Abstract
BACKGROUND: Collecting system entry in robot-assisted partial nephrectomy may occur even in cases showing a low N factor in the R.E.N.A.L nephrometry score. Therefore, in this study, we focused on the tumor contact surface area with the adjacent renal parenchyma and attempted to construct a novel predictive model for collecting system entry. METHODS: Among 190 patients who underwent robot-assisted partial nephrectomy at our institution from 2015 to 2021, 94 patients with a low N factor (1-2) were analyzed. Contact surface was measured with three-dimensional imaging software and defined as the C factor, classified as C1, < 10 cm [2]; C2, ≥ 10 and < 15 cm [2]; and C3: ≥ 15 cm [2]. Additionally, a modified R factor (mR) was classified as mR1, < 20 mm; mR2, ≥ 20 and < 40 mm; and mR3, ≥ 40 mm. We discussed the factors influencing collecting system entry, including the C factor, and created a novel collecting system entry predictive model. RESULTS: Collecting system entry was observed in 32 patients with a low N factor (34%). The C factor was the only independent predictive factor for collecting system entry in multivariate regression analysis (odds ratio: 4.195, 95% CI: 2.160-8.146, p < 0.0001). Models including the C factor showed better discriminative power than the models without the C factor. CONCLUSIONS: The new predictive model, including the C factor in N1-2 cases, may be beneficial, considering its indication for preoperative ureteral catheter placement in patients undergoing robot-assisted partial nephrectomy.