Presurgical MRI to predict risk of chronic kidney disease: AuntMinnie Article Summary

Purpose: Patients with solid renal masses (SRMs) are at risk of chronic kidney disease (CKD) after surgical resection with- out a reliable pre-operative predictor. This study investigates whether pre-operative multiparametric MRI (mpMRI) can predict CKD development and progression to stage 3 CKD.

Methods: 43 patients at the Icahn School of Medicine scheduled to undergo partial or radical nephrectomy for a localized solid renal mass agreed to a presurgical MRI including T1, R2*, ASL, and 9-bvalue DWI. We also obtained histopathological tumor characterization, and measures of kidney function at baseline and at 12months after surgery with serum creatinine estimated glomerular filtration rate (eGFR). We also calculated a clinical CKD risk score from eGFR, age, diabetes status, and surgery technique following the simplified calculation proposed by Ellis et al. (2020): +1 point for age>65years, +1 point for history of diabetes, +3 points for radical nephrectomy, and +3 for eGFR < 90 mL/min/1.73 m2, with an added +1 if eGFR < 80, and another added +1 if eGFR < 70. It’s proposed that a higher clinical CKD risk score = higher risk of CKD after surgery.

Results: Thirty of 43 (67%) participants had normal baseline renal function (eGFR>60mL/min/1.73m2). Of the 29 participants who completed 12-month follow-up, 66% (19/29) had baseline normal eGFR with 37% (7/19) developed stage 3 CKD. 30% had baseline stage 3 CKD and 48% had an eGFR decline > 5 mL/min/1.73m2.

eGFR from DCE-MRI and tubule diffusion correlated with baseline eGFR (r2 = 0.43 and 0.33 respectively). Reduced vascular diffusion from multi-b-value DWI MRI predicted eGFR decline (AUC=0.75–0.83, DOR=6.8–16.5), and lower vascular diffusion also correlated with a greater amount of decline after surgery. The clinical risk score was not predictive of renal decline. A higher CKD clinical risk score was associated with CKD development and showed acceptable discriminative performance (AUC = 0.81) with high specificity but low sensitivity (specificity = 0.92, sensitivity = 0.33). A larger contralateral ADC corticomedullary difference (AUC = 0.89; DOR = 22.5) had the highest predictive ability with high sensitivity (0.83) and specificity (0.82).

Conclusions: Pre-operative mpMRI may provide complementary information to clinical measures for predicting CKD progression and functional deterioration post-nephrectomy and allows assessment of individual kidney function in patients undergoing surgical management of renal masses. While pre-surgical MRI is commonly acquired to characterize renal masses, the coinciding information of the renal parenchyma can provide biologically specific biomarkers of renal pathology and physiology. As kidney-protective therapies emerge, and management options include active surveillance and focal ablation, identifying high-risk patients before surgery is critical to guide decision-making, personalize treatment, and prevent end-stage kidney disease.

Integrating imaging biomarkers with clinical and laboratory data to capture overall patient health and organ-specific health could yield a robust, precise and sensitive personalized predictor of CKD risk.

Figure from Multiparametric MRI for Predicting Renal Function Deterioration and Chronic Kidney Disease Development in Patients Undergoing Nephrectomy for Renal Masses: A Pilot Study, Journal of Magnetic Resonance Imaging (2026)

List of some publications relevant to this topic:

M. Liu, O. Bane, X. Mu. et al. Multiparametric MRI for Predicting Renal Function Deterioration and Chronic Kidney Disease Development in Patients Undergoing Nephrectomy for Renal Masses: A Pilot Study, 2025. DOI: 10.1002/jmri.70213

R. J. Ellis, S. J. Del Vecchio, K. M. J. Gallagher, et al., “A Simple Clin- ical Tool for Stratifying Risk of Clinically Significant CKD After Ne- phrectomy: Development and Multinational Validation,” Journal of the American Society of Nephrology (2020). DOI: 10.1681/asn.2019121328.

R. J. Ellis, “Chronic Kidney Disease After Nephrectomy: A Clinically- Significant Entity?,” Translational Andrology and Urology (2019). DOI: 10.21037/tau.2018.10.13.

S.-H. S. Huang, A. P. Sharma, A. Yasin, R. M. Lindsay, W. F. Clark, and G. Filler, “Hyperfiltration Affects Accuracy of Creatinine eGFR Measurement,” Clinical Journal of the American Society of Nephrology (2011). DOI: 10.2215/cjn.02760310.

D. Chae, N. Y. Kim, K. J. Kim, K. Park, C. Oh, and S. Y. Kim, “Pre- dictive Models for Chronic Kidney Disease After Radical or Partial Nephrectomy in Renal Cell Cancer Using Early Postoperative Serum Creatinine Levels,” Journal of Translational Medicine (2021). DOI: 10.1186/s12967-021-02976-2.

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M. Liu, O. Bane, X. Mu. et al. Immuno-Oncologic Profiling of Renal Masses using Multiparametric MRI: A Pilot Study. Journal of ImmunoTherapy of Cancer, 2025; DOI: 10.1136/jitc-2025-012833

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M. Liu, O. Bane, H. Al-Mubarak, A. Reddy, P. Kennedy, P. Robson, J. Cuevas, K. Meilika, A. Horowitz, B. Kuhn, K. Badani, B. Taouli, S. Lewis. Assessment & Prediction of Renal Function with Non-Contrast MRI in Patients Undergoing Surgical Management of Solid Renal Masses.” International Society for Magnetic Resonance in Medicine Workshop on IVIM 2024. (Oral Presentation)