Friedrich-Carl von Rundstedt1,2, Jason M Scovell1, Smriti Agrawal3, Jacques Zaneveld4, Richard E Link1,5,6. 1. Scott Department of Urology, Baylor College of Medicine, Houston, TX, USA. 2. Department of Urology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany. 3. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. 4. Lazarus 3D LLC, Houston, TX, USA. 5. Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA. 6. Center for Reproductive Medicine, Baylor College of Medicine, Houston, TX, USA.
Abstract
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 3D-print 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: 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.
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 3D-print 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 patienttumour. 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: 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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