Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses

Background

The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL.

Objective

We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function.

Design setting and participants

We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software.

Outcome measurements and statistical analysis

Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1.

Results and limitations

Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58-89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR.

Conclusions

A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application.

Patient summary

Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

Predicting GFR after radical nephrectomy: the importance of split renal function

PURPOSE

To evaluate a conceptually simple model to predict new-baseline-glomerular-filtration-rate (NBGFR) after radical nephrectomy (RN) based on split-renal-function (SRF) and renal-functional-compensation (RFC), and to compare its predictive accuracy against a validated non-SRF-based model. RN should only be considered when the tumor has increased oncologic potential and/or when there is concern about perioperative morbidity with PN due to increased tumor complexity. In these circumstances, accurate prediction of NBGFR after RN can be important, with a threshold NBGFR > 45 ml/min/1.73m² correlating with improved overall survival.

METHODS

236 RCC patients who underwent RN (2010-2012) with preoperative imaging (CT/MRI) and relevant functional data were included. NBGFR was defined as GFR 3-12 months post-RN. SRF was determined using semi-automated software that provides differential parenchymal-volume-analysis (PVA) from preoperative imaging. Our SRF-based model was: Predicted NBGFR = 1.24 (× Global GFR_Pre-RN) (× SRF_{Contralateral}), with 1.24 representing the mean RFC estimate from independent analyses. A non-SRF-based model was also assessed: Predicted NBGFR = 17 + preoperative GFR (× 0.65)-age (× 0.25) + 3 (if tumor > 7 cm)-2 (if diabetes). Alignment between predicted/observed NBGFR was assessed by comparing correlation coefficients and area-under-the-curve (AUC) analyses.

RESULTS

The correlation-coefficients (r) were 0.87/0.72 for SRF-based/non-SRF-based models, respectively (p = 0.005). For prediction of NBGFR > 45 ml/min/1.73m², the SRF-based/non-SRF-based models provided AUC of 0.94/0.87, respectively (p = 0.044).

CONCLUSION

Previous non-SRF-based models to predict NBGFR post-RN are complex and omit two important parameters: SRF and RFC. Our proposed model prioritizes these parameters and provides a conceptually simple, accurate, and clinically implementable approach to predict NBGFR post-RN. SRF can be easily obtained using PVA software that is affordable, readily available (FUJIFILM-Medical-Systems), and more accurate than nuclear-renal-scans. The SRF-based model demonstrates greater predictive-accuracy than a non-SRF-based model, including the clinically-important predictive-threshold of NBGFR > 45 ml/min/1.73m².