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.

Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction

期刊论文
发表日期：

2025年10月13日

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Xiaodong Liu,
Lihua Chen,
Le Yang,
Jinxia Zhu,
Wen Shen

摘要

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 (D_slow), fast diffusion coefficient (D_fast), 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, D_slow, D_fast, 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 D_slow 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.