MP68-18: Console-integrated real-time three-dimensional (3D) physical model navigation for robotic partial nephrectomy in patient with highly complex renal tumors (RENAL nephrometry score&[ge]7): prospective case-matched study

Jung Kwon Kim, Hakmin Lee, Jong Jin Oh, Sangchul Lee, Seong Jin Jeong, Sung Kyu Hong, Sang Eun Lee, Seok-Soo Byun

Introduction: Three-dimensional (3D) images can be used to facilitate patient education, surgical planning, as well as both a research and marketing tool in field of surgery. In current study, we tried to evaluate the effectiveness of 3D physical model for robotic partial nephrectomy (RPN) in patient with highly complex renal tumors (RENAL nephrometry score [RNS] = 7)

Methods: A total of 80 patients who underwent RPN were included in current prospective case-matched study [case group (n = 40, application of 3D physical model during RPN) vs. matching group (n = 40), Figure 1]. RPN performed by single surgeon (SSB) who has experience of over 500 cases of RPN. RPN procedure consisted of 6 steps: (1) vessel preparation for clamping; (2) tumor detection and dissection; (3) robotic ultrasound; (4) tumor resection; (5) calyx repair and hemostasis; (6) renorrhaphy.

Results: Both groups were well-balanced in all baseline characteristics. Mean and median RNS were 9.54 and 10, respectively. Total agreement rate in RNS was 92.5% (37/40). Mean and median console time and warm ischemic time (WIT) were 71.51 / 69.67 (min) and 23.1 / 23.5 (min), respectively. In comparative analysis of each step time including console time and WIT, we found the significant differences in console time, vessel preparation time, detection and dissection time, and calyx repair and hemostasis time between the two groups (all, p < 0.05). On multivariate logistic regression analysis, tumor radius (p < 0.001) and application of 3D physical model (p = 0.009) were identified as significant predictors of console time = 70 (min). In subgroup analysis according to the each component, we also found some effectiveness of 3D physical model (Figure 2).

Conclusions: We found the effectiveness of 3D physical models for RPN in specific patient groups with highly complex tumors. Source of

Funding: N/A