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: 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: 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.

Yonsei Cancer Center developed an RF(Radiofrequency) capacitive type heating device, GHT-RF8(Greenytherm) in cooperation with Green Cross Medical Corp., Korea in 1986 for the first time in Korea. Cooperative clinical studies of hyperthermia for the treatment of cancer using GHT-RF8 were conducted by Yonsei Cancer Center in collaboration with the Presbyterian Medical Center, Chonju, Korea. A total of forty patients with various histologically proven malignant tumors, including superficial (N = 13) and deep-seated tumors (N = 27), were treated with this newly developed heating device in conjunction with radiotherapy (N = 38) or chemotherapy (N = 2) at two different institutes between October 1986 and September 1987. These patients were locally far advanced or recurrent cases and considered to be refractory to conventional cancer treatment modalities. Radiotherapy was given in 200cGy per day, five times a week fractionations with a total tumor dose of 50-60Gy in 5-6 weeks. Within an hour after radiotherapy, the RF capacitive type of hyperthermia was given two times a week for a total of 4-10 treatment sessions and an attempt was made to maintain the tumor temperature at 41-45 degrees C for 30-60 minutes. Of forty patients treated, 14 patients with deep-seated tumors showed complete response and 20 patients showed partial response. The overall response rate was 85% (34 out of 40 patients) and only 6 patients showed no response. Complications from this treatment were mainly burns, superficial first degree burn in 2 cases, second degree in 4 cases and subcutaneous fat necrosis was observed in 2 cases.
Adolescent ; Adult ; Aged ; Equipment Design ; Female ; Heating/*instrumentation ; Human ; Hyperthermia, Induced/adverse effects/*instrumentation ; Male ; Middle Age ; Neoplasms/radionuclide imaging/therapy ; Support, Non-U.S. Gov't ; Tomography, X-Ray Computed

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.