ArticleFrontiers in endocrinology2023
Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.Pediatric radiology · 2026Review
- Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications.Diagnostics (Basel, Switzerland) · 2026Review
- Multimodal MRI-based two-stage artificial intelligence framework for renal fibrosis classification in chronic kidney disease.BMC nephrology · 2026Article
- Machine learning predicts diabetes risk in high-risk populations: analysis of National Health and Nutrition Examination Survey data.Archives of medical science : AMS · 2026Article
- Machine learning models predict the progression of long-term renal insufficiency in patients with renal cancer after radical nephrectomy.BMC nephrology · 2024Article
- Native TiScience · 2024Article
- Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis.Journal of inflammation research · 2023Article
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9 authors.
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Abstract
Introduction: Diabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN. Methods: For this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort ( Results: The mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort. Discussion: The model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function.
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