Retrospective Multicenter Analysis of Prognostic Risk Factors for One Year Recurrence in Patient With Renal Cell Carcinoma After Partial or Radical Nephrectomy: Results of Korean Renal Cancer Study Group (KRoCS) Database.

BACKGROUND: We sought to identify prognostic risk factors for one year recurrence in patient with renal cell carcinoma (RCC) after partial or radical nephrectomy. METHODS: We performed a retrospective study of 1,269 patients with RCC after partial or radical nephrectomy and diagnosed recurrence using Korean Renal Cancer Study Group (KRoCS) database between January 1991 and March 2017. Recurrence-free survival (RFS), and overall survival (OS) were calculated using the Kaplan-Meier method and multivariate Cox regression analysis were performed to evaluate independent prognostic factors for recurrence. RESULTS: The median patient age was 56 years and median follow-up period was 67 months. Multivariable analysis demonstrated BMI greater than or equal to 23 and less than 30 (vs. BMI less than 23, hazard ratio [HR]: 0.707, P = 0.020) reduced recurrence one year postoperatively. Eastern Cooperative Oncology Group performance status (ECOG PS) greater than or equal to 1 (vs. ECOG PS 0, HR: 1.548, P = 0.007), high pathological T stage (pT2 vs. pT1, HR: 2.622, P < 0.001; pT3 vs. pT1, HR: 4.256, P < 0.001; pT4 vs. pT1, HR: 4.558, P < 0.001), and tumor necrosis (vs. no tumor necrosis, HR: 2.822, P < 0.001) were independent predictive factors for early recurrence within one year in patients with RCC. Statistically significant differences on RFS and OS were found among pathological T stages (pT2 vs. pT1; pT3 vs. pT1; pT4 vs. pT1, all P < 0.001). CONCLUSION: This large multicenter study demonstrated ECOG PS greater than or equal to 1, high pathological T stage, tumor necrosis and BMI less than 23 were significant prognostic risk factors of early recurrence within one year in patients with RCC who underwent nephrectomy.

Prediction of the composition of urinary stones using deep learning.

PURPOSE: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images. MATERIALS AND METHODS: We classified 1,332 stones into 31 classes according to the stone composition. The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohydrate [COM] 100%, class 2: COM 80%+struvite 20%, class 3: COM 60%+calcium oxalate dihydrate [COD] 40%, class 4: uric acid 100%) were selected. With the 965 stone images of the top 4 classes, we used the seven convolutional neural networks (CNN) to classify urinary stones and compared their classification performances. RESULTS: Among the seven models, Xception_Ir0.001 showed the highest accuracy, precision, and recall and was selected as the CNN model to predict the stone composition. The sensitivity and specificity for the 4 classes by Xception_Ir0.001 were as follows: class 1 (94.24%, 91.73%), class 2 (85.42%, 96.14%), class 3 (86.86%, 99.59%), and class 4 (94.96%, 98.82%). The sensitivity and specificity of the individual components of the stones were as follows. COM (98.82%, 94.96%), COD (86.86%, 99.64%), struvite (85.42%, 95.59%), and uric acid (94.96%, 98.82%). The area under the curves for class 1, 2, 3, and 4 were 0.98, 0.97, 1.00, and 1.00, respectively. CONCLUSIONS: This study showed the feasibility of deep learning for the diagnostic ability to assess urinary stone composition from images. It can be an alternative tool for conventional stone analysis and provide decision support to urologists, improving the effectiveness of diagnosis and treatment.

Effect of obesity and metabolic health on urolithiasis: A nationwide population-based study.

PURPOSE: To investigate the risk of symptomatic urolithiasis requiring surgical treatment according to obesity and metabolic health status using a nationwide dataset of the Korean population. MATERIALS AND METHODS: Of the 5,300,646 persons who underwent health examinations between the year 2009 and 2016, within one year after the health examination, 35,137 patients who underwent surgical treatment for urolithiasis were enrolled. Participants were classified as "obese" or "non-obese" using a body mass index (BMI) cutoff of 25 kg/m². People who developed ≥1 metabolic disease component in the index year were considered "metabolically unhealthy", while those with none were considered "metabolically healthy". RESULTS: Out of 34,330 participants excluding 843 missing, 16,509 (48.1%), 4,320 (12.6%), 6,456 (18.8%), and 7,045 (20.5%) subjects were classified into the metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) group, respectively. Mean BMI was 22.1±1.9 kg/m², 22.9±1.6 kg/m², 26.9±1.8 kg/m², and 27.9±2.4 kg/m² respectively. After adjusting the age and sex, the subjects in the MUNO group had an HR (95% CI) of 1.192 (1.120-1.268), those in the MHO group, 1.242 (1.183-1.305), and those in the MUO group, 1.341 (1.278-1.407) for either extracorporeal shockwave lithotripsy or surgery, compared to those in the MHNO group. CONCLUSIONS: Metabolically healthy, obese individuals have a higher risk of developing symptomatic urolithiasis than non-obese, unhealthy, but have a lower risk than obese, unhealthy. It suggests that metabolic health and obesity have collaborative effects, independently affecting the development of symptomatic urinary stone diseases.

