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已发表论文
超分辨率 MRI 多影像组学特征用于预测 Wagner 3 级糖尿病足溃疡小截肢
Authors Cao B, Zhong J, Zhang J, Zhao D
Received 24 September 2025
Accepted for publication 14 January 2026
Published 10 February 2026 Volume 2026:19 565938
DOI https://doi.org/10.2147/DMSO.S565938
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Rebecca Baqiyyah Conway
Bin Cao,1,2,* Jiali Zhong,3,* Jie Zhang,4 Dong Zhao1,2
1Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Key Laboratory of Diabetes Research and Care, Beijing, 101149, People’s Republic of China; 3Department of Medical Imaging, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Department of Vascular Surgery, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dong Zhao, Email zhaodong@ccmu.edu.cn Jie Zhang, Email Zhangjiesjz@vip.163.com
Aim: This study aimed to investigate effectiveness of multi-radiomics features (RFs) extracted from super-resolution MRI for predicting minor amputation in patients with Wagner 3 diabetic foot ulcers.
Methods: 275 eligible patients with Wagner 3 ulcers were retrospectively included and randomly divided into a training set (n = 192) and a test set (n = 83). A deep-transfer learning network was utilized to process coronal proton density weighted images (PDWI) and generate super-resolution PDWI (SRPDWI). RFs were extracted separately from soft tissues and phalanges. Random forest models were then developed to predict minor amputation.
Results: The minor amputation rate was 36.4% for the left foot and 32.2% for the right foot. All random forest models demonstrated excellent performance in the training set, achieving AUC ranging from 0.91 to 0.99. In the test set, the integrated radiomics model (combining RFs extracted from both soft tissue and phalanges) exhibited superior performance, with an AUC of 0.83, sensitivity of 0.71, and specificity of 0.79. Additionally, the inclusion of clinical indicators did not enhance the predictive performance of the integrated radiomics model.
Conclusion: The multi-RFs extracted from SRPDWI demonstrated promising potential in predicting the risk of minor amputation for Wagner 3 diabetic foot ulcers.
Keywords: diabetic foot ulcer, super-resolution, radiomics, minor amputation
1Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Beijing Key Laboratory of Diabetes Research and Care, Beijing, 101149, People’s Republic of China; 3Department of Medical Imaging, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Department of Vascular Surgery, Beijing Luhe Hospital, Capital Medical University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dong Zhao, Email zhaodong@ccmu.edu.cn Jie Zhang, Email Zhangjiesjz@vip.163.com
Aim: This study aimed to investigate effectiveness of multi-radiomics features (RFs) extracted from super-resolution MRI for predicting minor amputation in patients with Wagner 3 diabetic foot ulcers.
Methods: 275 eligible patients with Wagner 3 ulcers were retrospectively included and randomly divided into a training set (n = 192) and a test set (n = 83). A deep-transfer learning network was utilized to process coronal proton density weighted images (PDWI) and generate super-resolution PDWI (SRPDWI). RFs were extracted separately from soft tissues and phalanges. Random forest models were then developed to predict minor amputation.
Results: The minor amputation rate was 36.4% for the left foot and 32.2% for the right foot. All random forest models demonstrated excellent performance in the training set, achieving AUC ranging from 0.91 to 0.99. In the test set, the integrated radiomics model (combining RFs extracted from both soft tissue and phalanges) exhibited superior performance, with an AUC of 0.83, sensitivity of 0.71, and specificity of 0.79. Additionally, the inclusion of clinical indicators did not enhance the predictive performance of the integrated radiomics model.
Conclusion: The multi-RFs extracted from SRPDWI demonstrated promising potential in predicting the risk of minor amputation for Wagner 3 diabetic foot ulcers.
Keywords: diabetic foot ulcer, super-resolution, radiomics, minor amputation