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Background <p>Membranous nephropathy (MN) and IgA nephropathy (IgAN) exhibit similar clinical symptoms but differ substantially in treatment strategies and prognoses, highlighting the need for a non-invasive and precise diagnostic method. Multiparametric MRI (mpMRI) has shown great potential for the non-invasive assessment and quantification of renal function. This study aims to explore the potential value of mpMRI in distinguishing between MN and IgAN.</p> Methods <p>Cortical renal blood flow (cRBF), cortical true diffusion coefficient (cD), cortical pseudo-diffusion coefficient (cD*), cortical perfusion fraction (cf), cortical R2* (cR2*), cortical T1 mapping (cT1), cortical mean diffusivity (cMD), and cortical mean kurtosis (cMK) were prospectively measured in 58 patients with MN, 47 patients with IgAN, and 75 healthy controls. One-way analysis of variance was used to compare MRI parameters among the three groups. The correlations between laboratory data and MRI parameters were evaluated using Spearman correlation analysis. Logistic regression analysis was performed to construct diagnostic models. The diagnostic performances of models for differentiating MN and IgAN were assessed using receiver operating characteristic curves with area under the curve (AUC).</p> Results <p>Compared with the IgAN group, patients with MN were older and had higher cRBF, cf, and cT1 values (all <i>P</i> &lt; 0.050), but cMK value was significantly lower than that in IgAN (<i>P</i> = 0.009). In the MN group, a significant correlation was observed between eGFR and cf. (<i>r</i> = 0.61, <i>P</i> &lt; 0.001), whereas in the IgAN group, eGFR showed a significant correlation with cRBF (<i>r</i> = 0.51, <i>P</i> &lt; 0.001). Age, eGFR, 24&#xa0;h urinary protein, and cMK demonstrated the ability to distinguish between MN and IgAN in both univariable and multivariate logistic regression analysis and were used to construct diagnostic models. Among the multiparametric models, the combination model incorporating age, eGFR, 24&#xa0;h urinary protein, and cMK exhibited the highest AUC of 0.92 (95% confidence interval [CI], 0.87–0.97), with a sensitivity of 0.91 (0.81–0.96) and a specificity of 0.81 (0.67–0.90).</p> Conclusion <p>An integrated model combining mpMRI and clinical variables provides a non-invasive method for differentiating MN from IgAN with high diagnostic accuracy.</p>

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Value of multiparametric MRI as a potential marker to differentiate membranous nephropathy from IgA nephropathy

  • 期刊论文

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  • 发表日期：

    2026年7月9日

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  • Rongchao Shi,
  • Hao Wang,
  • Min Li,
  • KaiXin Li,
  • Huai Yang,
  • JinXia Zhu,
  • Bernd Kuehn,
  • Marcel Dominik Nickel,
  • Dawei Yang,
  • Weikang Guo,
  • Hui Xu,
  • Zhenghan Yang

摘要

Background

Membranous nephropathy (MN) and IgA nephropathy (IgAN) exhibit similar clinical symptoms but differ substantially in treatment strategies and prognoses, highlighting the need for a non-invasive and precise diagnostic method. Multiparametric MRI (mpMRI) has shown great potential for the non-invasive assessment and quantification of renal function. This study aims to explore the potential value of mpMRI in distinguishing between MN and IgAN.

Methods

Cortical renal blood flow (cRBF), cortical true diffusion coefficient (cD), cortical pseudo-diffusion coefficient (cD*), cortical perfusion fraction (cf), cortical R2* (cR2*), cortical T1 mapping (cT1), cortical mean diffusivity (cMD), and cortical mean kurtosis (cMK) were prospectively measured in 58 patients with MN, 47 patients with IgAN, and 75 healthy controls. One-way analysis of variance was used to compare MRI parameters among the three groups. The correlations between laboratory data and MRI parameters were evaluated using Spearman correlation analysis. Logistic regression analysis was performed to construct diagnostic models. The diagnostic performances of models for differentiating MN and IgAN were assessed using receiver operating characteristic curves with area under the curve (AUC).

Results

Compared with the IgAN group, patients with MN were older and had higher cRBF, cf, and cT1 values (all P < 0.050), but cMK value was significantly lower than that in IgAN (P = 0.009). In the MN group, a significant correlation was observed between eGFR and cf. (r = 0.61, P < 0.001), whereas in the IgAN group, eGFR showed a significant correlation with cRBF (r = 0.51, P < 0.001). Age, eGFR, 24 h urinary protein, and cMK demonstrated the ability to distinguish between MN and IgAN in both univariable and multivariate logistic regression analysis and were used to construct diagnostic models. Among the multiparametric models, the combination model incorporating age, eGFR, 24 h urinary protein, and cMK exhibited the highest AUC of 0.92 (95% confidence interval [CI], 0.87–0.97), with a sensitivity of 0.91 (0.81–0.96) and a specificity of 0.81 (0.67–0.90).

Conclusion

An integrated model combining mpMRI and clinical variables provides a non-invasive method for differentiating MN from IgAN with high diagnostic accuracy.