Auto-Segmentation Ultrasound-Based Radiomics Technology to Stratify Patient With Diabetic Kidney Disease: A Multi-Center Retrospective Study

Frontiers in Oncology

Jiayue Lu¹  Jifan Chen²  Qin Chen³  Liting Feng³  Chenxi Cai⁴  Jinfeng Yang⁴  Yan Zhou⁵  Xiang Jing⁵  Hongjian Chen⁶  Pintong Huang⁷  Jiaxin Shen⁸  Lei Xin⁸  Peile Jin⁸  Wen Xu⁸  Yue Song⁸  Chao Zhang⁸  Fuqiang Qiu⁸  Zhang Cong⁸  Yanan Zhao⁸ 

[1] Department of Clinical Laboratory, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China;Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China;Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China;Department of Ultrasound, The People’s Hospital of Yinshang, Anhui, China;Department of Ultrasound, Tianjin Third Central Hospital, Tianjin, China;Post-Doctoral Research Center, Hangzhou Supor South Ocean Pharmaceutical Co., Ltd, Hangzhou, China;Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, China;Ultrasound in Medicine and Biomedical Engineering Research Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China;

关键词: ultrasound;    radiomics;    deep learning;    diabetic kidney disease;    multicenter;   

DOI  :  10.3389/fonc.2022.876967

来源: DOAJ

【 摘 要 】

BackgroundAn increasing proportion of patients with diabetic kidney disease (DKD) has been observed among incident hemodialysis patients in large cities, which is consistent with the continuous growth of diabetes in the past 20 years.PurposeIn this multicenter retrospective study, we developed a deep learning (DL)-based automatic segmentation and radiomics technology to stratify patients with DKD and evaluate the possibility of clinical application across centers.Materials and MethodsThe research participants were enrolled retrospectively and separated into three parts: training, validation, and independent test datasets for further analysis. DeepLabV3+ network, PyRadiomics package, and least absolute shrinkage and selection operator were used for segmentation, extraction of radiomics variables, and regression, respectively.ResultsA total of 499 patients from three centers were enrolled in this study including 246 patients with type II diabetes mellitus (T2DM) and 253 patients with DKD. The mean intersection-over-union (Miou) and mean pixel accuracy (mPA) of automatic segmentation of the data from the three medical centers were 0.812 ± 0.003, 0.781 ± 0.009, 0.805 ± 0.020 and 0.890 ± 0.004, 0.870 ± 0.002, 0.893 ± 0.007, respectively. The variables from the renal parenchyma and sinus provided different information for the diagnosis and follow-up of DKD. The area under the curve (AUC) of the radiomics model for differentiating between DKD and T2DM patients was 0.674 ± 0.074 and for differentiating between the high and low stages of DKD was 0.803 ± 0.037.ConclusionIn this study, we developed a DL-based automatic segmentation, radiomics technology to stratify patients with DKD. The DL technology was proposed to achieve fast and accurate anatomical-level segmentation in the kidney, and an ultrasound-based radiomics model can achieve high diagnostic performance in the diagnosis and follow-up of patients with DKD.

【 授权许可】

Unknown