4.6 Article

Funding Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy

Journal

BJU INTERNATIONAL
Volume 119, Issue 4, Pages 598-604

Publisher

WILEY
DOI: 10.1111/bju.13712

Keywords

robotic partial nephrectomy; RALPN; surgical simulation; 3D printing; #KidneyCancer; #KCSM

Funding

  1. NIGMS NIH HHS [T32 GM088129] Funding Source: Medline

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Objective To describe our experience using patient-specific tissue-like kidney models created with advanced three-dimensional (3D)-printing technology for preoperative planning and surgical rehearsal prior to robot-assisted laparoscopic partial nephrectomy (RALPN). Patients and Methods A feasibility study of 10 patients with solid renal masses who underwent RALPN after preoperative rehearsal using 3Dprint kidney models. A single surgeon performed all surgical rehearsals and procedures. Using standard preoperative imaging and 3D reconstruction, we generated pre-surgical models using a silicone-based material. All surgical rehearsals were performed using the da Vinci robotic system (Intuitive Surgical Inc., Sunnyvale, CA, USA) before the actual procedure. To determine construct validity, we compared resection times between the model and actual tumour in a patient-specific manner. Using 3D laser scanning in the operating room, we quantified and compared the shape and tumour volume resected for each model and patient tumour. Results We generated patient-specific models for 10 patients with complex tumour anatomy. R. E. N. A. L. nephrometry scores were between 7 and 11, with a mean maximal tumour diameter of 40.6 mm. The mean resection times between model and patient (6: 58 vs 8: 22 min, P = 0.162) and tumour volumes between the computer model, excised model, and excised tumour (38.88 vs 38.50 vs 41.79 mm3, P = 0.98) were not significantly different. Conclusions patient-We have developed a patient-specific pre-surgical simulation protocol for RALPN. We demonstrated construct validity and provided accurate representation of enucleation time and resected tissue volume. This simulation platform can assist in surgical decision-making, provide preoperative rehearsals, and improve surgical training.

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