BMC medical imaging · 2026
Value of multiparametric MRI as a potential marker to differentiate membranous nephropathy from IgA nephropathy.
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Shi Rongchao, Wang Hao, Li Min, Li KaiXin, Yang Huai, Zhu JinXia, Kuehn Bernd, Nickel Marcel Dominik, Yang Dawei, Guo Weikang, Xu Hui, Yang Zhenghan
Аннотация
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.
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.