Cancer detection and complications of transperineal prostate biopsy with antibiotics when indicated.

OBJECTIVES: To describe the prostate cancer (PCa) detection rate, including clinically significant prostate cancer (csPCa), in a large cohort of patients who underwent transperineal ultrasonography-guided systematic prostate biopsy (TPB-US) using a probe-mounted transperineal access system, with magnetic resonance imaging (MRI) cognitive fusion in case of a Prostate Imaging-Reporting and Data System grade 3-5 lesion, under local anaesthesia in an outpatient setting. Additionally, to compare the incidence of procedure-related complications with a cohort of patients undergoing transrectal ultrasonography-guided (TRB-US) and transrectal MRI-guided biopsies (TRB-MRI). PATIENTS AND METHODS: This was an observational cohort study in men who underwent TPB-US prostate biopsy in a large teaching hospital. For each participant, prostate-specific antigen level, clinical tumour stage, prostate volume, MRI parameters, number of (targeted) prostate biopsies, biopsy International Society of Uropathology (ISUP) grade and procedure-related complications were assessed. csPCa was defined as ISUP grade ≥2. Antibiotic prophylaxis was only given in those with an increased risk of urinary tract infection. RESULTS: A total of 1288 TPB-US procedures were evaluated. The overall detection rate for PCa in biopsy-naive patients was 73%, and for csPCa it was 63%. The incidence of hospitalization was 1% in TPB-US (13/1288), compared to 4% in TRB-US (8/214) and 3% in TRB-MRI (7/219; P = 0.002). CONCLUSIONS: Contemporary combined systematic and target TPB-US with MRI cognitive fusion is easy to perform in an outpatient setting, with a high detection rate of csPCa and a low incidence of procedure-related complications.

Using prostate-specific membrane antigen positron-emission tomography to guide prostate biopsies and stage men at high-risk of prostate cancer.

OBJECTIVE: To assess whether a diagnostic pathway in which prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) is used as a single imaging modality is feasible to guide targeted biopsy and to detect clinically significant prostate cancer (csPCa) in biopsy-naïve men at high-risk of disease. PATIENTS AND METHODS: A total of 60 men with a prostate-specific antigen (PSA) level of 20-50 ng/mL underwent 18 F-PSMA(DCFPyL)-PET/CT prior to prostate biopsies in this prospective, non-randomised cohort study. Magnetic resonance imaging (MRI) was not performed. Using a 12-segment mapping model of the prostate, PSMA-guided targeted biopsy was performed along with systematic biopsies. The detection rate of PCa and csPCa was assessed for combined systematic and targeted biopsy, and for targeted biopsy only. csPCa was defined as a prostate biopsy with an International Society of Uropathology (ISUP) Grade Group ≥2. RESULTS: Lesions suspicious for PCa in the prostate gland were observed on all PSMA-PET/CTs. A total of 27/60 men (45%) already had metastatic disease on staging 18 F-PSMA(DCFPyL)-PET/CT. Combined PSMA-guided targeted and systematic biopsies detected PCa in 56/60 (93.3%) patients, with 52 of them (92.9%) having csPCa. PSMA-guided targeted biopsy, if performed as a single biopsy modality, identified PCa in 52/60 men (86.7%) and in 27/27 men (100%) men with metastases. CONCLUSIONS: Using the PSMA-driven single imaging modality pathway in biopsy-naïve men at high-risk of PCa, a substantial number of diagnostic MRI scans could be avoided while at the same time obtaining adequate targeting, staging, and detection of csPCa.

Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients.

BACKGROUND: Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology, and evaluate the respective diagnostic performance characteristics of the PURE probability calculator developed with machine learning (ML) algorithms comparing regression versus classification algorithms. METHODS: Eight ML models (i.e. logistic regression, LASSO regression, RIDGE regression, decision tree, bagged trees, boosted trees, XGBoost trees, RandomForest) were trained on 5.323 unique patients with 52 different features, and evaluated on diagnostic performance of PURE within 30 days of discharge from the department of Urology. RESULTS: Our main findings were that performances from classification to regression algorithms had good AUC scores (0.62-0.82), and classification algorithms showed a stronger overall performance as compared to models trained with regression algorithms. Tuning the best model, XGBoost, resulted in an accuracy of 0.83, sensitivity of 0.86, specificity of 0.57, AUC of 0.81, PPV of 0.95, and a NPV of 0.31. CONCLUSIONS: Classification models showed stronger performance than regression models with reliable prediction for patients with high probability of readmission, and should be considered as first choice. The tuned XGBoost model shows performance that indicates safe clinical appliance for discharge management in order to prevent an unplanned readmission at the department of Urology.

