Evaluation of Renal Fibrosis by Mapping Histology and Magnetic Resonance Imaging

• Main Authors: Jiong Zhang, Yuanmeng Yu, Xiaoshuang Liu, Xiong Tang, Feng Xu, Mingchao Zhang, Guotong Xie, Longjiang Zhang, Xiang Li, Zhi-Hong Liu
• Format: Article
• Language: English
• Published: Karger Publishers 2021-02-01
• Series: Kidney Diseases
• Subjects: chronic kidney disease; peritubular capillaries; extracellular matrix; diffusion; weighted imaging; magnetic resonance elastography
• Online Access: https://www.karger.com/Article/FullText/513332

Background: Renal fibrosis is a key driver of progression in chronic kidney disease (CKD). Recent advances in diagnostic imaging techniques have shown promising results for the noninvasive assessment of renal fibrosis. However, the specificity and accuracy of these techniques are controversial because they indirectly assess renal fibrosis. This limits fibrosis assessment by imaging in CKD for clinical practice. To validate magnetic resonance imaging (MRI) assessment for fibrosis, we derived representative models by mapping histology-proven renal fibrosis and imaging in CKD. Methods: Ninety-seven adult Chinese CKD participants with histology were studied. The kidney cortex interstitial extracellular matrix volume was calculated by the Aperio ScanScope system using Masson’s trichrome slices. The kidney cortex microcirculation was quantitatively assessed by peritubular capillary density using CD34 staining. The imaging techniques included intravoxel incoherent motion diffusion-weighted imaging and magnetic resonance elastography (MRE) imaging. Relevant analyses were performed to evaluate the correlations between MRI parameters and histology variables. Multiple linear regression models were used to describe the relationships between a response variable and other variables. The best-fit lines, which minimize the sum of squared residuals of the multiple linear regression models, were generated. Results: MRE values were negatively associated with the interstitial extracellular matrix volume (Rho = −0.397, p < 0.001). The best mapping model of extracellular matrix volume with the MRE value and estimated glomerular filtration rate (eGFR) we obtained was as follows: Interstitial extracellular matrix volume = 218.504 – 14.651 × In(MRE) – 18.499 × In(eGFR). DWI-fraction values were positively associated with peritubular capillary density (Rho = 0.472, p < 0.001). The best mapping model of peritubular capillary density with DWI-fraction value and eGFR was as follows: Peritubular capillaries density = 17.914 + 9.403 × (DWI – fraction) + 0.112 × (eGFR). Conclusions: The study provides histological evidence to support that MRI can effectively evaluate fibrosis in the kidney. These findings picture the graphs of the mapping model from imaging and eGFR into fibrosis, which has significant value for clinical implementation.