(1) Background: This study aimed to evaluate the associations of lymphovascular invasion (LVI) at first transurethral resection of bladder (TURBT) and radical cystectomy (RC) with survival outcomes, and to evaluate the concordance between LVI at first TURBT and RC. (2) Methods: We analyzed 216 patients who underwent first TURBT and 64 patients who underwent RC at Toho University Sakura Medical Center. (3) Results: LVI was identified in 22.7% of patients who underwent first TURBT, and in 32.8% of patients who underwent RC. Univariate analysis identified ≥cT3, metastasis and LVI at first TURBT as factors significantly associated with overall survival (OS) and cancer-specific survival (CSS). Multivariate analysis identified metastasis (hazard ratio (HR) 6.560, p = 0.009) and LVI at first TURBT (HR 9.205, p = 0.003) as significant predictors of CSS. On the other hand, in patients who underwent RC, ≥pT3, presence of G3 and LVI was significantly associated with OS and CSS in univariate analysis. Multivariate analysis identified inclusion of G3 as a significant predictor of OS and CSS. The concordance rate between LVI at first TURBT and RC was 48.0%. Patients with positive results for LVI at first TURBT and RC displayed poorer prognosis than other patients (p < 0.05). (4) Conclusions: We found that the combination of LVI at first TURBT and RC was likely to provide a more significant prognostic factor.

Prostate cancer (PCa) exhibits a spectrum of heterogeneity, from indolent to highly aggressive forms, with approximately 10–20% of patients experiencing metastatic PCa. Oligometastatic PCa, characterized by a limited number of metastatic lesions in specific anatomical locations, has gained attention due to advanced imaging modalities. Although patients with metastatic PCa typically receive systemic therapy, personalized treatment approaches for oligometastatic PCa are emerging, including surgical and radiotherapeutic interventions. This comprehensive review explores the latest developments in the field of oligometastatic PCa, including its biological mechanisms, advanced imaging techniques, and relevant clinical studies. Oligometastatic PCa is distinct from widespread metastases and presents challenges in patient classification. Imaging plays a crucial role in identifying and characterizing oligometastatic lesions, with new techniques such as prostate-specific membrane antigen positron emission tomography demonstrating a remarkable efficacy. The management strategies encompass cytoreductive surgery, radiotherapy targeting the primary tumor, and metastasis-directed therapy for recurrent lesions. Ongoing clinical trials are evaluating the effectiveness of these approaches. Oligometastatic PCa occupies a unique position between locally advanced and high-volume metastatic diseases. While a universally accepted definition and standardized diagnostic criteria are still evolving, emerging imaging technologies and therapeutic strategies hold promise for improving the patient outcomes in this intermediate stage of PCa.

Background/Objectives: Acute obstructive pyelonephritis caused by ureteral calculi is a severe urological emergency that can rapidly progress to life-threatening complications, including urosepsis. Early risk stratification is critical for timely intervention and improved patient outcomes. Although Hu’s nomogram has been proposed as a predictive tool for urosepsis, its external validation remains limited. This study aims to validate Hu’s nomogram in an independent cohort and identify novel clinical and imaging predictors of urosepsis. Methods: This retrospective cohort study included 341 patients diagnosed with ureteral calculi who underwent surgical intervention at a single institution between January 2019 and October 2023. Clinical, laboratory, and imaging data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of urosepsis. The predictive accuracy of Hu’s nomogram was evaluated using receiver operating characteristic curve analysis. Results: Among 341 patients, 66 (19.4%) developed urosepsis. Multivariate analysis identified female gender, corticosteroid use, lower platelet count, elevated C-reactive protein levels, positive urine white blood cell count, lower computed tomography attenuation values of calculi, and higher computed tomography attenuation values of hydronephrosis as independent predictors of urosepsis. Hu’s nomogram demonstrated a strong predictive performance (area under the curve: 0.761; 95% CI: 0.701–0.821), reaffirming its clinical utility for risk stratification. Conclusions: This study provides an external validation of Hu’s nomogram and identifies novel risk factors for urosepsis prediction, including corticosteroid use and imaging-based parameters. Incorporating these findings into clinical practice may enhance early risk stratification, facilitate timely interventions, and ultimately improve patient outcomes.

