Small RF after ESWL is clinically significant in children and increase the chance of adverse clinical outcome. These patients require close followup, particularly those with identifiable predisposing disorders.

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Low grade VUR in DS cases has an outcome similar to that of VUR in SS cases. Females with moderate or high grade VUR and a DS should be treated differently because they present more often with complications.

We consider RALP as the technique of choice for surgeons who want to increase their reconstructive laparoscopic skills. It provides an additional technique when skilled laparoscopists are faced with technically difficult pyeloplasties.

These findings suggest that the artifactual supranormal differential renal function that is observed during renographic evaluation of large hydronephrotic kidneys actually exists. Parenchymal proximity and distribution in relation to the pelvis are critical determinants. These findings suggest that the conjugate view technique may be more suitable for renographic evaluation of large hydronephrotic kidneys.

The outcome of VUR is similar in children with or without PUD. Therefore, treatment of these patients should not differ. Surgery should be reserved for patients with breakthrough infection or renal scar progression.

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Introduction: We aimed to develop an explainable machine learning (ML) model to predict side-specific extraprostatic extension (ssEPE) to identify patients who can safely undergo nerve-sparing radical prostatectomy using preoperative clinicopathological variables. Methods: A retrospective sample of clinicopathological data from 900 prostatic lobes at our institution was used as the training cohort. Primary outcome was the presence of ssEPE. The baseline model for comparison had the highest performance out of current biopsy-derived predictive models for ssEPE. A separate logistic regression (LR) model was built using the same variables as the ML model. All models were externally validated using a testing cohort of 122 lobes from another institution. Models were assessed by area under receiver operating characteristic (AUROC), precision-recall (AUPRC), calibration, and decision curve analysis. Model predictions were explained using Shapley Additive exPlanations. This tool was deployed as a publicly available web application. Results: Incidence of ssEPE in the training and testing cohorts were 30.7 and 41.8%, respectively. The ML model achieved AUROC 0.81 (LR 0.78, baseline 0.74) and AUPRC 0.69 (LR 0.64, baseline 0.59) on the training cohort. On the testing cohort, the ML model achieved AUROC 0.81 (LR 0.76, baseline 0.75) and AUPRC 0.78 (LR 0.75, baseline 0.70). The ML model was explainable, well-calibrated, and achieved the highest net benefit for clinically relevant cutoffs of 10–30%. Conclusions: We developed a user-friendly application that enables physicians without prior ML experience to assess ssEPE risk and understand factors driving these predictions to aid surgical planning and patient counselling (www.ssepe.ml).