Purpose: This study evaluated the feasibility of radical nephrectomy and thrombectomy without cardiopulmonary bypass (CPB) in patients with renal cell carcinoma (RCC) and level IV venous tumor thrombus, compared with CPB-assisted surgery. Materials and Methods: This retrospective cohort study analyzed patients with RCC and level IV venous tumor thrombus who underwent surgery at a single center between 2014 and 2020. Feasibility of non-CPB surgery was assessed by comparing perioperative safety-related outcomes, overall survival (OS), and progression-free survival (PFS) between the non-CPB and CPB groups. Perioperative outcomes included operative time, blood loss, severe complications (Clavien-Dindo classification grade ≥III), intensive care unit (ICU) stay, and mortality. Kaplan-Meier analysis and generalized Wilcoxon tests were used to compare survival outcomes. Results: A total of 16 patients met eligibility criteria: 5 underwent surgery without CPB, and 11 underwent CPB-assisted surgery. Median operative time was similar between the CPB and non-CPB groups (490 minutes vs. 480 minutes, p=0.650). Compared with the CPB group, blood loss was lower in the non-CPB group (4000 mL vs. 1080 mL, p=0.333). Severe complications occurred in 36.4% of CPB patients and 0% of non-CPB patients (p=0.245). ICU stay was comparable between the non-CPB and CPB groups (2 days vs. 3 days, p=0.356). OS did not differ significantly between groups (p=0.180), whereas PFS was longer in the non-CPB group (p=0.041). Conclusions Radical nephrectomy and thrombectomy without CPB appears feasible and may be associated with lower perioperative morbidity and blood loss without compromising oncologic outcomes. Non-CPB surgery should be considered in selected patients with level IV venous tumor thrombus when technically feasible.

Purpose: To develop and validate an artificial intelligence (AI)-based personalized outcome prediction model for upper-urinary tract urothelial carcinoma patients undergoing radical nephroureterectomy. Materials and Methods: Data from patients who underwent radical nephroureterectomy between 2010 and 2020 across three hospitals were retrospectively analyzed. A model was developed using one tertiary center’s data and externally validated with data from two other hospitals. An AI model using XGBoost as risk estimator and bootstrapped Weibull Accelerated Failure Time model for 10-year survival probability was employed. Hyperparameter tuning used Optuna method. Model efficacy was assessed using concordance index, average Brier score, D-calibration, and six-month interval time-dependent area under the curve (AUC). Results: Of 1,039 patients, 627 qualified after excluding 50 with neoadjuvant chemotherapy. Model development used 564 patients (507 training, 57 test) with 9:1 stratified random split, plus 63 for internal validation and 362 for external validation. Significant parameters included preoperative glomerular filtration rate (p<0.001), hydroureteronephrosis (p=0.013), pathological N stage (p<0.001), concurrent carcinoma in situ (p<0.001), disease progression (p<0.001), and survival rate (p<0.001). Disease-free survival (DFS) model’s concordance index: internal validation 0.789, external validations 0.734 and 0.771. Overall survival (OS) model’s concordance index: internal validation 0.819, external validations 0.780 and 0.771. Mean time-dependent AUC was 0.66–0.77 for DFS and 0.67–0.80 for OS during 10-year periods. Conclusions AI-based model effectively predicts disease-free and OS outcomes for upper-urinary tract urothelial carcinoma patients with post-radical nephroureterectomy, showcasing robust performance across multicenter settings.

Purpose: This postmarketing surveillance (PMS) study aimed to evaluate real-world data on the effectiveness and safety of abiraterone acetate plus prednisone (AAP) in patients with metastatic hormone-sensitive prostate cancer (mHSPC). Materials and Methods: This prospective, multicenter observational study was conducted at 27 centers in South Korea between June 2018 and April 2021. Among 117 enrolled patients, 108 were included in the final analysis. Median follow-up period was 40.4 weeks (interquartile range, 22.8–49.2 weeks). Patients aged ≥18 years with mHSPC treated with AAP were included. Effectiveness endpoints included radiographic progression-free survival (rPFS), time to prostate-specific antigen (PSA) progression (TTPP), and PSA response (≥50%, ≥90% reduction). Adverse events (AEs) were assessed and graded based on severity. Results: A total of 108 patients were included in the analysis. Radiographic and PSA progression were observed in 13 (12.0%) and 26 patients (24.1 %), respectively. In the Kaplan-Meier analysis, the median rPFS and TTPP were not reached. The PSA response rates were 85.2% (≥50% reduction) and 75.9% (≥90% reduction) during the study period. AEs were reported in 31 patients (28.7%), with grade 3 or 4 AEs reported in 6 patients (5.6%). The most frequently observed AE was elevated aspartate aminotransferase level (5.6%). Conclusion This PMS study found that AAP is effective and well-tolerated in the real-world management of mHSPC.

