Li Zonghan, Li Jiayi, Sun Ning, Zhang Qifeng, Zhang Weiping, Li Zhenwu, Song Hongcheng
Department of Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Quant Imaging Med Surg. 2024 Dec 5;14(12):9137-9145. doi: 10.21037/qims-24-1038. Epub 2024 Nov 29.
Several nephrometry scores have been proposed and applied in the adult population. We previously established a novel model to predict the feasibility of nephron-sparing surgery (NSS) in pediatric bilateral Wilms tumor (WT) patients. This study aimed to evaluate whether our model had better predictive performance compared to other scores.
Data from 58 patients during the period 2008-2019 were retrospectively reviewed. Nephrometry scores, including RENAL [radius (R), exophytic/endophytic (E), nearness to collecting system/sinus (N), anterior/posterior (A), and location relative to polar lines (L)], PADUA (preoperative aspects and dimensions used for an anatomical classification), and SPARE (Simplified PADUA REnal) scores, were calculated. A senior radiologist and a senior urologist independently reviewed computed tomography (CT) or magnetic resonance scans while blinded to clinical outcomes. A urology resident re-evaluated the imaging scans using our RSR [relation with collecting system (R), size (S), remaining renal parenchyma (R)] model. The areas under the curve (AUCs) were compared. Inter-rater reliability of the RSR model was assessed using kappa statistics and intraclass correlation coefficient (ICC).
The AUC was 0.982 for the RSR model, which outperformed the other three scores. Decision curve analysis (DCA) showed the same results. Compared to the RENAL score, the RSR model showed significant reclassification ability [0.573 (95% confidence interval: 0.161, 1.204), P=0.03]. Significant overall discrimination ability was found with RSR compared to the other three scores (P<0.001). The ICC value was 0.871 for S, and the kappa values of the two R indicators were 0.806 and 0.777, respectively. A web-based calculator was constructed.
The RSR model's predictive performance outperforms the established adult nephrometry scores while offering overall fair interobserver agreement. A web-based calculator could help to make personalized treatment decisions.
已有多种肾计量评分方法被提出并应用于成人患者。我们之前建立了一种新模型来预测小儿双侧肾母细胞瘤（WT）患者保留肾单位手术（NSS）的可行性。本研究旨在评估我们的模型与其他评分方法相比是否具有更好的预测性能。
回顾性分析了2008年至2019年期间58例患者的数据。计算了肾计量评分，包括RENAL评分[半径（R）、外生性/内生性（E）、与集合系统/肾窦的接近程度（N）、前后位（A）以及相对于极线的位置（L）]、PADUA评分（用于解剖学分类的术前特征和尺寸）和SPARE评分（简化的PADUA肾评分）。一名资深放射科医生和一名资深泌尿外科医生在对临床结果不知情的情况下独立回顾计算机断层扫描（CT）或磁共振扫描图像。一名泌尿外科住院医师使用我们的RSR模型[与集合系统的关系（R）、大小（S）、剩余肾实质（R）]重新评估影像扫描图像。比较曲线下面积（AUC）。使用kappa统计量和组内相关系数（ICC）评估RSR模型的评分者间信度。
RSR模型的AUC为0.982，优于其他三种评分方法。决策曲线分析（DCA）显示了相同的结果。与RENAL评分相比，RSR模型显示出显著的重新分类能力[0.573（95%置信区间：0.161，1.204），P = 0.03]。与其他三种评分方法相比，RSR显示出显著的总体鉴别能力（P < 0.001）。S的ICC值为0.871，两个R指标的kappa值分别为0.806和0.777。构建了一个基于网络的计算器。
RSR模型的预测性能优于已有的成人肾计量评分方法，同时观察者间总体一致性尚可。基于网络的计算器有助于做出个性化的治疗决策。
