Byeong Jin Kang

Background: Urinary tract infections (UTIs) are common worldwide, and rising antimicrobial resistance (AMR) threatens effective empirical treatment, particularly in low- and middle-income countries (LMICs), where diagnostic and stewardship capacity may be limited. We synthesized evidence on uropathogen distribution and resistance patterns in LMICs and translated the findings into an implementation-oriented public health roadmap. Methods: We conducted a systematic review in accordance with PRISMA and MOOSE guidance and registered the protocol in PROSPERO (ID: 1267531). PubMed, Embase, and Scopus were searched through December 1, 2025, for observational studies reporting culture-confirmed UTIs and antimicrobial susceptibility outcomes in LMICs. Because of methodological and clinical heterogeneity, findings were synthesized descriptively without meta-analysis. Results: Twelve studies from Sub-Saharan Africa, South Asia, Southeast Asia, and the Caribbean met the inclusion criteria. Escherichia coli was the predominant uropathogen, accounting for 26.7%–62.4% of isolates in studies reporting organism-level data. Resistance to commonly used oral agents was frequently high: trimethoprim–sulfamethoxazole resistance ranged from 19 to 87%, and fluoroquinolone resistance exceeded 70% in several cohorts. Nitrofurantoin resistance was comparatively lower where reported. Multidrug-resistant and extended-spectrum β-lactamase-producing organisms were identified in hospital, community, and antenatal settings, although definitions and potential overlap between these phenotypes varied across studies. Conclusions: Evidence from the included LMIC studies indicates that resistance to widely used empirical UTI treatments may constrain routine management. The predominance of hospital-based surveillance and heterogeneity in susceptibility reporting highlight the need to expand access to culture and antimicrobial susceptibility testing, standardize reporting, and extend antimicrobial stewardship beyond tertiary care. We propose a structured roadmap linking identified evidence gaps to feasible interventions that support locally informed empirical therapy and strengthened surveillance.

Background/Objectives: Although intravesical Bacillus Calmette–Guérin (BCG) is an established adjuvant therapy for non-muscle-invasive bladder cancer (NMIBC), chronic global shortages and adverse events (AEs) can occur. Thus, intravesical gemcitabine has been used as an alternative. We compared the long-term oncological outcomes and safety profiles of BCG and gemcitabine in treatment-naïve patients with intermediate- and high-risk NMIBC. Methods: Patients with intermediate- and high-risk NMIBC (n = 477) received adjuvant intravesical induction and maintenance therapy with intravesical BCG (n = 361) or gemcitabine (n = 116) and their data were collected retrospectively. Results: Compared with the gemcitabine group, the BCG group had significantly higher proportions of patients with T1 stage, high-grade tumors, high-risk tumors, and longer median follow-up duration. Over a median 36-month observation period, the BCG group exhibited significantly better recurrence-free survival (RFS) and high-grade RFS (HG-RFS) than the gemcitabine group. In the propensity score–matched high-risk population, BCG also outperformed gemcitabine in RFS and HG-RFS. BCG therapy was identified as a potent protective predictor, reducing the risk of recurrence and high-grade recurrence by 65% and 66%, respectively, in the total cohort, and by 69% and 71%, respectively, in the propensity score-matched high-risk subgroup. No significant differences were observed in the frequency of grade ≥ 3 AEs between BCG and gemcitabine. Conclusions: Intravesical BCG is strongly associated with superior oncological outcomes over gemcitabine in intermediate- and high-risk NMIBC. The results of this study offer pivotal practice-based insights to guide clinical strategies for managing NMIBC.

Kang BJ, et al. World J Mens Health. 2026 ;44:e42. https://doi.org/10.5534/wjmh.250350

Addressing an Unmet Need in Postprostatectomy Care: Perspectives on Urovaxom

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.

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

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.

Background: Programmed death ligand 1 (PD-L1) expression cannot currently be predicted through radiological findings. This study aimed to develop a prediction model capable of differentiating between positive and negative PD-L1 expression through a radiomics-based investigation of computed tomography (CT) images in patients with urothelial carcinoma.Methods: Sixty-four patients with urothelial carcinoma who underwent immunohistochemical testing for PD-L1 were retrospectively reviewed. The number of patients in the positive and negative PD-L1 groups (PD-L1 expression >5%) was 14 and 50, respectively. CT images obtained 90 seconds after contrast medium administration were selected for radiomic extraction. For all tumors, 1,691 radiomic features were extracted from CT using a manually segmented three-dimensional volume of interest. Univariate and multivariate logistic regression analyses were performed to identify radiomic features that were significant predictors of PD-L1 expression. For the radiomics-based model, a receiver operating characteristic (ROC) analysis was performed. Results: Among 64 patients, 14 were included in the PD-L1 positive group. Logistic regression analysis found that the following radiomic features significantly predicted PD-L1 expression: wavelet-low-pass, low-pass, and high-pass filters (LLH)_gray-level size-zone matrix (GLSZM)_SmallAreaEmphasis, wavelet-LLH_firstorder_Energy, log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis, original_shape_Maximum2DDiameterColumn, wavelet-low-pass, low-pass, and low-pass filters (LLL)_gray-level run-length matrix (GLRLM)_ShortRunEmphasis, and exponential_firstorder_Kurtosis. The radiomics signature was –4.0934+21.6224 (wavelet-LLH_GLSZM_SmallAreaEmphasis)+0.0044 (wavelet-LLH_firstorder_Energy)–4.7389 (log-sigma-0-5-mm-3D_GLSZM_SmallAreaHighGrayLevelEmphasis)+0.0573 (original_shape_Maximum2DDiameterColumn)–29.5892 (wavelet-LLL_GLRLM_ShortRunEmphasis)–0.4324 (exponential_firstorder_Kurtosis). The area under the ROC curve model representing the radiomics signature for differentiating cases that were deemed PD-L1 positive based on immunohistochemistry was 0.96. Conclusions: This preliminary radiomics model derived from contrast-enhanced CT predicted PD-L1 positivity in patients with urothelial cancer.

Giant adrenal myelolipomas are rare, benign, and hormonally inactive tumors. We present the case of a 53-year-old man with a 19-cm retroperitoneal mass, initially suspected to be a retroperitoneal liposarcoma, angiomyolipoma, or adrenal myelolipoma. After conducting endocrine assessments, which were within normal ranges, we decided to perform surgical excision using a modified Makuuchi incision. The tumor was successfully removed, and the final pathological examination confirmed the diagnosis of adrenal myelolipoma. The patient was discharged with no complications and remained without disease recurrence or distant metastasis as of 1 year postoperatively. In conclusion, giant myelolipomas are rare and cause symptoms owing to their large size. Surgical removal is recommended for large or symptomatic myelolipomas. The modified Makuuchi incision allows efficient and safe tumor removal in open surgery for giant myelolipomas.