Purpose: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery. Materials and Methods: Intraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet, and U-Net++) and three transformers (SegFormer, BEiT, and DPT), and their performances were analyzed. Results: The overall segmentation performance during RARP varied across different model architectures. For the CNN models, DeepLabV3 achieved a mean Dice score of 0.938, MANet scored 0.944, and U-Net++ reached 0.930. For the transformer architectures, SegFormer attained a mean Dice score of 0.919, BEiT scored 0.916, and DPT achieved 0.940. The performance of CNN models was superior to that of transformer models in segmenting the prostate, vas, and seminal vesicle. Conclusions Deep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

Purpose: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. Materials and Methods: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both “urology” and “artificial intelligence” with one of the following: “machine learning,” “deep learning,” “neural network,” “renal cell carcinoma,” “kidney cancer,” “urothelial carcinoma,” “bladder cancer,” “prostate cancer,” and “robotic surgery.” Results: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. Conclusions AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Purpose: In men with metastatic castration-resistant prostate cancer (mCRPC), new bone lesions are sometimes not properly categorized through a confirmatory bone scan, and clinical significance of the test itself remains unclear. This study aimed to demonstrate the performance rate of confirmatory bone scans in a real-world setting and their prognostic impact in enzalutamide-treated mCRPC. Materials and Methods: Patients who received oral enzalutamide for mCRPC during 2014-2017 at 14 tertiary centers in Korea were included. Patients lacking imaging assessment data or insufficient drug exposure were excluded. The primary outcome was overall survival (OS). Secondary outcomes included performance rate of confirmatory bone scans in a real-world setting. Kaplan-Meier analysis and multivariate Cox regression analysis were performed. Results: Overall, 520 patients with mCRPC were enrolled (240 [26.2%] chemotherapy-naïve and 280 [53.2%] after chemotherapy). Among 352 responders, 92 patients (26.1%) showed new bone lesions in their early bone scan. Confirmatory bone scan was performed in 41 patients (44.6%), and it was associated with prolonged OS in the entire population (median, 30.9 vs. 19.7 months; p < 0.001), as well as in the chemotherapy-naïve (median, 47.2 vs. 20.5 months; p=0.011) and post-chemotherapy sub-groups (median, 25.5 vs. 18.0 months; p=0.006). Multivariate Cox regression showed that confirmatory bone scan performance was an independent prognostic factor for OS (hazard ratio 0.35, 95% confidence interval, 0.18 to 0.69; p=0.002). Conclusion Confirmatory bone scan performance was associated with prolonged OS. Thus, the premature discontinuation of enzalutamide without confirmatory bone scans should be discouraged.

Purpose: Pathologic T3b (pT3b) prostate cancer, characterized by seminal vesicle invasion (SVI), exhibits variable oncological outcomes post–radical prostatectomy (RP). Identifying prognostic factors is crucial for patient-specific management. This study investigates the impact of bilateral SVI on prognosis in pT3b prostate cancer. Materials and Methods: We evaluated the medical records of a multi-institutional cohort of men who underwent RP for prostate cancer with SVI between 2000 and 2012. Univariate and multivariable analyses were performed using Kaplan-Meier analysis and covariate-adjusted Cox proportional hazard regression for biochemical recurrence (BCR), clinical progression (CP), and cancer-specific survival (CSS). Results: Among 770 men who underwent RP without neo-adjuvant treatment, median follow-up was 85.7 months. Patients with bilateral SVI had higher preoperative prostate-specific antigen levels and clinical T category (all p < 0.001). Extracapsular extension, tumor volume, lymph node metastasis (p < 0.001), pathologic Gleason grade group (p < 0.001), and resection margin positivity (p < 0.001) were also higher in patients with bilateral SVI. The 5-, 10-, and 15-year BCR-free survival rates were 23.9%, 11.7%, and 8.5%; CP-free survival rates were 82.8%, 62.5%, and 33.4%; and CSS rates were 96.4%, 88.1%, and 69.5%, respectively. The bilateral SVI group demonstrated significantly lower BCR-free survival rates, CP-free survival rates, and CSS rates (all p < 0.001). Bilateral SVI was independently associated with BCR (hazard ratio, 1.197; 95% confidence interval, p=0.049), CP (p=0.022), and CSS (p=0.038) in covariate-adjusted Cox regression. Conclusion Bilateral SVI is a robust, independent prognostic factor for poor oncological outcomes in pT3b prostate cancer.

