Purpose: To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. Methods: During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes. Results: Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm³. Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%. Conclusion: As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and “learns” the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system.

Purpose: To validate the output of a machine learning-based software as an intelligible interface for predicting multiple outcomes after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy’s stone score (GSS) and the Clinical Research Office of Endourological Society (CROES) nomogram. Methods: Data from 146 adult patients (87 males, 59%) who underwent PCNL at our institute were used. To validate the system, accuracy of the software for predicting each postoperative outcome was compared to the actual outcome. Similarly, preoperative data were analyzed with GSS and CROES nomograms to determine stone-free status as predicted by these nomograms. A receiver operating characteristic (ROC) curve was generated for each scoring system, and the area under curve (AUC) was calculated and used to assess the predictive performance of all three models. Results: Overall stone-free rate (SFR) was 72.6% (106/146). Forty of 146 patients (27.4%) were scheduled for Forty two ancillary procedures (shock wave lithotripsy (SWL) (n= 31) or repeat PCNL (n=11)) to manage residual renal stones. Overall, the machine learning system predicted the PCNL outcomes with an accuracy ranging between 80% and 95.1%. For predicting the stone-free status, the area under the ROC curve for the software (0.915) was significantly larger than the AUC for GSS (0.615) or CROES nomograms (0.621) (p<0.001). Conclusion: At the internal institutional level, the machine learning-based software was a promising tool for recording, processing and predicting outcomes after PCNL. Validation of this system against an external dataset is highly recommended before its widespread application.

This investigation reports the mineralogy and possible pathological significance of urinary stones removed from patients in Fars province, Iran. X-ray diffraction (XRD), scanning electron microscopy (SEM) and polarizing microscope (PM) techniques were used to investigate the mineralogical compositions of urinary stones. The identified mineral components include whewellite, weddellite, hydroxyapatite, uricite and cystine. These techniques revealed that the whewellite and uricite were the most common mineral phases. Platy-like/monoclinic whewellite, prismatic/monoclinic uric acid and hexagonal cystine crystals were revealed by SEM. Biominerals (calcium carbonate) and quartz were also identified in PM images. Of the variables determining the type of precipitated minerals, the effects of pH on depositional conditions proved to be the most apparent parameter, as shown by occurrences and relationships among the studied minerals. Our results revealed the importance of detailed knowledge of mineralogical composition in assessing the effects of age and sex. The highest incidence of urinary stones was observed in the 40–60 age group. Calcium oxalate and uric acid stones are more frequent in men than women. Finally, the study concluded that knowledge of the mineralogical composition of urinary stones is important as it helps the scientific community to explain the chemistry and the etiology of the calculi in the urinary system.

Objectives: To describe the management protocol in cases with massive hemorrhage after percutaneous nephrolithotomy (PCNL) with a failed angioembolization or when angioembolization is not available. Patients and methods: Between October 2006 and December 2012, the charts of patients who had undergone PCNL and were complicated with massive post procedural bleeding unresponsive to conservative management were reviewed. Those cases in whom angioembolization had failed, or was unavailable, or could not be afforded by the patient were selected and studied. These patients underwent open surgical exploration through a midline transperitoneal or a flank retroperitoneal approach. In both approaches, kidney mobilization outside the Gerota's fascia, temporal renal pedicle clamping and partial nephrectomy or renorrhaphy were done in a stepwise manner. Results: During the study period, we had 8 patients for whom angioembolization had failed (n = 4), was not available (n = 2) or the patient could not afford it (n = 2). Median patients' age was 31 years (range 16-59 years). We did a partial nephrectomy in 2 and renorrhaphy in 6 of patients with a successful outcome. Median operative time was 2.25 h and median warm ischemia time was 26 min (range 24-42 min). After a median follow up period of 21 months, the involved renal unit, in all cases, remained functional in the postoperative intravenous urography. Conclusion: Massive hemorrhage after PCNL when angioembolization failed or was not feasible due to any reason could be controlled by partial nephrectomy or renorrhaphy with the same principles as that used for surgical exploration in patients with high grade renal trauma. (C) 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.