Huang HaoXiang, Chen Bohong, Feng Cong, Chen Wei, Wu Dapeng
Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
World J Urol. 2024 Dec 19;43(1):37. doi: 10.1007/s00345-024-05344-z.
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.
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.
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).
The SPARE score combined with 3DVM has a more accurate predictive ability for Tetrafecta of RAPN compared to the traditional 2D SPARE score.
为提高机器人辅助部分肾切除术预后的可预测性，我们将三维虚拟成像用于SPARE肾计量评分。我们将该方法与传统二维评分系统进行比较，以确定三维虚拟图像对四要素预后是否具有更高的预测准确性。
我们回顾性收集了西安交通大学第一附属医院泌尿外科因肾肿块接受机器人辅助部分肾切除术患者的基本信息、人口统计学数据和围手术期指标。采用三维可视化系统（IPS系统，Yorktal）通过基于人工智能的自动分割重建患者的影像数据，生成三维可视化模型（3DVM）。然后将该模型导入虚拟手术规划软件（Touch Viewer系统，Yorktal）以自动测量SPARE评分。四要素定义为热缺血时间（WIT）小于25分钟、手术切缘阴性、无重大围手术期并发症且术后肾功能无下降。采用受试者操作特征（ROC）曲线评估SPARE评分的敏感性和特异性。
本研究共纳入141例患者，平均年龄55.6±11.14岁，平均肿瘤大小3.5±1.2厘米。比较CT和三维虚拟模型时，除估计失血量（EBL）外（系数=0.056，0.035；P=0.514，0.685），所有变量与SPARE评分均显著相关（四要素：系数=0.408，0.56；P<0.001，<0.001；WIT：系数=0.340，0.237；P<0.
