Diagnostic performance of mDIXON-Quant imaging in assessing glomerulosclerosis severity for chronic kidney disease: a comparative study of R2* and fat fraction parameters
To compare two region-of-interest (ROI) measurement methods based on mDIXON-Quant for distinguishing healthy volunteers (HV), chronic kidney disease (CKD) patients with low (≤ 15%) and high (> 15%) glomerulosclerosis (GS), and establish a non-invasive auxiliary protocol. A total of 42 CKD patients and 25 HV underwent mDIXON-Quant MRI. CKD patients were stratified into low (n = 24) and high (n = 18) GS groups via renal biopsy. Two ROI methods were applied: Method 1 (cortex/medulla separation, 15–20 mm² circular ROIs at renal hilum, upper/lower poles) and Method 2 (whole-kidney contour tracing). R2* and fat fraction (FF) parameters were analyzed using ANOVA, LSD tests, and DeLong tests for diagnostic efficacy (AUC). R2* parameters (RcR2*, RR2*) showed significant differences across groups (P < 0.05), increasing with GS severity. LcR2* and RmR2* distinguished HV from high GS (P < 0.05), while LFF differentiated low vs. high GS (P < 0.05). Combining FF with R2* improved AUC from 0.685 to 0.877 (P < 0.05) for low/high GS discrimination. Method 1’s RcR2* (AUC = 0.957) and Method 2’s RR2* (AUC = 0.9) were most accurate for distinguishing high GS from HV, with no significant difference between them (P > 0.05). RcR2* outperformed medullary parameters (P < 0.05). The present study demonstrates that employing the Method 2(whole-kidney contour tracing) based on mDIXON-Quant technology enables auxiliary quantitative assessment of the severity of GS via the R2 parameter of the right kidney, thereby confirming its role as an adjunctive tool in GS evaluation.
Authors
- Wenhui Wang (ORCID: https://orcid.org/0000-0001-6681-1373)
- Heng Zhang (ORCID: https://orcid.org/0000-0002-0103-2173)
- Jiazheng Wang (ORCID: https://orcid.org/0000-0003-1182-442X)
- Ye Ju (ORCID: https://orcid.org/0009-0004-2744-2260)
- Q. Hu (ORCID: https://orcid.org/0009-0008-1098-3502)
- Qianyu Zhang (ORCID: https://orcid.org/0009-0004-7142-2718)
- Ailian Liu
- Liangjie Lin
- Qingwei Song
- Renna Luo
- Nan Wang
Institutions
- Dalian Medical University (CN)
- Liaoning University of Technology (CN)
- Philips (China) (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2026-03-26
- DOI
- https://doi.org/10.1186/s12880-026-02259-6
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00