STUDY NEED AND IMPORTANCE: Unplanned readmissions form a consistent problem for many hospitals. Unplanned readmission rates can go up as high as to 35%, and may differ significantly between respective hospital departments. In addition, in the field of Urology readmission rates can be greatly influenced by type of surgery performed and unplanned readmissions in patients can go up as high as 26%. Although predicting unplanned readmissions for individual patients is often complex, due to multiple factors that need to be taken into account (e.g. functional disability, poor overall condition), there is evidence that these can be prevented when discharge management is evaluated with an objective measuring tool that facilitate such risk stratification between high and low risk patients. However, to the best of our knowledge, the latter risk stratification using ML driven probability calculators in the field of Urology have not been evaluated to date. Using ML, calculated risk scores based on analysing complex data patterns on patient level can support safe discharge and inform concerning the risk of having an unplanned readmission. WHAT WE FOUND: Eight ML models were trained on 5.323 unique patients with 52 different features, and evaluated on diagnostic performance. Classification models showed stronger performance than regression models with reliable prediction for patients with high probability of readmission, and should be considered as first choice. The tuned XGBoost model shows performance that indicates safe clinical appliance for discharge management in order to prevent an unplanned readmission at the department of Urology. Limitations of our study were the quality and presence of patient data on features, and how to implement these findings in clinical setting to transition from predicting to preventing unplanned readmissions. INTERPRETATION FOR CLINICIANS: ML models based on classification should be first choice to predict unplanned readmissions, and the XGBoost model showed the strongest results.

The optimal duration of frequency-volume charts related to compliance and reliability.

AIMS: To assess Frequency-volume charts (FVCs) for the yield of additional recorded days and the ideal duration of recording related to compliance and reliability. METHODS: Of 500 consecutive urologic outpatients willing to complete a 7-day FVC, 378 FVCs were evaluable. During seven consecutive days every voiding time and volume were recorded. Missed entries were indicated with a coded letter, thereby assessing the true frequency and compliance. Reliability is the agreement of the day-to-day FVC parameters with the 7-day FVC pattern. Single-day reliability was assessed and used in the Spearman-Brown formula. RESULTS: FVCs of 228 male and 150 females were evaluated. Mean age was 55.2 years (standard deviation [SD]: 16.2 years), and mean 24-hr urine production was 1,856 ml (SD: 828 ml). The percentage of patients with complete FVCs decreased from 78% on day 2 to 58% on day 7, and dropped below 70% after 4 days. Single-day reliability was r = 0.63 for nocturnal urine production, r = 0.72 for 24-hr urine production, and r = 0.80 for mean voided volume. At 5 days, reliability of 90% was achieved for all parameters. CONCLUSIONS: With each additional day, FVCs showed a decrease in compliance and an increase in reliability. At day 3, reliability of 80% was achieved for all FVC parameters, but compliance dropped to 73%. Beyond 5 days, the yield of additional recorded days was limited. We advocate an FVC duration of 3 days, but the duration may be shortened or extended depending on the goal of the FVC.

Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data.

Objective: When correcting for the "class imbalance" problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Materials and Methods: Multiple classification algorithms were trained on 7 resampled datasets: no correction, random undersampling, 4 ratios of Synthetic Minority Oversampling Technique (SMOTE), and random oversampling with the Adaptive Synthetic algorithm (ADASYN). Performance was evaluated in Area Under the Curve (AUC), precision, recall, Brier score, and calibration metrics. A case study on prediction modeling for 30-day unplanned readmissions in previously admitted Urology patients was presented. Results: For most algorithms, using resampled data showed a significant increase in AUC and precision, ranging from 0.74 (CI: 0.69-0.79) to 0.93 (CI: 0.92-0.94), and 0.35 (CI: 0.12-0.58) to 0.86 (CI: 0.81-0.92) respectively. All classification algorithms showed significant increases in recall, and significant decreases in Brier score with distorted calibration overestimating positives. Discussion: Imbalance correction resulted in an overall improved performance, yet poorly calibrated models. There can still be clinical utility due to a strong discriminating performance, specifically when predicting only low and high risk cases is clinically more relevant. Conclusion: Resampling data resulted in increased performances in classification algorithms, yet produced an overestimation of positive predictions. Based on the findings from our case study, a thoughtful predefinition of the clinical prediction task may guide the use of resampling techniques in future studies aiming to improve clinical decision support tools.

