Quantitative imaging in medicine and surgery · 2025
Evaluation of multiparametric MRI and clinical indicators for renal fibrosis in chronic kidney disease.
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Wei Chaogang, Liu Rong, Li Xiaojing, Zhou Chunshan, Ma Qing, Xu Yilin, Zhu Ye, Shen Junkang, Zeng Ying, Song Kai, Jiang Zhen
Аннотация
BACKGROUND: Chronic kidney disease (CKD) is a major global health challenge, while renal fibrosis (RF) is the key pathological process and represents irreversible kidney damage. There is an urgent need for non-invasive techniques for assessment of RF. This study aimed to assess the diagnostic value of integrating native T1 mapping, readout segmentation of long variable echo-trains-diffusion-weighted imaging (RESOLVE-DWI), and T2* mapping imaging with clinical indicators in detecting RF caused by CKD.
METHODS: A prospective analysis was conducted on 117 patients with a clinical diagnosis of CKD who were scheduled for renal biopsy and underwent multiparametric magnetic resonance imaging (MRI) (native T1 mapping, RESOLVE-DWI, and T2* mapping) examinations from September 2021 to December 2023. Patients were divided into RF 1 (no fibrosis; n=23), RF 2 (mild RF, ≤25% fibrosis; n=54), and RF 3 (moderate to severe RF, >25% fibrosis; n=40). Univariate and multivariate logistic regression analyses were used to identify independent predictors for the presence of RF (RF 1 vs. RF 2 + RF 3) and the severity of RF (RF 2 vs. RF 3). Then, combined models were constructed. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of the models. Areas under the curves (AUCs) were compared using DeLong's test.
RESULTS: The independent predictors for the presence of RF were the estimated glomerular filtration rate (eGFR), mean corticomedullary T1 ratio (T1%), and mean corticomedullary apparent diffusion coefficient (ADC) ratio (ADC%). The independent predictors for the severity of RF were the eGFR and mean corticomedullary T1 difference (ΔT1). The AUC of the combined model-1 (eGFR + T1% + ADC%) was 0.919, which was significantly greater than that of the eGFR (AUC =0.828, P=0.008) and ADC% (AUC =0.801, P=0.009), but not significantly different from that of T1% (AUC =0.879, P=0.087). The diagnostic sensitivity of the combined model-1 for identifying RF increased to 90.4% and the specificity was 87.0%. The AUC of the combined model-2 (eGFR + ΔT1) was 0.887, which was significantly greater compared with the eGFR (AUC =0.808, P=0.019) and ΔT1 (AUC =0.834, P=0.032) models. When the eGFR was combined with ΔT1, the sensitivity of the combined model-2 to discriminate mild RF from moderate to severe RF increased to 92.5%, with a specificity of 77.8%.
CONCLUSIONS: Native T1 mapping and RESOLVE-DWI in combination with the eGFR can improve the diagnostic sensitivity of CKD-related RF, thus contributing to the early detection of RF and clinical decision-making.
METHODS: A prospective analysis was conducted on 117 patients with a clinical diagnosis of CKD who were scheduled for renal biopsy and underwent multiparametric magnetic resonance imaging (MRI) (native T1 mapping, RESOLVE-DWI, and T2* mapping) examinations from September 2021 to December 2023. Patients were divided into RF 1 (no fibrosis; n=23), RF 2 (mild RF, ≤25% fibrosis; n=54), and RF 3 (moderate to severe RF, >25% fibrosis; n=40). Univariate and multivariate logistic regression analyses were used to identify independent predictors for the presence of RF (RF 1 vs. RF 2 + RF 3) and the severity of RF (RF 2 vs. RF 3). Then, combined models were constructed. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of the models. Areas under the curves (AUCs) were compared using DeLong's test.
RESULTS: The independent predictors for the presence of RF were the estimated glomerular filtration rate (eGFR), mean corticomedullary T1 ratio (T1%), and mean corticomedullary apparent diffusion coefficient (ADC) ratio (ADC%). The independent predictors for the severity of RF were the eGFR and mean corticomedullary T1 difference (ΔT1). The AUC of the combined model-1 (eGFR + T1% + ADC%) was 0.919, which was significantly greater than that of the eGFR (AUC =0.828, P=0.008) and ADC% (AUC =0.801, P=0.009), but not significantly different from that of T1% (AUC =0.879, P=0.087). The diagnostic sensitivity of the combined model-1 for identifying RF increased to 90.4% and the specificity was 87.0%. The AUC of the combined model-2 (eGFR + ΔT1) was 0.887, which was significantly greater compared with the eGFR (AUC =0.808, P=0.019) and ΔT1 (AUC =0.834, P=0.032) models. When the eGFR was combined with ΔT1, the sensitivity of the combined model-2 to discriminate mild RF from moderate to severe RF increased to 92.5%, with a specificity of 77.8%.
CONCLUSIONS: Native T1 mapping and RESOLVE-DWI in combination with the eGFR can improve the diagnostic sensitivity of CKD-related RF, thus contributing to the early detection of RF and clinical decision-making.