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

Insights into Imaging

Original Article

Xuewei Wu¹  Luyan Chen¹  Xiaokai Mo¹  Zhiyuan Xiong¹  Zhuozhi Chen¹  Minmin Li¹  Yulian Chen¹  Jingjing You¹  Bin Zhang¹  Simin Chen¹  Yuanshu Guo¹  Zhe Jin¹  Qiuying Chen¹  Shuixing Zhang¹  Lu Zhang¹  Wenbo Chen² 

[1] Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, 510627, Guangzhou, Guangdong, People’s Republic of China;Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, 510627, Guangzhou, Guangdong, People’s Republic of China;Department of Radiology, Huizhou Municipal Central Hospital, No. 41 Eling Bei Road, 516001, Huizhou, Guangdong, People’s Republic of China;

关键词: Chronic renal insufficiency;    Glomerular filtration rate;    Magnetic resonance imaging;    Texture analysis;    Machine learning;   

DOI  :  10.1186/s13244-023-01370-4

 received in 2022-07-22, accepted in 2023-01-03,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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