Journal Article

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

Jin, Zhe, Chen, Zhuozhi, Guo, Yuanshu, Wu, Xuewei, Zhang, Shuixing, Li, Minmin, Chen, Yulian, Chen, Qiuying, Chen, Wenbo, Zhang, Lu, Mo, Xiaokai, Chen, Simin, Zhang, Bin, You, Jingjing, Xiong, Zhiyuan, Chen, Luyan

2023

Overview

BackgroundTo develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.MethodsA retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.ResultsThe models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935–0.940), 0.919 (95%CI 0.916–0.922), and 0.959 (95%CI 0.956–0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800–0.807), 0.852 (95%CI 0.846–0.857), and 0.863 (95%CI 0.857–0.887) in the validation cohorts, respectively.ConclusionWe developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.Key pointsTexture analysis based on coronal T2-weighted MR images could evaluate the renal function in patients with diabetes.The All-K and LC-K outperformed other segmentation methods in the evaluation of renal function impairment.The segmentation methods could affect the results of renal function evaluation and the integrity of the coronal slices was crucial for renal imaging texture analysis.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Biomarkers / Classification / Creatinine / Diabetes / Feature extraction / Hypertension / Image segmentation / Impairment / Kidney diseases / Machine learning / Magnetic resonance imaging / Medical imaging / Performance evaluation / Renal function / Support vector machines / Texture / Training