{
    "abstract": "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).\nOne 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.\nThe training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical f\nA combined model of cortical f",
    "authors": [
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Guanjie",
            "initials": "G",
            "lastname": "Yuan"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Zhouyan",
            "initials": "Z",
            "lastname": "Liao"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Ping",
            "initials": "P",
            "lastname": "Liang"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Lingli",
            "initials": "L",
            "lastname": "Cai"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Kailun",
            "initials": "K",
            "lastname": "Zhou"
        },
        {
            "affiliation": "MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.",
            "firstname": "Ting",
            "initials": "T",
            "lastname": "Yin"
        },
        {
            "affiliation": "MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.",
            "firstname": "Wei",
            "initials": "W",
            "lastname": "Chen"
        },
        {
            "affiliation": "MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany.",
            "firstname": "Omar",
            "initials": "O",
            "lastname": "Darwish"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Chuou",
            "initials": "C",
            "lastname": "Xu"
        },
        {
            "affiliation": "Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Min",
            "initials": "M",
            "lastname": "Han"
        },
        {
            "affiliation": "Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.",
            "firstname": "Zhen",
            "initials": "Z",
            "lastname": "Li"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.1080/0886022X.2025.2480751\n10.1016/j.kint.2023.10.016\n10.1016/j.mam.2023.101206\n10.1681/ASN.0000000000000066\n10.1002/mrm.22436\n10.1007/s00330-023-10198-x\n10.1002/nbm.4485\n10.1002/jmri.24985\n10.1148/radiol.2017170025\n10.1002/jmri.29302\n10.1007/s00330-023-09674-1\n10.1148/radiol.2020191498\n10.1038/s41591-023-02418-0\n10.7326/0003-4819-150-9-200905050-00006\n10.1007/s10157-023-02408-z\n10.1681/ASN.2019010008\n10.2215/CJN.07900716\n10.1186/s13244-024-01736-2\n10.21037/tp-22-558\n10.1016/j.neuroimage.2017.12.062\n10.1002/jmri.24234\n10.1093/bjr/tqae024\n10.1002/jmri.26657\n10.1002/mrm.22285\n10.3174/ajnr.A4836\n10.1002/nbm.4920\n10.1002/mrm.27101\n10.1016/j.kint.2023.10.018\n10.1007/s00330-022-09268-3\n10.1038/s41581-019-0248-y\n10.2215/CJN.10150915\n10.1016/j.heliyon.2021.e06072\n10.1002/jmri.28477\n10.1016/j.ejrad.2020.108865\n10.1038/ki.2008.350\n10.1159/000491551\n10.1016/j.kint.2017.10.020\n10.1093/ndt/gfab229\n10.1016/j.jneumeth.2020.108951",
    "journal": "Renal failure",
    "keywords": [
        "Diffusion weighted imaging",
        "chronic kidney disease",
        "interstitial fibrosis",
        "progression"
    ],
    "methods": null,
    "publication_date": "2025-03-26",
    "pubmed_id": "40133226\n38519239\n37541106\n36720675\n20665805\n37672055\n33543512\n26119267\n28604279\n38376448\n37148348\n32043948\n37330614\n19414839\n37806976\n31292199\n28298435\n38900336\n36643671\n29277647\n24259401\n38291900\n30659687\n20187164\n27256851\n36912198\n29493816\n38490803\n36454259\n32060481\n27026518\n33553749\n36219465\n32058895\n18633339\n30048964\n29325997\n34302484\n33017644",
    "results": "The training, testing, and temporal external validation sets had 75, 30, and 28 patients, respectively. The combined model incorporating cortical f",
    "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.",
    "xml": "<Element 'PubmedArticle' at 0x7885037f18a0>"
}