Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction.
Objective: To develop and rigorously validate radiomics-based predictive models using postoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) MRI for the early, noninvasive assessment of impaired renal allograft function (IRF) in kidney transplant recipients. Methods: This r...
| Publicado en: | Abdominal Radiology Vol. 51; no. 5; pp. 2539 - 2555 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194809185&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194809185 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2026 vid: 51 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194809185 188636720 10.1007/s00261-025-05226-x 194809185 ppf: 2539 ppct: 16 formats: tig: atl: Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction. aug: au: Liu, Xiaodong Chen, Lihua Yang, Le Zhu, Jinxia Shen, Wen affil: https://ror.org/02mh8wx89 First Central Clinical College, Tianjin Medical University, Tianjin, China sug: ab: Objective: To develop and rigorously validate radiomics-based predictive models using postoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) MRI for the early, noninvasive assessment of impaired renal allograft function (IRF) in kidney transplant recipients. Methods: This retrospective study included 97 kidney transplant recipients (mean age, 36.77 ± 10.71 years), categorized into normal or impaired renal function groups based on an estimated glomerular filtration rate (eGFR) cutoff of 60 ml/min/1.73 m2. Patients were randomly assigned to training (n = 68) or validation (n = 29) groups. Postoperative IVIM-DWI MRI with 11 b-values was performed on a 3T scanner, generating parametric maps (apparent diffusion coefficient (ADC), slow diffusion coefficient (Dslow), fast diffusion coefficient (Dfast), perfusion fraction (PF)). Whole-graft 3D manual segmentation was used to extract 1604 radiomic features per dataset. Feature selection was performed through analysis of variance (ANOVA), Relief, and recursive feature elimination (RFE), followed by classification using ten machine learning algorithms, including auto-encoder (AE) and naïve Bayes (NB). Performance was evaluated using receiver operating characteristic (ROC) analysis, with area under the curve (AUC), accuracy, sensitivity, and specificity as metrics. Results: Radiomics models based on IVIM-derived parametric maps (ADC, Dslow, Dfast, PF) achieved superior diagnostic performance, with a validation AUC of 0.790 (95% confidence interval (CI) 0.607–0.937) using ANOVA-based feature selection and AE classification, and a training AUC of 0.770. Integrative models combining multi-b-value DWI and IVIM maps further enhanced predictive power, achieving a validation AUC of 0.790 (95% CI 0.600–0.951) and a training AUC of 0.816, utilizing 16 features selected via ANOVA and classified with the NB algorithm. AE and NB classifiers consistently exhibited the strongest discriminative performance across all model configurations. Notably, the median histogram intensity from the Dslow map was the most influential feature for predicting impaired renal function. Conclusions: This study is the first to comprehensively compare the predictive performance of radiomics models based on IVIM-DWI, including both single b-value DWI and IVIM parametric maps, for early assessment of renal allograft dysfunction. The integrative use of multi-b-value DWI and IVIM imaging markedly improves diagnostic accuracy, demonstrating a robust noninvasive framework for early detection of renal allograft dysfunction. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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