目的:使用扩散MRI方法评估肾间质纤维化(IF),并探讨扩散参数的皮质髓质差异(CMD),MRI参数之间的组合,或结合估计的肾小球滤过率(eGFR)获益IF评估。
方法:纳入42例慢性肾脏病患者,正在接受MRI检查。来自表观扩散系数(ADC)的MRI参数,体素内不相干运动(IVIM),扩散峰度成像(DKI),并获得了肾皮质和髓质的扩散-弛豫相关光谱成像(DR-CSI)。计算这些参数的CMD。通过活检获得病理IF评分(1-3)。患者分为轻度(IF=1,n=23)和中重度纤维化(IF=2-3,n=19)组。进行MRI参数的分组比较。通过接收器操作员曲线分析评估诊断性能,以区分轻度和中度重度IF患者。
结果:皮质ADC存在显著的组间差异,IVIM-D,IVIM-f,DKI-MD,DR-CSIVB,和DR-CSIVC。ΔADC存在显著的组间差异,ΔMD,ΔVB,ΔVC,ΔQB,和ΔQC。在皮质MRI参数中,VB显示最高的AUC=0.849,而ADC,f,MD也显示AUC>0.8。结合皮质值和CMD后,除IVIM-D外,MRI参数的诊断性能略有改善。在MRI双变量模型中,将VB与f结合可带来最佳性能(AUC=0.903)。皮质VB的组合,ΔADC,eGFR与eGFR相比,诊断性能(AUC0.963vs0.879,特异性0.826vs0.896,敏感性1.000vs0.842)明显改善。
结论:我们的研究显示了使用扩散MRI方法评估肾脏IF的有希望的结果。
■我们的研究探讨了肾脏IF的非侵入性评估,肾脏结局的独立且有效的预测因子,通过比较和组合扩散MRI方法,包括隔室,非房室,和无模型方法。
结论:轻度和中度-重度IF的扩散参数存在显著差异。一般来说,皮质参数显示比相应的CMD更好的性能。双变量模型提升了评估IF的诊断性能。
OBJECTIVE: To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation.
METHODS: Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator\'s curve analysis for discriminating mild from moderate-severe IF patients.
RESULTS: Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone.
CONCLUSIONS: Our study shows promising results for the assessment of renal IF using diffusion MRI approaches.
UNASSIGNED: Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches.
CONCLUSIONS: Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.