Purpose: To find out the incidence and predictors for late high-grade genitourinary (GU) toxicity following salvage radiotherapy (SRT), we investigated the consecutive patients who were treated with SRT after radical prostatectomy. Materials and Methods: Patients who underwent SRT for biochemical recurrence after radical prostatectomy were reviewed. The incidence of GU toxicity was assessed and risk factors for grade ≥2 and ≥3 GU toxicity were evaluated. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guided the reporting of this study. Results: Among the total of 217 patients, 88 patients (40.5%) showed late grade ≥2 GU toxicity. The incidence of late grade ≥3 GU toxicity was 11.5%. The presence of grade ≥2 baseline GU dysfunction (hazard ratio [HR], 6.097; 95% confidence interval [CI], 3.280–11.333; p<0.001) and short interval (<1 year) from surgery to SRT (HR, 1.994; 95% CI, 1.182–3.365; p=0.01) were associated with late grade ≥2 GU toxicity. A short interval from surgery to SRT was an independent predictor of late grade ≥3 GU toxicity (HR, 2.975; 95% CI, 1.135–7.794; p=0.027). Conclusions The incidence of late high-grade GU toxicity was not uncommon after SRT. Thus, care should be taken when we consider SRT in patients with baseline urinary dysfunction and a short interval from surgery to SRT, to determine an optimal treatment strategy with balancing quality of life and oncologic outcome of patients.

Purpose: This study investigated the prognostic factors and cancer-specific survival (CSS) of patients who had renal cell carcinoma (RCC) with venous thrombus and underwent radical nephrectomy with thrombectomy (RNTx). Materials and Methods: From January 1990 to December 2022, we retrospectively reviewed the medical records of patients diagnosed with RCC with venous thrombus who underwent RNTx at a single tertiary medical center. Univariate and multivariable Cox proportional hazard regression analyses were conducted to identify significant prognostic factors affecting CSS. A Kaplan-Meier model was used to calculate CSS rates at 1, 3, and 5 years after RNTx. Results: We included 262 patients in the final analysis (median age, 59 years) with a median follow-up of 28 months. The 1-, 3-, and 5-year CSS rates were 84.1%, 62.5%, and 46.4%, respectively. Multivariable analysis revealed that pathologic T4 stage (hazard ratio [HR], 3.711; 95% confidence interval [CI], 1.599–8.611, p=0.002), pathologic N1 stage (HR, 2.371; 95% CI, 1.231–4.567; p=0.01), sarcomatoid differentiation (HR, 1.89; 95% CI, 1.027–3.477; p=0.041), and tumor necrosis (HR, 2.993; 95% CI, 1.132–7.914; p=0.027) were associated with CSS. Conclusions Approximately one-third of all RCC patients with venous thrombus remained disease-free, and half survived 5 years after RNTx. Sarcomatoid differentiation and the presence of tumor necrosis in pathology predicted poorer CSS outcomes in our study. Further retrospective studies are required to validate these findings.

Purpose: This study aimed to evaluate the impact of serum testosterone level before enzalutamide treatment in metastatic castration-resistant prostate cancer (mCRPC) for antitumor outcomes. Materials and Methods: Single-center, retrospective study including patients that treated with enzalutamide for mCRPC before and after docetaxel chemotherapy. Clinicopathological parameters including serum testosterone at initial enzalutamide use were examined. Prostate-specific antigen (PSA) response, progression-free survival (PFS), and cancer-specific survival (CSS) were the outcomes of interest. Logistic-regression analysis was done for discovering odds for PSA response. Cox-proportional model was applied for risk stratification for progression and cancer-specific death. Results: A total of 228 patients with mCRPC, treated with enzalutamide, both prechemotherapy and postchemotherapy, between 2011 and 2019 were included. One hundred sixty-two of patients (71.1%) experienced PSA decline over 50%. Median PFS and CSS were 5.4 and 13.2 months, respectively. Serum testosterone at initial enzalutamide use was the noble predictor for progression (hazard ratio [HR], 0.409; p=0.020) and cancer-specific death (HR, 0.454; p=0.033) in postchemotherapy group. No significant effect of serum testosterone in prechemotherapy group was detected. Time to CRPC, high-metastatic burden revealed as risk factors for PSA response, PFS, and CSS, both in prechemotherapy and postchemotherapy group. Conclusions High testosterone level at commencement of enzalutamide treatment was associated with a good prognosis in postdocetaxel setting, but not related to oncological outcomes in chemotherapy-naïve patients.