Recent Trends in Transurethral Surgeries and Urological Outpatient Procedures: a Nationwide Population-based Cohort Study.

BACKGROUND: Demographic change and advances in technology affect transurethral surgery and outpatient procedures in the urologic field. There are few population-based studies that accurately assess the trend of transurethral surgery and outpatient procedures including diagnostic tests. We investigated the recent epidemiologic trends in transurethral surgeries and urological outpatient procedures from 2009 to 2016 in Korea using the entire population-based cohort. METHODS: We analyzed medical service claim data of transurethral surgery, urological outpatient procedures submitted by medical service providers from the Health Insurance Review and Assessment Service from 2009 to 2016. RESULTS: Transurethral ureter surgery increased by 134.9% from 14,635 in 2009 to 34,382 in 2016 (B = 2,698; R² = 0.98; P < 0.001). The transurethral bladder surgery increased by 65.5% from 12,482 in 2009 to 20,658 in 2016 (B = 1,149; R² = 0.97; P < 0.001). Over the 8-years period, there were not significant changes in transurethral prostate (B = 43; R² = 0.04; P = 0.617) and urethral surgery (B =-12; R² = 0.18; P = 0.289). The significantly increasing trends in cystoscopy (B = 5,260; R² = 0.95; P < 0.001) and uroflowmetry (B = 53,942; R² = 0.99; P < 0.001) were observed during the 8-year period. There was no difference in bladder catheterization during the 8-year period. Urodynamic study (UDS: B =-2,156; R² = 0.77; P = 0.003) and electrical stimulation treatment (EST: B =-1,034; R² = 0.87; P < 0.001) significantly decreased. CONCLUSION: In Korea, transurethral ureter surgery and transurethral bladder surgery have been continuously increasing. Transurethral prostate surgery and transurethral urethral surgery remained constant with no increase or decrease. Cystoscopy and uroflowmetry continue to increase, while UDS and EST continue to decrease.

Radioisotope identification using an energy-weighted algorithm with a proof-of-principle radiation portal monitor based on plastic scintillators.

In this study, we validated the feasibility of an energy weighted algorithm that highlights a characteristic area including the Compton edge as a single peak in a proof-of-principle radiation portal monitor system with a plastic scintillator measuring 50 × 100 × 5 cm3. We measured the energy weighted spectra with steel shielding and the dynamic movements of the 137Cs and 60Co sources. The results showed that the peak locations of each source could be identified under shielded or dynamic motion conditions, each within a maximum difference of 0.08 MeV.

Validation of energy-weighted algorithm for radiation portal monitor using plastic scintillator.

To prevent illicit tracking of radionuclides, radiation portal monitor (RPM) systems employing plastic scintillators have been used in ports and airports. However, their poor energy resolution makes the discrimination of radioactive material inaccurate. In this study, an energy weight algorithm was validated to determine (133)Ba, (22)Na, (137)Cs, and (60)Co by using a plastic scintillator. The Compton edges of energy spectra were converted to peaks based on the algorithm. The peaks have a maximum error of 6% towards the theoretical Compton edge.

A Monte Carlo study of an energy-weighted algorithm for radionuclide analysis with a plastic scintillation detector.

Nuisance and false alarms due to naturally occurring radioactive material (NORM) are major problems facing radiation portal monitors (RPMs) for the screening of illicit radioactive materials in airports and ports. Based on energy-weighted counts, we suggest an algorithm that distinguishes radioactive nuclides with a plastic scintillation detector that has poor energy resolution. Our simulation study, using a Monte Carlo method, demonstrated that man-made radionuclides can be separated from NORM by using a conventional RPM.