nephrectomy.Materials and Methods: This retrospective study included 170 patients with localized RCC who underwent partial nephrectomy at a single institution between 2011 and 2017. Myosteatosis was assessed on CT scans using an AI-based tool. The patients were categorized into 2 groups according to the presence or absence of myosteatosis. The clinical outcomes, including disease-free survival (DFS), were compared to determine the prognostic significance of myosteatosis.Results: Of 170 patients, 36 (21.2%) were diagnosed with myosteatosis. These patients were older and had a higher body mass index. The myosteatosis group had a higher proportion of females than the no myosteatosis group. Lymphovascular invasion and tumor necrosis were prevalent pathological features in patients with myosteatosis. Kaplan-Meier analysis demonstrated that myosteatosis was associated with significantly shorter DFS (p<0.05). Multivariate analysis confirmed that myosteatosis independently predicted adverse outcomes in patients with localized RCC.Conclusion: AI-based CT analysis of myosteatosis is a reliable method for improving the risk stratification of patients with localized RCC. Patients with myosteatosis demonstrate poor pathological features and shorter DFS. These findings highlight the potential of AI-driven body composition analysis to refine prognostic models and personalized treatment strategies.