Hereditary predisposition substantially shapes prevention and management across urologic oncology. This narrative review synthesizes contemporary, practice-oriented guidance on whom to test, what to test, how to act on results, and how to implement care equitably for hereditary forms of prostate cancer, renal cell carcinoma (RCC), urothelial carcinoma, pheochromocytoma/paraganglioma (PPGL), and adrenocortical carcinoma (ACC). We delineate between forms of indication-driven germline testing (e.g., universal testing in metastatic prostate cancer; early-onset, bilateral/multifocal, or syndromic RCC; reflex tumor mismatch repair (MMR)/microsatellite instability (MSI) screening in upper-tract urothelial carcinoma (UTUC); universal testing in PPGL; universal TP53 testing in ACC) and pair these strategies with minimum actionable gene sets and syndrome-specific surveillance frameworks. Key points include targeted prostate-specific antigen screening in BRCA2 carriers and the impact of BRCA/ATM variants on reclassification during active surveillance; major hereditary RCC syndromes with genotype-tailored surveillance and pathway-directed therapy (e.g., HIF-2α inhibition for von Hippel–Lindau disease); UTUC/bladder cancer in Lynch syndrome with tumor MMR/MSI screening, annual urinalysis (selective cytology), and immunotherapy opportunities in deficient MMR disease/MSI-H; PPGL management emphasizing universal germline testing, intensified surveillance for SDHB, cortical-sparing adrenalectomy, and emerging HIF-2α inhibition; and ACC care modified by Li–Fraumeni syndrome (minimization of radiation/genotoxic therapy with whole-body imaging surveillance). Testicular germ cell tumor remains largely polygenic, with no routine germline testing in typical presentations. Finally, we provide pre-/post-test genetic-counseling checklists and mainstreamed workflows with equity metrics to operationalize precision care and close real-world access gaps.

Background/Objectives: The therapeutic landscape of advanced or metastatic urothelial carcinoma (UC) has shifted from platinum chemotherapy to precision immuno-oncology. Immune checkpoint inhibitors (ICIs)—pembrolizumab, nivolumab, and avelumab—show efficacy across platinum-refractory, maintenance, and adjuvant settings, yet benefit is limited to subsets, underscoring the need for biomarkers. Antibody–drug conjugates (ADCs), notably enfortumab vedotin(EV), and targeted agents such as FGFR inhibitors further expand options. This review synthesizes current evidence and emerging paradigms to guide combinations and sequencing. Methods: We performed a narrative synthesis of peer-reviewed trials (emphasizing pivotal phase III studies), key translational investigations, and contemporary guidelines on ICIs, ADCs, HER2-directed therapies, FGFR inhibitors, molecular subtyping, and genomic profiling in UC, integrating efficacy signals, biomarker associations, and practical implications for sequencing. Results: ICIs now occupy multiple settings, but heterogeneous benefit highlights the importance of molecularly informed selection. EV alone and with pembrolizumab has produced unprecedented first-line activity, prompting a strategic shift. Molecular subtyping and genomic profiling delineate phenotypes with variable immune responsiveness and targetable vulnerabilities, enabling rational combinations and refined sequencing. Ongoing trials are evaluating next-generation ADCs, HER2-directed approaches, and dual checkpoint blockade to achieve durable, personalized disease control. Conclusions: Management of locally advanced or metastatic UC is converging on precision immuno-oncology, wherein biomarker-driven selection, molecular subtyping, and thoughtful sequencing of ICIs, ADCs, and targeted agents are central to optimizing outcomes. Active trials and translational advances are expected to refine personalized strategies and embed molecular guidance into routine care.

The role of multi-parametric magnetic resonance imaging (mp-MRI) is becoming increasingly important in the diagnosis of the clinical severity of prostate cancer (PCa). However, mp-MRI images usually contain several unaligned 3D sequences, such as DWI image sequences and T2-weighted image sequences, and there are many images among the entirety of 3D sequence images that do not contain cancerous tissue, which affects the accuracy of large-scale prostate cancer detection. Therefore, there is a great need for a method that uses accurate computer-aided detection of mp-MRI images and minimizes the influence of useless features. Our proposed PCa detection method is divided into three stages: (i) multimodal image alignment, (ii) automatic cropping of the sequence images to the entire prostate region, and, finally, (iii) combining multiple modal images of each patient into novel 3D sequences and using 3D convolutional neural networks to learn the newly composed 3D sequences with different modal alignments. We arrange the different modal methods to make the model fully learn the cancerous tissue features; then, we predict the clinical severity of PCa and generate a 3D cancer response map for the 3D sequence images from the last convolution layer of the network. The prediction results and 3D response map help to understand the features that the model focuses on during the process of 3D-CNN feature learning. We applied our method to Toho hospital prostate cancer patient data; the AUC (=0.85) results were significantly higher than those of other methods.