Accurate prediction of NB-GFR for patients undergoing nephrectomy is essential, offering prognostic benefits for preoperative counseling [6], potentially reducing morbidity, especially cardiovascular events, and expanding eligibility for adjuvant treatments [7, 8, 15]. This study highlights differences in RPV between RCC patients and living kidney donors, with the latter having fewer underlying morbidities. We observed varying RPV compensation patterns and their relationships with NB-GFR across different nephrectomy types, underscoring the value of preoperative RPV measurement from routine CT scans for individualized counseling, especially in patient undergoing PN, where NRS predictability may be limited.

Previous studies have shown that RPV varies based on gender and body habitus [16, 17]. In this study, patients with small renal masses eligible for PN demonstrated the highest preoperative PV, whereas those scheduled for DN had the lowest preoperative PV. These findings suggest that preoperative PV compensation might be influenced by the presence of renal tumors. However, the interpretation of these variations is limited by factors such as the female predominance, younger age, fewer comorbidities in the DN group, and higher BMI in the PN group. Additionally, the specific effects of RCC and its histologic subtypes on preoperative RPV remain unclear, potentially limiting volumetric analysis conclusions.

For surgeons managing complex renal masses, particularly in patients with limited preoperative GFR or hereditary conditions that predispose them to multiple tumors, accurately predicting NB-GFR or CKD risk is crucial. Studies indicate that patients undergoing nephrectomy typically experience a 20–30% reduction in renal function [18,19,20], while those undergoing nephron-sparing surgery maintain 90–95% of their renal function postoperatively [12, 19]. Previous reports have shown that split PV-based models, incorporating remaining PV, preoperative GFR, and a 25% compensation factor, can predict NB-GFR effectively [18]. In this study, VA-GFR, derived from preoperative RPV, showed the strongest correlation with NB-GFR in the PN group and a high correlation in the DN group. Compared to models relying solely on preoperative characteristics, PV analysis offers improved performance in predicting NB-GFR, especially in PN cases. Expected resected parenchyma helps guide the surgical approach, minimizing parenchymal loss and preserving NB-GFR. However, data on PV analysis accuracy for obstructed kidneys or those with renal vein or inferior vena cava thrombus are limited. Previous studies have shown that conditions like hydronephrosis, pyelonephritis, and renal vein thrombosis alter PV analysis [21]. Additionally, the impact of various treatment modalities, including tumor ablation, laparoscopic or robotic-assisted approaches, and renal vascular clamping duration (including parenchymal cooling), on RPV and NB-GFR predictive accuracy requires further study [2, 22, 23].

This study focused on changes in NB-GFR and RPV within the first year post-nephrectomy, as long-term GFR and RPV could be influenced more by patient characteristics or progression of underlying conditions than by nephrectomy itself. In contrast, previous studies have suggested that GFR recovery may continue for up to 25 months post-nephrectomy, with compensatory hypertrophy frequently observed [24]. The hyperfiltration theory has been proposed to explain this phenomenon [25]. The degree of compensation varies and depends largely on preoperative parenchymal and glomerular health. Greater compensatory hypertrophy is associated with less preoperative fibrosis and glomerular injury, underscoring the importance of considering underlying conditions and patient characteristics when predicting postoperative RPV and GFR changes. In our study, kidneys that underwent PN did not show hypertrophic PV compensation post-surgery, while the contralateral kidney exhibited a slight PV increase. Interestingly, in RN cases where preoperative hypertrophic compensation was suspected, the preserved kidney still showed the greatest PV increase postoperatively. Moreover, GFR per cubic centimeter of RPV was significantly higher in RN cases compared to PN cases, highlighting the need for preoperative evaluation of underlying conditions that could impact compensatory capacity.

Consistent with findings from larger studies [12, 14], this study found that baseline characteristics, including advancing age, male gender, hypertension, proteinuria, preoperative eGFR, and remaining PV percentage, significantly impacted NB-GFR. By incorporating these factors with VA-GFR, we propose a novel NB-GFR prediction model based on CT volumetry that may generalize to all nephrectomy types. The VA-GFR model demonstrated substantial interrater reliability and promising sensitivity, specificity, and accuracy compared to the RCC and DN models [12, 15]. Given the critical GFR cutoff of 45 mL/min/1.73 m², VA-GFR models accurately predicted de novo CKD with 97.5% accuracy following nephrectomy. A few patients, however, presented with unexpectedly low NB-GFR (< 45 mL/min/1.73 m²). Despite reviewing patient histories and operative findings, no clear cause was identified for these declines, highlighting the limitations of existing tools in accurately predicting significant renal function loss. Combining the VA-GFR model with other predictive tools, such as the RENSAFE AKI and CKD nomogram [26] for RN cases or Cystatin C [27] in DN cases, may help reduce the risk of unexpected low NB-GFR occurrences.

This study has several limitations. First, the retrospective design may introduce selection bias in treatment choices and surgical approaches, though volumetric analysis was derived from the least parenchymal resection possible. Second, manual volumetric analysis is time-intensive compared to earlier methods, such as contact surface area or spherical excised parenchymal prediction methods [28, 29]. Automated or semi-automated RPV calculations have shown accuracy [30,31,32], but their validation in PN cases is needed. Third, the limited follow-up period restricts assessment, as NB-GFR is a single point-in-time outcome that does not ensure long-term renal function preservation, which may be influenced by multiple factors. Nevertheless, recent study has shown a correlation between CT-predicted and actual NB-GFR over time [24]. Fourth, cases with renal anomalies, ureteral obstruction, or previous diversion were excluded. Fifth, the study population was limited to Thai patients. Recent studies show varying post-nephrectomy CKD incidences across races [33], highlighting the need for external population validation to assess the model’s generalizability. Finally, contrast-enhanced CT scans can impact renal function, particularly in CKD patients, underscoring the need for future studies on non-contrast and non-irradiating imaging techniques for RPV assessment.

Despite these limitations, our study provides a foundation for more accurate predictions of NB-GFR in patients undergoing nephrectomy.