Cite as: Can Urol Assoc J 2014;8(11-12):e918-20. http://dx.doi.org/10.5489/cuaj.2289 Published online December 15, 2014. AbstractHorseshoe kidney has an incidence rate ranging from 1 in 400 to 1 in 1000, with a 2:1 ratio in men. It also has a predilection for chromosomal aneuploidies. From a pathophysiology standpoint, this anomaly occurs during the second to sixth week of gestation when the inferior portion of the metanephric blastema fuses to form an isthmus, commonly in the lower renal pole (90%). As a result of this fusion, the kidney may not bypass the inferior mesenteric artery and is impeded in its ascent. With an aberrant anatomical orientation and location, complications arise including hydronephrosis, renal calculi and a twofold risk of Wilms tumour. Despite these findings, the association of renal cell carcinoma (RCC) within a horseshoe kidney is extremely rare and fewer than 200 cases have been described. Therapeutically speaking, partial nephrectomies are the gold standard of treatment for renal tumours smaller than 4 cm in diameter, with a growing indication to accomplish this procedure by laparoscopic or robotic means. We report a case of an asymptomatic 58-year-old male with an incidental computed tomography scan finding of a 4-cm solid mass in the right moiety of a horseshoe kidney. He was treated by laparoscopic partial nephrectomy. There have only been 2 other reported cases to our knowledge on a laparoscopic partial nephrectomy in a horseshoe kidney for RCC. We believe that, in experienced hands, the laparoscopic approach may be used successfully for this clinical situation.

No abstract

Purpose: We assess the variations between post-graduate trainees (PGTs) and attending urologists in applying the Revised ClavienDindo Classification System (RCCS) to urological complications. Methods: Twenty postoperative complications were selected from urology service Quality Assurance meeting minutes spanning 1 year at a tertiary care centre. The cases were from adult and pediatric sites and included minor and major complications. After a briefing session to review the RCCS, the survey was administered to 16 attending urologists and 16 PGTs. Concordance rates between the two groups were calculated for each case and for the whole survey. Inter-rater agreement was calculated by kappa statistics. Results: There was good overall agreement rate of 81 % (range: 30-100) when both groups were compared. Thirteen of the 20 cases (65%) held an agreement rate above 80% (k = 0.753, p < 0.001) including 3 (15%) cases with 100% agreement. There were only 2 cases where the scores given by PGTs were significantly different from that given by attending urologists (p ≤ 0.03). There was no significant difference between both groups in terms of overall RCCS grades (p = 0.12). When all participants were compared as one group, there was good overall inter-rater agreement rate of 75% (k = 0.71). Although the percent of overall agreement rate among PGTs was higher than the attending urologists (82% [k = 0.79] vs. 69% [k = 0.64]), this was not significantly different (p = 0.68).

No abstract

Purpose:We sought to automate R.E.N.A.L. (for radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry scoring of preoperative computerized tomography scans and create an artificial intelligence-generated score (AI-score). Subsequently, we aimed to evaluate its ability to predict meaningful oncologic and perioperative outcomes as compared to expert human-generated nephrometry scores (H-scores).Materials and Methods:A total of 300 patients with preoperative computerized tomography were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer at a single institution. A deep neural network approach was used to automatically segment kidneys and tumors, and geometric algorithms were developed to estimate components of R.E.N.A.L. nephrometry score. Tumors were independently scored by medical personnel blinded to AI-scores. AI- and H-score agreement was assessed using Lin’s concordance correlation and their predictive abilities for both oncologic and perioperative outcomes were assessed using areas under the curve.Results:Median age was 60 years (IQE 51–68), and 40% were female. Median tumor size was 4.2 cm and 91.3% had malignant tumors, including 27%, 37% and 24% with high stage, grade and necrosis, respectively. There was significant agreement between H-scores and AI-scores (Lin’s ⍴=0.59). Both AI- and H-scores similarly predicted meaningful oncologic outcomes (p <0.001) including presence of malignancy, necrosis, and high-grade and -stage disease (p <0.003). They also predicted surgical approach (p <0.004) and specific perioperative outcomes (p <0.05).Conclusions:Fully automated AI-generated R.E.N.A.L. scores are comparable to human-generated R.E.N.A.L. scores and predict a wide variety of meaningful patient-centered outcomes. This unambiguous artificial intelligence-based scoring is intended to facilitate wider adoption of the R.E.N.A.L. score.