Three Dimensional Printing Technology Used to Create a High-Fidelity Ureteroscopy Simulator: Development and Validity Assessment (Rein-3D-Print-UroCCR-39).

OBJECTIVE: To create and assess the validity of a high-fidelity, three dimensional (3D) printed, flexible ureteroscopy simulator resulting from a real case. METHODS: A patient's CT scan was segmented to obtain a 3D model in .stl format, including the urinary bladder, ureter and renal cavities. The file was printed and a kidney stone was introduced into the cavities. The simulated surgery consisted of monobloc stone extraction. Nineteen participants split into 3 groups according to their level (6 medical students, 7 residents and 6 urology fellows) performed the procedure twice at a 1-month interval. They were rated according to a global score and a task-specific score, based on an anonymized, timed video recording. RESULTS: Participants demonstrated a significant improvement between the 2 assessments, both on the global score (29.4 vs 21.9 points out of 35; P < .001) and the task-specific score (17.7 vs 14.7 points out of 20; P < .001) as well as procedure time (498.5 vs 700 seconds; P = .001). Medical students showed the greatest progress for the global score (+15.5 points (mean), P = .001) and the task-specific score (+6.5 points (mean), P < .001). 69.2% of participants considered the model as visually quite realistic or highly realistic and all of them judged it quite or extremely interesting for intern training purposes. CONCLUSION: Our 3D printed ureteroscopy simulator was able to enhance the progress of medical students who are new to endoscopy, whilst being valid and reasonably priced. It could become part of a training program in urology, in line with the latest recommendations for surgical education.

Machine-learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma (UroCCR-15).

OBJECTIVES: To assess the impact of pathological upstaging from clinically localized to locally advanced pT3a on survival in patients with renal cell carcinoma (RCC), as well as the oncological safety of various surgical approaches in this setting, and to develop a machine-learning-based, contemporary, clinically relevant model for individual preoperative prediction of pT3a upstaging. MATERIALS AND METHODS: Clinical data from patients treated with either partial nephrectomy (PN) or radical nephrectomy (RN) for cT1/cT2a RCC from 2000 to 2019, included in the French multi-institutional kidney cancer database UroCCR, were retrospectively analysed. Seven machine-learning algorithms were applied to the cohort after a training/testing split to develop a predictive model for upstaging to pT3a. Survival curves for disease-free survival (DFS) and overall survival (OS) rates were compared between PN and RN after G-computation for pT3a tumours. RESULTS: A total of 4395 patients were included, among whom 667 patients (15%, 337 PN and 330 RN) had a pT3a-upstaged RCC. The UroCCR-15 predictive model presented an area under the receiver-operating characteristic curve of 0.77. Survival analysis after adjustment for confounders showed no difference in DFS or OS for PN vs RN in pT3a tumours (DFS: hazard ratio [HR] 1.08, P = 0.7; OS: HR 1.03, P > 0.9). CONCLUSIONS: Our study shows that machine-learning technology can play a useful role in the evaluation and prognosis of upstaged RCC. In the context of incidental upstaging, PN does not compromise oncological outcomes, even for large tumour sizes.

3D-Image guided robotic-assisted partial nephrectomy: a multi-institutional propensity score-matched analysis (UroCCR study 51).

PURPOSE: Robot-assisted partial nephrectomy (RAPN) is a difficult procedure with risk of significant perioperative complications. The objective was to evaluate the impact of preoperative planning and intraoperative guidance with 3D model reconstructions on perioperative outcomes of RAPN. METHODS: We conducted a retrospective analysis of all patients who underwent RAPN for kidney tumor by three high-volume expert surgeons from academic centers. Clinical data were collected prospectively after written consent into the French kidney cancer network database UroCCR (CNIL-DR 2013-206; NCT03293563). Our cohort was divided into two groups: 3D-Image guided RAPN group (3D-IGRAPN) and control group. A propensity score according to age, pre-operative renal function and RENAL tumor complexity score was used. Both surgical techniques were compared in terms of perioperative outcomes. RESULTS: The initial study cohort included 230 3D-IGRAPN and 415 control RAPN. Before propensity-score matching, patients in the 3D-IGRAPN group had a larger tumor (4.3 cm vs. 3.5 cm, P < 0.001) and higher RENAL complexity score (9 vs. 8, P < 0.001). Following propensity-score matching, there were 157 patients in both groups. The rate of major complications was lower for patients in the 3D-IGRAPN group (3.8% vs. 9.5%, P = 0.04). The median percentage of eGFR variation recorded at first follow-up was lower in the 3D-IGRAPN group (- 5.6% vs. - 10.5%, P = 0.002). The trifecta achievement rate was higher in the 3D-IGRAPN group (55.7% vs. 45.1%; P = 0.005). CONCLUSION: Three-dimensional kidney reconstructions use for pre-operative planning and intraoperative surgical guidance lowers the risk of complications and improve perioperative clinical outcomes of RAPN.