A cutoff value based on analysis of a reference population decreases overestimation of the prevalence of nocturnal polyuria.

PURPOSE: We sought criteria for nocturnal polyuria in asymptomatic, nonurological adults of all ages by reporting reference values of the ratio of daytime and nighttime urine volumes, and finding nocturia predictors. MATERIALS AND METHODS: Data from a database of frequency-volume charts from a reference population of 894 nonurological, asymptomatic volunteers of all age groups were analyzed. The nocturnal polyuria index and the nocturia index were calculated and factors influencing these values were determined by multivariate analysis. RESULTS: The nocturnal polyuria index had wide variation but a normal distribution with a mean ± SD of 30% ± 12%. The 95th percentile of the values was 53%. Above this cutoff a patient had nocturnal polyuria. This value contrasts with the International Continence Society definition of 33% but agrees with several other reports. On multivariate regression analysis with the nocturnal polyuria index as the dependent variable sleeping time, maximum voided volume and age were the covariates. However, the increase in the nocturnal polyuria index by age was small. Excluding polyuria and nocturia from analysis did not alter the results in a relevant way. The nocturnal voiding frequency depended on sleeping time and maximum voided volume but most of all on the nocturia index. CONCLUSIONS: The prevalence of nocturnal polyuria is overestimated. We suggest a new cutoff value for the nocturnal polyuria index, that is nocturnal polyuria exists when the nocturnal polyuria index exceeds 53%. The nocturia index is the best predictor of nocturia.

The international prostate symptom score overestimates nocturia assessed by frequency-volume charts.

PURPOSE: We analyzed differences in nocturia, as estimated by the International Prostate Symptom Score and 7-day frequency-volume charts. MATERIALS AND METHODS: A total of 398 forms were collected from 500 consecutive urological outpatients willing to record a 7-day frequency-volume chart. All patients completed a general questionnaire, an International Prostate Symptom Score, and a bladder symptom and bother score. Missed recordings were indicated by a coded letter. Patients who lacked essential data, bedtimes or an International Prostate Symptom Score, or who recorded the frequency-volume chart for less than 5 days were excluded from study. RESULTS: A total of 186 men and 115 women with a mean age of 56 years were evaluable. In 10.6% of patients no nocturia occurred. Of those with nocturia 70% and 34% experienced nocturia a mean of 1 or more and 2 or more times, respectively. In 43% of patients the International Prostate Symptom Score equaled calculated categorized nocturia while 50% had a higher International Prostate Symptom Score nocturia score than calculated nocturia. On univariate analysis the correlation of International Prostate Symptom Score question 7 with mean nocturia increased with frequency-volume chart duration (day 1 r = 0.52 to day 3 r = 0.63). On longer duration frequency-volume charts the correlation showed no further increase. Multivariate regression analysis revealed that the nocturia score was determined by mean nocturia in the frequency-volume chart, the nocturia bother score and patient age. CONCLUSIONS: The International Prostate Symptom Score nocturia score overestimated nocturia in most patients, as derived from a 7-day frequency-volume chart. When scoring International Prostate Symptom Score nocturia question 7, patients included a degree of bother. The correlation of question 7 with mean nocturia increased with frequency-volume chart duration until day 3.

Inter- and Intraobserver Agreement in Measuring Urolithiasis Density on Nonenhanced Computed Tomography.

