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Published on: June 23, 2015
Kidney MRI Texture Analysis-A Universal Assessment of Kidney State and Function?
Marcin Majos1, Artur Klepaczko2, Katarzyna Szychowska3
1Department of Normal and Clinical Anatomy, Medical University of Lodz, 90-419 Lodz, Poland.
Journal of Clinical Medicine
|June 26, 2026
Summary
Magnetic Resonance Imaging (MRI) texture analysis shows promise for assessing chronic kidney disease (CKD) activity and function. This AI-driven approach correlates with estimated glomerular filtration rate (eGFR), offering a non-invasive diagnostic tool.
Area of Science:
- Radiology
- Nephrology
- Artificial Intelligence
Background:
- Current chronic kidney disease (CKD) diagnosis relies on glomerular filtration rate (GFR), proteinuria, or kidney biopsy.
- Advancements in MRI techniques and AI offer new possibilities for assessing CKD activity and kidney function through image analysis.
Purpose of the Study:
- To evaluate the efficacy of MRI image texture analysis combined with AI for assessing CKD activity and kidney function.
- To determine the correlation between MRI-derived features and estimated GFR (eGFR).
Main Methods:
- MRI scans (1.5 T or 3 T) were acquired from healthy volunteers and CKD patients.
- Patients underwent kidney biopsy for histopathological assessment of CKD activity.
- An AI algorithm was developed using T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI) data.
Main Results:
- The AI algorithm achieved high sensitivity (77.78-87.50%) and specificity (86.67-94.12%) in distinguishing CKD patients.
- T1-weighted and T2-weighted MRI features showed strong correlations with GFR (coefficients -0.5922 to -0.7090 and 0.6126 to 0.6380, respectively).
Conclusions:
- MRI image texture analysis is a viable method for assessing CKD activity, independent of the MRI scanner used.
- MRI texture features demonstrate a significant correlation with eGFR values, supporting its use in kidney function assessment.
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