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: 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: 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: 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: To evaluate the outcome of fulguration of Hunner's ulcers (HUs) in painful bladder syndrome/interstitial cystitis (PBS/IC) that is refractory to conservative treatment. MATERIALS AND METHODS: Patients diagnosed with refractory PBS/IC and treated with fulguration between 2011 and 2013 were identified through screening of medical records. To evaluate treatment outcomes, voiding diaries, the visual analogue scale (VAS) for pain, and two IC symptom questionnaires (pelvic pain and urgency/frequency scale [PUF] and O'Leary-Sant IC symptom index and IC problem index [OS]) were used. Fulguration was deemed to be successful if the VAS score was <2 or less than half of the preoperative VAS score. RESULTS: In total, 27 patients with PBS/IC in whom conservative treatments had failed were enrolled. Two months after fulguration, decreases were observed in the mean 24-hour urinary frequency (from 16.0 to 10.2), 24-hour urgency episodes (8.0 to 1.8), and the VAS (5.8 to 1.2), PUF symptom (15.1 to 7.0), PUF bother (8.4 to 2.7), OS symptom (15.1 to 7.2), and OS problem (13.8 to 6.0) scores. At 5 and 10 months, all variables had worsened. At 2, 5, and 10 months, the success rates were 94.1%, 70.0%, and 33.3%, respectively. Four patients underwent one repeat fulguration on average 11.3 months after the first fulguration. Repeat fulguration was not significantly associated with any clinical characteristics. CONCLUSIONS: In PBS/IC that was refractory to medication or other conservative treatments, HU elimination by fulguration effectively improved symptoms. However, this effect decreased gradually over time.
Cystitis ; Cystitis, Interstitial* ; Electrocoagulation ; Humans ; Mass Screening ; Medical Records ; Ulcer* ; Urinary Bladder

PURPOSE: In radical prostatectomy (RP) procedures, sparing the neurovascular bundles adjacent to the posterolateral aspect of the prostatic fascia has often been suggested as a possible risk factor for positive surgical margins. Here we aimed to quantify the probability of extracapsular extension (ECE) at the posterolateral side of the prostate to aid in nerve-sparing decision making. MATERIALS AND METHODS: We evaluated 472 patients who underwent RP between July 2007 and January 2012. All patients underwent preoperative magnetic resonance imaging (MRI) with diffusion-weighted imaging and apparent diffusion coefficient mapping. We analyzed 944 side-specific prostate lobes with preoperative variables. To quantify the risk of side-specific posterolateral ECE after RP, we developed a risk-stratification scoring system through logistic regression analysis. RESULTS: Overall, 20.6% of 944 prostate lobes had ECE. In the multivariate analysis, prostate-specific antigen (PSA), biopsy Gleason score > or =7, percentage of side-specific cores with tumor, and posterolateral ECE on MRI were independent predictive factors of posterolateral ECE. On internal and external validation to calculate the predicted risk, the Hosmer-Lemeshow goodness-of-fit test showed good calibration (p=0.396). CONCLUSIONS: PSA, biopsy Gleason score, percentage of side-specific cores with tumor, and posterolateral ECE on MRI are independent predictors of posterolateral ECE. The scoring system derived from this study will provide objective parameters for use when deciding if the neurovascular bundle can be safely spared.
Biopsy ; Calibration ; Diffusion ; Fascia ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; Multivariate Analysis ; Neoplasm Grading ; Prostate ; Prostate-Specific Antigen ; Prostatectomy* ; Prostatic Neoplasms ; Risk Factors