Introduction: Treatment choice for urolithiasis is partially based on measuring stone density in HU on nonenhanced computed tomography (NECT). Interobserver variability in these measurements could have treatment consequences. This study aims to assess the observer agreement of measuring HU and whether the use of a protocol leads to a better agreement. Materials and Methods: We retrospectively included 155 consecutive NECTs of patients with stones ≥4 mm. Five observers (two radiologists, one urologist, one urology resident, and one radiology resident) assessed all anonymized NECTs four times in randomized order. HU was measured without instruction (rounds 1 and 2) and subsequently using two protocols (A and B, rounds 3 and 4). Protocols comprised using bone setting, zoom, and measuring HU without the penumbra, in either three (A) or one (B) axial plane. The inter- and intraobserver agreement was evaluated using the intraclass correlation coefficient (ICC). Results: Interobserver agreement on HU measurement without protocol was as follows: ICC = 0.84 (confidence interval [CI]: 0.79-0.87). Agreement diminished with protocol A, ICC = 0.62 (CI: 0.37-0.76), and improved with protocol B, ICC = 0.90 (CI: 0.86-0.92). Intraobserver agreement without protocol was ICC = 0.87, with protocol A, ICC = 0.87, and with protocol B, ICC = 0.93. The biggest improvement was seen for urologists' agreement from no protocol to protocol B, where ICC improved from 0.81 (CI: 0.70-0.87) to 0.91 (CI: 0.84-0.94). Conclusions: Observer agreement of HU measurement of urolithiasis without protocol is already good but using zoom, bone setting, and measuring in a representative plane is recommended. This protocol results in higher agreement, especially among urologists. Measuring in three axial planes does not increase agreement.

Upper Tract Urothelial Carcinoma Grade Prediction Based on the Ureteroscopic Appearance: Caution Should be Taken.

OBJECTIVES: To investigate the diagnostic accuracy, inter-rater and intrarater agreement of grade predictions based on the visual appearance of papillary upper tract urothelial carcinoma (UTUC) during digital ureteroscopy. METHODS: Nine urologists predicted the histopathologic grade of 64 papillary UTUC (low-grade vs high-grade) by assessing the visual appearance of the tumors in videos from digital ureteroscopy. The diagnostic accuracy was estimated by comparing the grade predictions with the histopathology from colocalized biopsies. Inter-rater agreement was assessed by pairwise inter-rater percentage agreement and Fleiss Kappa analysis. The videos were rated in a random order again 30 days after the first assessment to evaluate the intrarater percentage agreement. RESULTS: Low-grade tumors were predicted correctly in 37%-85% of the cases with a median concordance of 59% for questionnaire 1 and 66% for questionnaire 2. High-grade tumors were predicted correctly in 26%-91% of the cases with a median concordance of 52% and 61% for each questionnaire. The median pairwise inter-rater percentage agreement was 66% for both questionnaires with a Fleiss Kappa of 0.29 and 0.38, respectively. The median intrarater percentage agreement was 81%. CONCLUSION: The histopathologic grade of UTUC is essential to the risk-stratification for treatment selection. Predictions of the histopathologic grade based on the visual appearance of papillary UTUC with digital ureteroscopy are often incorrect in comparison with biopsy results and yield low inter-rater agreement. Urologists must be aware of these limitations in the assessment of the tumor grade during digital ureteroscopy to warrant good clinical practice.

Can RENAL and PADUA nephrometry indices predict complications of laparoscopic cryoablation for clinical stage T1 renal tumors?

OBJECTIVE: Assessment of anatomical complexity with the RENAL (radius; exophytic/endophytic; nearness; anterior/posterior; location) and preoperative aspects and dimensions used for anatomical classification (PADUA) nephrometry indices is used to predict complications related to surgical extirpation treatment for patients with clinical T1a/b renal mass. This single center study aims to investigate the value of these indices to predict complications in a cohort of patients treated with laparoscopic cryoablation (LCA) for cT1 renal mass. MATERIALS AND METHODS: Single institution data from consecutive LCA procedures were prospectively collected from December 2006 to April 2013. Renal mass anatomical complexity was categorized according to RENAL and PADUA indices. Comorbidity was assessed by the Charlson-index. Intraoperative complications (IOCs) were reviewed and categorized: blood loss >100 mL, conversion, tumor fracture, and incomplete ablation. Postoperative complications (POCs) were graded using the modified Clavien-index. Univariate and multivariate logistic regression models addressed the risk for complications. RESULTS: Ninety-nine LCA procedures were included. The median RENAL-score was 7.0 (standard deviation [SD] 1.7), and the median PADUA-score was 8.0 (SD 1.6). IOC occurred in 19 procedures (19%). The risk for IOC was significantly correlated (p<0.05) with tumor diameter (mm), surface, volume, the RENAL domains "R-size," "N-nearness to collecting system," "RENAL score," and the PADUA domain "diameter." In multivariate analysis with surgical complication as the independent variable, tumor diameter, surface, and volume were determining factors. A threshold was set for 35 mm tumor diameter, it being predictive for an increased risk for IOC performing LCA. Twenty-three POC occurred in 20 patients. On univariate analysis, the RENAL domain "nearness to collecting system," and no PADUA domains, had a significant association with POC. CONCLUSION: The RENAL score, and not the PADUA score, is associated with a higher risk for IOC. A noncategorized method of scoring tumor diameter showed a more significant correlation with the risk for IOC than the categorized method of the nephrometry indices. As a result a threshold diameter of 35 mm was established.

