Abstract
OBJECTIVE: To improve the predictability of outcomes in robotic-assisted partial nephrectomy, we utilized three-dimensional virtual imaging for SPARE nephrometry scoring. We compared this method with a conventional two-dimensional scoring system to determine whether 3D virtual images offer enhanced predictive accuracy for Tetrafecta outcomes. METHODS: We retrospectively collected basic information, demographic data, and perioperative indices from patients who underwent robot-assisted partial nephrectomy for renal masses at the Department of Urology, First Affiliated Hospital of Xi'an Jiaotong University. A three-dimensional visualization system (IPS system, Yorktal) was employed to reconstruct the patients' imaging data using AI-based automatic segmentation, resulting in a three-dimensional visualization model (3DVM). This model was then imported into the virtual surgical planning software (Touch Viewer System, Yorktal) for automatic measurement of the SPARE score. Tetrafecta was defined as a warm ischemic time (WIT) of less than 25 min, negative surgical margins, absence of major perioperative complications, and no decline in postoperative renal function. The receiver operating characteristic (ROC) curve was utilized to evaluate the sensitivity and specificity of the SPARE score. RESULTS: A total of 141 patients were included in this study, with a mean age of 55.6 ± 11.14 years and a mean tumor size of 3.5 ± 1.2 cm. All variables, except for estimated blood loss (EBL) (Coefficient = 0.056, 0.035; P = 0.514, 0.685), showed significant correlation with the SPARE score when comparing CT and 3D virtual models (Tetrafecta: Coefficient = 0.408, 0.56; P < 0.001, < 0.001; WIT: Coefficient = 0.340, 0.237; P < 0.001, 0.007; ΔeGFR: Coefficient = 0.212, 0.257; P = 0.012, 0.002). The area under the curve (AUC) values from the ROC curves indicated that the 3D virtual model group had significantly better performance than the 2D image group for the SPARE score. However, there was no significant difference in the ROC curves for the SPARE complexity category between the two imaging modalities (AUC for SPARE category with 3DVM = 0.658 vs. AUC for SPARE category with CT = 0.643, P = 0.59; AUC for SPARE score with 3DVM = 0.854 vs. AUC for SPARE score with CT = 0.755, P < 0.001). CONCLUSIONS: The SPARE score combined with 3DVM has a more accurate predictive ability for Tetrafecta of RAPN compared to the traditional 2D SPARE score.