Evaluation of split renal dysfunction using radiomics based on magnetic resonance diffusion‐weighted imaging

Author:

Zhao Shengchao123,Ding Yi4,Gan Lijuan5,Yang Pei4,Xie Yuanliang4,Hu Yun4,Chen Jun6,Wang Xiang4,Huang Zengfa4,Zhou Bin123

Affiliation:

1. Center of Interventional Medicine The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guangdong Province China

2. Center of Cerebrovascular Disease The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guangdong Province China

3. Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging The Fifth Affiliated Hospital of Sun Yat‐sen University Zhuhai Guangdong Province China

4. Department of Radiology The Central Hospital of Wuhan Tongji Medical College Huazhong University of Science and Technology Wuhan China

5. Department of Gynecological Oncology Zhongnan Hospital of Wuhan University Wuhan China

6. GE Healthcare Wuhan China

Abstract

AbstractBackgroundAccurate and noninvasive assessment of split renal dysfunction is crucial, while there is lack of corresponding method clinically.PurposeTo investigate the feasibility of using diffusion‐weighted imaging (DWI)‐based radiomics models to evaluate split renal dysfunction.MethodsWe enrolled patients with impaired and normal renal function undergoing renal DWI examination. Glomerular filtration rate (GFR, mL/min) was measured using 99mTc‐DTPA scintigraphy, which is reference standard of GFR measurement. The kidneys were classified into normal (GFR ≥40), mildly impaired (20≤ GFR < 40), moderately impaired (10≤ GFR < 20), and severely impaired (GFR < 10) renal function groups. Optimized subsets of radiomics features were selected from renal DWI images and radiomics scores (Rad‐score) calculated to discriminate groups with different renal function. The radiomics model (Rad‐score based) was developed in a training cohort and validated in a test cohort. Evaluations were conducted on the discrimination, calibration, and clinical application of the method.ResultsThe final analysis included 330 kidneys. Logistic regression was used to develop three radiomics models, model A, B, and C, which were used to distinguish normal from impaired, mild from moderate, and moderate from severe renal function, respectively. The area under the curve of the three models were 0.822, 0.704, and 0.887 in the training cohort and 0.843, 0.717, and 0.897 in the test cohort, respectively, indicating efficient discrimination performance.ConclusionsDWI‐based radiomics models have potential for evaluating split renal dysfunction and discriminating between normal and impaired renal function groups and their subgroups.

Publisher

Wiley

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