Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis
Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis
复制标题
多参数磁共振成像评估慢性肾脏病和肾纤维化的价值
作者：
Chenchen Hua;Lu Qiu;Leting Zhou;Zhuang Yi;Ting Cai;Bin Xu;Shaowei Hao;Xiangming Fang;Liang Wang;Haoxiang Jiang
目的筛选评价慢性肾脏病（CKD）和肾间质纤维化（IF）的最佳MRI指标。材料与方法本前瞻性研究包括43例CKD患者和20例对照。根据病理结果将CKD组分为轻度和中重度两个亚组。扫描序列包括T1标测、R2* 标测、体素内非相干运动成像和弥散加权成像。采用单因素方差分析比较各组MRI参数。以年龄作为协变量，分析MRI参数与估计肾小球滤过率（eGFR）和肾脏IF的相关性。支持向量机（SVM）模型被用来评估多参数MRI的诊断效果。结果与对照组比较，轻、中、重度组肾皮质表观弥散系数（cADC）、髓质表观弥散系数（mADC）、皮质纯弥散系数（cDt）、髓质表观弥散系数（mDt）、皮质移位表观弥散系数（csADC）、髓质表观弥散系数（msADC）逐渐降低，皮质T1（cT 1）、髓质T1（mT 1）逐渐升高。cADC、mADC、cDt、mDt、cT 1、mT 1、csADC和msADC值与eGFR和IF显著相关（p < 0.001）。SVM模型表明，结合cT 1和csADC的多参数MRI可以区分CKD患者和对照组，具有较高的准确性（0.84）、灵敏度（0.70）和特异性（0.92）（AUC：0.96）。结合cT 1和cADC的多参数MRI在评估IF严重程度（AUC：0.96）方面表现出较高的准确性（0.91）、敏感性（0.95）和特异性（0.81）。结论多参数MRI结合T1标测和弥散成像对CKD和IF的无创性评价具有临床应用价值。临床相关性声明本研究表明，结合T1标测和弥散成像的多参数MRI在临床上可用于慢性肾脏病（CKD）和间质纤维化的无创评估;这可为风险分层、诊断、治疗和预后提供信息。关键点·研究了用于评估慢性肾脏疾病和肾间质纤维化的优化MRI标志物。·肾皮质/髓质T1值随着间质纤维化增加而增加;皮质偏移表观扩散系数（csADC）与eGFR和间质纤维化显著相关。·结合皮质T1（cT 1）和csADC/cADC的支持向量机（SVM）可有效识别慢性肾脏疾病并准确预测肾间质纤维化。
Objectives To identify optimized MRI markers for evaluating chronic kidney disease (CKD) and renal interstitial fibrosis (IF). Materials and methods This prospective study included 43 patients with CKD and 20 controls. The CKD group was divided into mild and moderate-to-severe subgroups based on pathological results. Scanned sequences included T1 mapping, R2* mapping, intravoxel incoherent motion imaging, and diffusion-weighted imaging. One-way analyses of variance were used to compare MRI parameters among groups. Correlations of MRI parameters with estimated glomerular filtration rate (eGFR) and renal IF were analyzed using age as covariates. The support vector machine (SVM) model was used to evaluate the diagnostic efficacy of multiparametric MRI. Results Compared to control values, renal cortical apparent diffusion coefficient (cADC), medullary ADC (mADC), cortical pure diffusion coefficient (cDt), medullary Dt (mDt), cortical shifted apparent diffusion coefficient (csADC), and medullary sADC (msADC) values gradually decreased in the mild and moderate-to-severe groups, while cortical T1 (cT1) and medullary T1 (mT1) values gradually increased. Values of cADC, mADC, cDt, mDt, cT1, mT1, csADC, and msADC were significantly associated with eGFR and IF ( p < 0.001). The SVM model indicated that multiparametric MRI combining cT1 and csADC can distinguish patients with CKD from controls with high accuracy (0.84), sensitivity (0.70), and specificity (0.92) (AUC: 0.96). Multiparametric MRI combining cT1 and cADC exhibited high accuracy (0.91), sensitivity (0.95), and specificity (0.81) for evaluating IF severity (AUC: 0.96). Conclusion Multiparametric MRI combining T1 mapping and diffusion imaging may be of clinical utility in non-invasive assessment of CKD and IF. Clinical relevance statement This study shows that multiparametric MRI combining T1 mapping and diffusion imaging may be clinically useful in the non-invasive assessment of chronic kidney disease (CKD) and interstitial fibrosis; this could provide information for risk stratification, diagnosis, treatment, and prognosis. Key Points • Optimized MRI markers for evaluating chronic kidney disease and renal interstitial fibrosis were investigated . • Renal cortex/medullary T1 values increased as interstitial fibrosis increased; cortical shifted apparent diffusion coefficient (csADC) correlated significantly with eGFR and interstitial fibrosis . • Support vector machine (SVM) combining cortical T1 (cT1) and csADC/cADC effectively identifies chronic kidney disease and accurately predicts renal interstitial fibrosis .
影响因子：
39.2
作者：
Levey AS;Stevens LA;Schmid CH;Zhang YL;Castro AF 3rd;Feldman HI;Kusek JW;Eggers P;Van Lente F;Greene T;Coresh J;CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
通讯作者：
CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
影响因子：
19.6
作者：
Bane, Octavia;Hectors, Stefanie J.;Taouli, Bachir
通讯作者：
Taouli, Bachir