Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models

被引:0
|
作者
Zhu, Jianbing [1 ,2 ]
Gan, Meng [3 ]
Yang, Yi [4 ]
Pang, Hongquan [1 ]
Zhu, Zhengyang [5 ]
Hou, Zujun [6 ]
Hou, Guocun [7 ]
Wang, Cong [8 ]
机构
[1] Nanjing Univ, Affiliated Hosp, Suzhou Hosp, Dept Radiol,Med Sch,Suzhou Res Ctr, Suzhou 215153, Peoples R China
[2] Nanjing Univ, Affiliated Hosp, Suzhou Hosp, Tongan Branch Hosp,Med Sch, Suzhou 215100, Peoples R China
[3] Suzhou Vocat Univ, Sch Comp Engn, Suzhou 215104, Peoples R China
[4] Nanjing Univ, Affiliated Hosp, Suzhou Hosp, Dept Nucl Med,Med Sch, Suzhou 215153, Peoples R China
[5] Nanjing Univ, Affiliated Drum Tower Hosp, Med Sch, Dept Radiol, Nanjing 210093, Peoples R China
[6] Wenzhou Med Univ, Affiliated Hosp 2, Wenzhou 325027, Peoples R China
[7] Nanjing Univ, Affiliated Hosp, Suzhou Hosp, Dept Nephrol,Med Sch, Suzhou 215153, Peoples R China
[8] Suzhou Vocat Univ, Sch Elect Informat Engn, Suzhou 215104, Peoples R China
基金
中国国家自然科学基金;
关键词
Diffusion weighted imaging; Glomerular filtration rate; Magnetic resonance imaging; Kidney; Regression; RENAL-FUNCTION; DISEASE; GFR; MRI;
D O I
10.1007/s00261-024-04489-0
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. Methods We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Results Fifteen (male/female, 10/5; age, 41.60 +/- 10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient (p<0.001). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient (p<0.001). Conclusion The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.
引用
收藏
页码:336 / 345
页数:10
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