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Background <p>Renal fibrosis (RF) is a key pathological hallmark and prognostic indicator of chronic kidney disease (CKD). Accurate evaluation of RF is critical for risk stratification and therapeutic decision-making, yet the current assessment relies mainly on renal biopsy, which has several limitations. This study aimed to develop a two-stage artificial intelligence framework that integrates multimodal MRI (native T<sub>1</sub> mapping, ADC, and T<sub>2</sub><sup>*</sup> mapping) and clinical indicators for the noninvasive assessment of RF in patients with CKD.</p> Methods <p>This prospective study included 152 patients with biopsy-proven CKD (RF 1: no RF, 34 patients; RF 2: mild RF, 69 patients; and RF 3: moderate to severe RF, 49 patients). The dataset was randomly partitioned into training and test cohorts at a 2:1 ratio. A two-stage model combining MobileNetV2-SE-based deep learning features with clinical indicators was developed for RF classification. Two binary tasks were performed: RF presence (RF 1 <i>vs</i>. RF 2 and RF 3) and severity (RF 2 <i>vs</i>. RF 3). Nested cross-validation was applied for model development and hyperparameter tuning, and bootstrapping was used to assess performance robustness. Model performance was evaluated with the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) visualization.</p> Results <p>Compared with single-modality models, the multimodal deep learning model (DL-combine), which is based exclusively on native T<sub>1</sub> mapping, ADC, and T<sub>2</sub><sup>*</sup> mapping, demonstrated favourable and stable performance (test AUC: 0.930). Among the 14 classifiers, XGBoost performed numerically better in terms of RF presence (mean AUCs: 0.986, 0.887; accuracy: 0.947, 0.829), whereas ExtraTree performed better in terms of RF severity assessment (mean AUCs: 0.935, 0.883; accuracy: 0.886, 0.848). Calibration curves and DCA confirmed robust predictive reliability and clinical utility. SHAP analysis highlighted the relative contributions of the DL-sign and eGFR.</p> Conclusion <p>This two-stage multimodal MRI-based framework provides accurate and interpretable assessment of RF across different stages of CKD, supporting noninvasive risk stratification and complementary clinical decision-making.</p>

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Multimodal MRI-based two-stage artificial intelligence framework for renal fibrosis classification in chronic kidney disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月4日

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  • Xiaojing Li,
  • Yirui Li,
  • Qing Ma,
  • Yilin Xu,
  • Ye Zhu,
  • Jing Zhang,
  • Junkang Shen,
  • Wu Cai,
  • Chaogang Wei,
  • Zhen Jiang

摘要

Background

Renal fibrosis (RF) is a key pathological hallmark and prognostic indicator of chronic kidney disease (CKD). Accurate evaluation of RF is critical for risk stratification and therapeutic decision-making, yet the current assessment relies mainly on renal biopsy, which has several limitations. This study aimed to develop a two-stage artificial intelligence framework that integrates multimodal MRI (native T1 mapping, ADC, and T2* mapping) and clinical indicators for the noninvasive assessment of RF in patients with CKD.

Methods

This prospective study included 152 patients with biopsy-proven CKD (RF 1: no RF, 34 patients; RF 2: mild RF, 69 patients; and RF 3: moderate to severe RF, 49 patients). The dataset was randomly partitioned into training and test cohorts at a 2:1 ratio. A two-stage model combining MobileNetV2-SE-based deep learning features with clinical indicators was developed for RF classification. Two binary tasks were performed: RF presence (RF 1 vs. RF 2 and RF 3) and severity (RF 2 vs. RF 3). Nested cross-validation was applied for model development and hyperparameter tuning, and bootstrapping was used to assess performance robustness. Model performance was evaluated with the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) visualization.

Results

Compared with single-modality models, the multimodal deep learning model (DL-combine), which is based exclusively on native T1 mapping, ADC, and T2* mapping, demonstrated favourable and stable performance (test AUC: 0.930). Among the 14 classifiers, XGBoost performed numerically better in terms of RF presence (mean AUCs: 0.986, 0.887; accuracy: 0.947, 0.829), whereas ExtraTree performed better in terms of RF severity assessment (mean AUCs: 0.935, 0.883; accuracy: 0.886, 0.848). Calibration curves and DCA confirmed robust predictive reliability and clinical utility. SHAP analysis highlighted the relative contributions of the DL-sign and eGFR.

Conclusion

This two-stage multimodal MRI-based framework provides accurate and interpretable assessment of RF across different stages of CKD, supporting noninvasive risk stratification and complementary clinical decision-making.