Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models

Objectives To explore the optimal choice of seven diffusion models (DWI, IVIM, DKI, CTRW, FROC, SEM, and sADC) to assess renal interstitial fibrosis (IF) and annual renal function loss in chronic kidney disease (CKD).Methods One hundred thirty-three CKD patients and 30 controls underwent multi-b dif...

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Main Authors: Guanjie Yuan, Zhouyan Liao, Ping Liang, Lingli Cai, Kailun Zhou, Ting Yin, Wei Chen, Omar Darwish, Chuou Xu, Min Han, Zhen Li
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Renal Failure
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Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2025.2480751
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author Guanjie Yuan
Zhouyan Liao
Ping Liang
Lingli Cai
Kailun Zhou
Ting Yin
Wei Chen
Omar Darwish
Chuou Xu
Min Han
Zhen Li
author_facet Guanjie Yuan
Zhouyan Liao
Ping Liang
Lingli Cai
Kailun Zhou
Ting Yin
Wei Chen
Omar Darwish
Chuou Xu
Min Han
Zhen Li
author_sort Guanjie Yuan
collection DOAJ
description Objectives To explore the optimal choice of seven diffusion models (DWI, IVIM, DKI, CTRW, FROC, SEM, and sADC) to assess renal interstitial fibrosis (IF) and annual renal function loss in chronic kidney disease (CKD).Methods One hundred thirty-three CKD patients and 30 controls underwent multi-b diffusion sequence scans. Patients were divided into the training, testing, and temporal external validation sets. Least absolute shrinkage and selection operator regression and logistic regression were used to select the optimal metrics for distinguishing the mild from moderate-to-severe IF. The performances of imaging, clinical, and combined models were compared. A linear mixed-effects model calculated estimated glomerular filtration rate (eGFR) slope, and multiple linear regression assessed the association between metrics and 1–3-year eGFR slopes.Results The training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical fIVIM, MKDKI and eGFR was superior to the clinical model combining the eGFR and 24-hour urinary protein in all sets (net reclassification index [NRI] > 0, p < 0.05). Decision curve analysis showed the combined model provided greater net clinical benefit across most thresholds. Fifty-two, 35, and 16 patients completed 1-, 2-, and 3-year follow-ups. After adjusting for covariates, cortical fIVIM correlated with the 1-year eGFR slope (β = 30.600, p = 0.001), and cortical αSEM correlated with the 2- and 3-year eGFR slopes (β = 44.859, p = 0.002; β = 95.631, p = 0.019).Conclusions A combined model of cortical fIVIM, MKDKI and eGFR provides a useful comprehensive tool for grading IF, with cortical fIVIM and αSEM as potential biomarkers for CKD progression.
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series Renal Failure
spelling doaj-art-5bca71c4b3e3439d94189e2d190e14222025-08-20T02:40:52ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2025.2480751Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion modelsGuanjie Yuan0Zhouyan Liao1Ping Liang2Lingli Cai3Kailun Zhou4Ting Yin5Wei Chen6Omar Darwish7Chuou Xu8Min Han9Zhen Li10Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, ChinaMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, ChinaMR Application Predevelopment, Siemens Healthcare, Erlangen, GermanyDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaObjectives To explore the optimal choice of seven diffusion models (DWI, IVIM, DKI, CTRW, FROC, SEM, and sADC) to assess renal interstitial fibrosis (IF) and annual renal function loss in chronic kidney disease (CKD).Methods One hundred thirty-three CKD patients and 30 controls underwent multi-b diffusion sequence scans. Patients were divided into the training, testing, and temporal external validation sets. Least absolute shrinkage and selection operator regression and logistic regression were used to select the optimal metrics for distinguishing the mild from moderate-to-severe IF. The performances of imaging, clinical, and combined models were compared. A linear mixed-effects model calculated estimated glomerular filtration rate (eGFR) slope, and multiple linear regression assessed the association between metrics and 1–3-year eGFR slopes.Results The training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical fIVIM, MKDKI and eGFR was superior to the clinical model combining the eGFR and 24-hour urinary protein in all sets (net reclassification index [NRI] > 0, p < 0.05). Decision curve analysis showed the combined model provided greater net clinical benefit across most thresholds. Fifty-two, 35, and 16 patients completed 1-, 2-, and 3-year follow-ups. After adjusting for covariates, cortical fIVIM correlated with the 1-year eGFR slope (β = 30.600, p = 0.001), and cortical αSEM correlated with the 2- and 3-year eGFR slopes (β = 44.859, p = 0.002; β = 95.631, p = 0.019).Conclusions A combined model of cortical fIVIM, MKDKI and eGFR provides a useful comprehensive tool for grading IF, with cortical fIVIM and αSEM as potential biomarkers for CKD progression.https://www.tandfonline.com/doi/10.1080/0886022X.2025.2480751Diffusion weighted imagingchronic kidney diseaseinterstitial fibrosisprogression
spellingShingle Guanjie Yuan
Zhouyan Liao
Ping Liang
Lingli Cai
Kailun Zhou
Ting Yin
Wei Chen
Omar Darwish
Chuou Xu
Min Han
Zhen Li
Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
Renal Failure
Diffusion weighted imaging
chronic kidney disease
interstitial fibrosis
progression
title Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
title_full Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
title_fullStr Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
title_full_unstemmed Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
title_short Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models
title_sort noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease the optimal solution of seven mr diffusion models
topic Diffusion weighted imaging
chronic kidney disease
interstitial fibrosis
progression
url https://www.tandfonline.com/doi/10.1080/0886022X.2025.2480751
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