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

作者：Xiaokai Mo, Wenbo Chen, Simin Chen, Zhuozhi Chen, Yuanshu Guo, Yulian Chen, Xuewei Wu, Lu Zhang, Qiuying Chen, Zhe Jin, Minmin Li, Luyan Chen, Jingjing You, Zhiyuan Xiong, Bin Zhang, Shuixing Zhang · 发表于：Insights into Imaging · 年份：2023 · DOI：10.1186/s13244-023-01370-4 · 被引用次数：16 · 研究领域：MRI in cancer diagnosis、Pediatric Urology and Nephrology Studies、Chronic Kidney Disease and Diabetes

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A 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. RESULTS: The 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 (...