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

• Published in: Frontiers in oncology Vol. 12; p. 876967
• Main Authors: Chen, Jifan, Jin, Peile, Song, Yue, Feng, Liting, Lu, Jiayue, Chen, Hongjian, Xin, Lei, Qiu, Fuqiang, Cong, Zhang, Shen, Jiaxin, Zhao, Yanan, Xu, Wen, Cai, Chenxi, Zhou, Yan, Yang, Jinfeng, Zhang, Chao, Chen, Qin, Jing, Xiang, Huang, Pintong
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 04.07.2022
• Subjects: deep learning; diabetic kidney disease; multicenter; Oncology; radiomics; ultrasound
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.876967

Background An 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. Purpose In 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 Methods The 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. Results A 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. Conclusion In 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.