Lewis Kieran, Siva Jayant, Bartholomew Angelica, Wong Anne, Munoz Lopez Carlos, Kazama Akira, Rathi Nityam, Maina Eran N, Campbell Rebecca A, Heller Nicholas, Scovell Jason M, Abouassaly Robert, Almassi Nima, Haywood Samuel C, Weight Christopher J, Campbell Steven C
Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio.
Division of Molecular Oncology, Department of Urology, Graduate School of Medical and Dental Sciences, Niigata University, Japan.
Urol Pract. 2025 May 5:101097UPJ0000000000000826. doi: 10.1097/UPJ.0000000000000826.
Accurate prediction of new baseline glomerular filtration rate (NBGFR) after radical nephroureterectomy (RNU) is important for managing upper tract urothelial carcinoma (UTUC) because it can inform timing of systemic chemotherapy. Current prediction models do not account for split renal function (SRF) and show modest accuracy. This study evaluates the accuracy of an SRF-based model, PVA, which incorporates both parenchymal volume analysis (PVA) and degree of parenchymal enhancement.
We reviewed patients with UTUC (n = 712) managed with RNU (2013-2023) and included patients with (1) contrast-enhanced CT < 1 year preoperatively, (2) glomerular filtration rate (GFR) < 3 months preoperatively, and (3) NBGFR 1 to 12 months postoperatively. Predicted NBGFR was 1.25 × (GFR) × (SRF), with 1.25 representing the average renal functional compensation after nephrectomy. For PVA, differential parenchymal volumes and degree of enhancement were estimated using semiautomated software. SRF-based models (based on PVA, PVA alone, or nuclear renal scans) and a non-SRF-based algorithm were compared using a 20% accuracy threshold.
Among patients analyzed (n = 352), the median preoperative GFR was 63 mL/min/1.73 m, 101 (29%) had moderate/severe hydronephrosis, and 42 (12%) had infiltrative renal masses (IRMs). For prediction of NBGFR after RNU, PVA demonstrated superior accuracy (84%) compared with PVA alone (accuracy = 79%, < .05), nuclear renal scans-based approach (accuracy = 73%, < .01), and non-SRF-based algorithm (accuracy = 65%, < .01). Among patients with hydronephrosis, notable improvements were observed for PVA compared with PVA alone (accuracies 88%/61%, respectively, ≤ .01). For IRMs, PVA seemed to be equivalent to other approaches for predicting NBGFR after RNU.
PVA incorporates both differential renal function (degree of enhancement) and parenchymal volumes and outperforms other SRF-based and non-SRF-based approaches for predicting NBGFR after RNU. These findings alleviate concerns that the prevalence of hydronephrosis and IRMs in this population reduces accuracy of SRF-based approaches. PVA can inform counseling about the timing of systemic chemotherapy in patients with high-risk UTUC.
根治性肾输尿管切除术（RNU）后新的基线肾小球滤过率（NBGFR）的准确预测对于上尿路尿路上皮癌（UTUC）的管理很重要，因为它可以为全身化疗的时机提供依据。目前的预测模型没有考虑分肾功能（SRF），准确性一般。本研究评估了基于SRF的模型PVA的准确性，该模型结合了实质体积分析（PVA）和实质强化程度。
我们回顾了2013年至2023年接受RNU治疗的UTUC患者（n = 712），纳入了符合以下条件的患者：（1）术前1年内的对比增强CT；（2）术前3个月内的肾小球滤过率（GFR）；（3）术后1至12个月的NBGFR。预测的NBGFR为1.25×（GFR）×（SRF），其中1.25代表肾切除术后的平均肾功能代偿。对于PVA，使用半自动软件估计不同的实质体积和强化程度。使用20%的准确性阈值比较基于SRF的模型（基于PVA、单独的PVA或核肾扫描）和基于非SRF的算法。
在分析的患者（n = 352）中，术前GFR的中位数为63 mL/min/1.73 m²，101例（29%）有中度/重度肾积水，42例（12%）有浸润性肾肿块（IRM）。对于RNU后NBGFR的预测，PVA显示出更高的准确性（84%），高于单独的PVA（准确性 = 79%，P <.05）、基于核肾扫描的方法（准确性 = 73%，P <.01）和基于非SRF的算法（准确性 = 65%，P <.01）。在肾积水患者中，与单独的PVA相比，PVA有显著改善（准确性分别为88%/61%，P ≤.01）。对于IRM，PVA在预测RNU后NBGFR方面似乎与其他方法相当。
PVA结合了不同的肾功能（强化程度）和实质体积，在预测RNU后NBGFR方面优于其他基于SRF和非SRF的方法。这些发现减轻了对该人群中肾积水和IRM的患病率会降低基于SRF方法准确性的担忧。PVA可为高危UTUC患者全身化疗时机的咨询提供依据。