Purpose: This study aimed to examine the causes of death in Korean patients who underwent radical prostatectomy for prostate cancer and investigate the relationship between comorbidity and mortality. Materials and Methods: We conducted a retrospective multicenter cohort study including 4,064 consecutive patients who had prostate cancer and underwent radical prostatectomy between January 1998 and June 2013. The primary endpoint of this study was all-cause mortality, and the secondary endpoints were cancer-specific mortality (CSM) and other-cause mortality (OCM). Charlson comorbidity index (CCI) was calculated to assess the comorbidities of each patient. Results: Of 4,064 patients, 446 (11.0%) died during follow-up. The cause of death was prostate cancer in 132 patients (29.6%), other cancers in 121 patients (27.1%), and vascular disease in 57 patients (12.8%) in our cohort. The overall 10-year CSM rate was lower than the OCM rate (4.6% vs. 10.5%). The 10-year CSM rate was lower than the OCM rate in low- to intermediate-risk group patients (1.2% vs. 10.6%), whereas they were similar in high-risk group patients (11.8% vs. 10.1%). In the multivariable analysis, CCI was independently associated with all-cause mortality after radical prostatectomy, regardless of age and pathologic features. Conclusion Death from prostate cancer was rare in Korean men who underwent radical prostatectomy. Clinicians should be aware of the possibility of overtreatment of low-risk prostate cancer in men with significant comorbidity. Our findings may help to facilitate counseling and plan management in this patient group.

Purpose: The purpose of this study was to explore the association between benign prostatic hyperplasia (BPH) and the incidence of falls from the perspective of geriatric morbid conditions. Methods: Data were sourced from the triennial National Survey of Older Koreans conducted by the Ministry of Health and Welfare (2017–2020). In total, 8,135 male participants aged 65 and older were included, and information was gathered through questionnaires and physical measurements. Logistic regression analysis was utilized to determine the impact of BPH on the risk of falls, and subgroup analyses were conducted to examine the influence of BPH on specific types of falls. Results: Of the participants, 15.2% (1,238 of 8,135) reported that their BPH treatment exceeded 3 months, and 8.0% (648 of 8,135) reported experiencing falls, with 61.4% (398/648) of these falls resulting in injuries. A significant association was identified between BPH and both falls (odds ratio [OR], 1.798; 95% confidence interval [CI], 1.479–2.185) and falls with injuries (OR, 2.133; 95% CI, 1.689–2.694). A subgroup analysis indicated a correlation between BPH and falls in groups having one (OR, 1.912; 95% CI, 1.356–2.694) and 2 or more conditions (OR, 1.856; 95% CI, 1.455–2.367) involving visual and auditory impairments, cognitive decline, depression, lower motor weakness, and limitations in daily activities. Conclusions The findings indicate that BPH contributes to the incidence of falls among older men, particularly those with comorbid conditions. Considering the heightened fall risk among elderly individuals suffering from multiple morbidities, particularly those with BPH, targeted interventions are essential for mitigating the risk of falls in this vulnerable group.

Purpose: We investigated the relationship between nocturia and mortality risk in the United States. Methods: Data were obtained from the National Health and Nutrition Examination Survey 2005–2010. Mortality data were obtained by linking the primary database to death certificate data found in the National Death Index with mortality follow-up up to December 31, 2015. Nocturia was defined based on symptoms reported in the symptom questionnaire. We categorized patients into 2 groups: mild nocturia (2–3 voidsight) and moderate-to severe nocturia (≥4 voidsight). Multiple Cox regression analyses were performed with adjustment for confounding variables at the baseline survey. Results: This study included 9,892 adults (4,758 men, 5,134 women). Nocturia occurred in 3,314 individuals (33.5%). Nocturia was significantly associated with all-cause mortality (hazard ratio [HR], 1.23; 95% confidence interval [CI], 1.10–1.39) and cardiovascular disease (CVD) mortality (HR, 1.55; 95% CI, 1.19–2.01). Moreover, the mortality risk increased with increasing nocturia severity. Further analysis with propensity score matching showed that nocturia was still significantly associated with all-cause mortality and CVD mortality. In subgroup analysis according to sex, nocturia was significantly associated with allcause mortality and CVD mortality in men. In women, moderate-to-severe nocturia was significantly associated with allcause mortality and CVD mortality. In subgroup analysis according to cardio-metabolic diseases, nocturia was associated with CVD mortality in patients with diabetes mellitus, hypertension, dyslipidemia, or CVD at baseline. In subgroup analysis of patients without diabetes mellitus, hypertension or CVD, nocturia was significantly associated with all-cause mortality. Conclusions Nocturia was significantly associated with mortality in men and women after adjusting for major confounding factors.