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优化根治性肾切除术后新基线肾小球滤过率的预测:算法真的有必要吗?

Optimizing prediction of new-baseline glomerular filtration rate after radical nephrectomy: are algorithms really necessary?

作者信息

Rathi Nityam, Yasuda Yosuke, Attawettayanon Worapat, Palacios Diego A, Ye Yunlin, Li Jianbo, Weight Christopher, Eltemamy Mohammed, Benidir Tarik, Abouassaly Robert, Campbell Steven C

机构信息

Center for Urologic Oncology, Glickman Urological and Kidney Institute, Room Q10-120, 9500 Euclid Avenue, Cleveland Clinic, Cleveland, OH, USA.

Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.

出版信息

Int Urol Nephrol. 2022 Oct;54(10):2537-2545. doi: 10.1007/s11255-022-03298-y. Epub 2022 Jul 17.

Abstract

INTRODUCTION

Radical nephrectomy (RN) is an important consideration for the management of localized renal-cell-carcinoma (RCC) whenever the tumor appears aggressive, although reduced renal function is a concern. Split-renal-function (SRF) in the contralateral kidney and postoperative renal functional compensation (RFC) are fundamentally important for the accurate prediction of new baseline GFR (NBGFR) post-RN. SRF can be estimated either from nuclear renal scans (NRS) or from preoperative imaging using parenchymal-volume-analysis (PVA). We compare two SRF-based models for predicting NBGFR after RN with a subjective prediction of NBGFR by an experienced urologic-oncologist.

METHODS

187 RCC patients managed with RN (2006-16) were included based on the availability of preoperative CT/MRI and NRS, and preoperative/postoperative eGFR. NBGFR was defined as the final GFR 3-12 months post-RN. For the SRF-based approaches, SRF was derived from either NRS or PVA, and RFC was estimated at 25% based on previous independent analyses. Thus, the formula (Global GFR × SRF) × 1.25 was used to predict NBGFR after RN. For subjective-assessment, a blinded, independent urologic oncologist provided NBGFR predictions based on preoperative eGFR, CT/MRI, and clinical/tumor characteristics. Predictive accuracies were assessed by correlation coefficients (r).

RESULTS

The r values for subjective-assessment, NRS/SRF-based, and PVA/SRF-based approaches were 0.72/0.72/0.85, respectively (p < 0.05). The PVA/SRF-based model also demonstrated significant improvement across other performance parameters.

CONCLUSIONS

The PVA/SRF-based model more accurately predicts NBGFR post-RN than NRS/SRF-based and Subjective Estimation. PVA software (Fujifilm-medical-systems) is readily available and affordable and provides accurate SRF estimations from routine preoperative imaging. This novel approach may inform clinical management regarding RN/PN for complex RCC cases.

摘要

引言

根治性肾切除术(RN)是局限性肾细胞癌(RCC)治疗的重要考量方式,只要肿瘤表现出侵袭性,尽管肾功能降低是一个问题。对侧肾脏的分肾功能(SRF)和术后肾功能代偿(RFC)对于准确预测RN术后的新基线肾小球滤过率(NBGFR)至关重要。SRF可通过核素肾扫描(NRS)或使用实质体积分析(PVA)的术前影像来估计。我们将两种基于SRF的预测RN术后NBGFR的模型与一位经验丰富的泌尿肿瘤学家对NBGFR的主观预测进行比较。

方法

纳入187例2006 - 2016年接受RN治疗的RCC患者,基于术前CT/MRI、NRS以及术前/术后估算肾小球滤过率(eGFR)的可得性。NBGFR定义为RN术后3 - 12个月的最终肾小球滤过率。对于基于SRF的方法,SRF源自NRS或PVA,且根据先前的独立分析,RFC估计为25%。因此,公式(总体肾小球滤过率×SRF)×1.25用于预测RN术后的NBGFR。对于主观评估,一位不知情的独立泌尿肿瘤学家根据术前eGFR、CT/MRI以及临床/肿瘤特征提供NBGFR预测。通过相关系数(r)评估预测准确性。

结果

主观评估、基于NRS/SRF以及基于PVA/SRF方法的r值分别为0.72/0.72/0.85(p < 0.05)。基于PVA/SRF的模型在其他性能参数方面也显示出显著改善。

结论

基于PVA/SRF的模型比基于NRS/SRF的模型以及主观估计更准确地预测RN术后的NBGFR。PVA软件(富士胶片医疗系统)易于获取且价格合理,可从常规术前影像中提供准确的SRF估计。这种新方法可能为复杂RCC病例的RN/部分肾切除术(PN)临床管理提供参考。

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