[Diagnostic image. A woman with a calcified structure in the bladder]. / Een vrouw met een kalkhoudende structuur in de blaas.

A 79-year-old woman suffered from microscopic haematuria following a symptomatic cystitis. Abdominal ultrasound investigation suggested a bladder stone. Cystoscopy revealed a calcified bladder tumour. After resection pathologic examination showed a superficial invasive urothelial carcinoma.

A cross-sectional study of the international prostate symptom scores related to age and gender in Dutch adults reporting no voiding complaints.

OBJECTIVE: To determine the IPSS in a selected population reporting no voiding complaints. SUBJECTS AND METHODS: 1143 adults without voiding complaints were included. They were divided over both sexes and all decades. All subjects filled out questionnaires including the IPSS. Statistical analysis was aimed at relating the IPSS to age and gender. RESULTS: The IPSS in both sexes shows a gradual significant increase in consecutive age groups. Men in the third age decade have a mean score of 2.8, while men older than 70 years of age have a score of 7.0. In women these scores are 4.0 and 5.6 respectively. The increase is about equally caused by storage and voiding scores. The items addressing weak stream in men and nocturia and urgency in men and women are the major factors causing the correlation with age. Nearly 17% of all subjects have moderate symptom scores and 1% has severe scores. CONCLUSION: In both adult men and women reporting no voiding complaints the IPSS increases with age. This rise is more prominent in males.

The effect of biofeedback physical therapy in men with Chronic Pelvic Pain Syndrome Type III.

UNLABELLED: Recent studies suggest that the symptoms of chronic non-bacterial prostatitis (CP) or Chronic Pelvic Pain Syndrome (CPPS) may be due to or associated with pelvic floor muscle dysfunction. Therapies aimed to improve relaxation and proper use of the pelvic floor muscles such as biofeedback physical therapy and pelvic floor re-education are expected to give symptom improvement. The objective of this study was to evaluate the effect of biofeedback physical therapy on the symptoms of men with CPPS. MATERIALS AND METHODS: Between March 2000 to March 2004, 33 consecutive men were diagnosed with CP/CPPS based on history including the NIH-CPSI questionnaire and physical examination including pelvic floor muscle tonus, urinalysis, uroflowmetry with residual urine measurement and transrectal ultrasonography of the prostate. All patients participated in a pelvic floor biofeedback re-educating program. A rectal EMG probe was used to measure resting tone of the pelvic floor muscles and was helpful for instruction pelvic floor muscles contraction and relaxation. RESULTS: Two of the 33 men dropped out. In the remaining 31 men, mean age 43.9 years (range 23-70), the mean total Chronic Prostatitis Symptom Index (NIH-CPSI) changed from 23.6 (range 11-34) at baseline to 11.4 (range 1-25) after treatment (p<0.001). The mean value of the pelvic floor muscle tonus was 4.9 at diagnosis (range 2.0-10.0) and decreased to 1.7 (range 0.5-2.8) after treatment (p<0.001). CONCLUSIONS: Our study clearly demonstrates a significant effect of biofeedback physical therapy and pelvic floor re-education for CP/CPPS patients, leading to a significant improvement of the symptom score. The correlation between the pelvic muscle tonus results with NIH-CPSI score is highly suggestive that the pelvic floor plays an important role in the pathophysiology of CP/CPPS.