A diabetic foot classification model based on radiomics features of fundus photographs
10.3760/cma.j.cn311282-20220407-00213
- VernacularTitle:基于眼底照片影像组学特征的糖尿病足分类模型构建
- Author:
Ying LI
1
;
Yijuan HUANG
;
Xiaokang LIANG
;
Zhentai LU
;
Dan SUN
;
Fang GAO
;
Yaoming XUE
;
Ying CAO
Author Information
1. 南方医科大学南方医院内分泌代谢科，广州 510515
- Keywords:
Diabetic foot;
Fundus photographs;
Radiomics;
Prediction model
- From:
Chinese Journal of Endocrinology and Metabolism
2023;39(2):103-111
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a diabetic foot classification prediction model based on radiomics features of fundus photographs.Methods:A total of 2 035 fundus photographs of patients with type 2 diabetes diagnosed at Nanfang Hospital between December 2011 and December 2018 were retrospectively collected [282 photographs from patients with diabetic foot(DF), and 1 753 from patients with diabetes mellitus(DM)]. All fundus photographs were randomly divided into a training set(1 424 photos) and a test set(611 photos) using a computer generated random number at 7∶3. After pre-processing the fundus photographs, a total of 4 128 texture features based on the gray matrix were extracted by the Radiomic toolkit, and 11 339 other features were extracted using the ToolboxDESC toolkit. The LASSO algorithm was used to select the 30 features most relevant to DF, and then the Bootstrap + 0.632 self-sampling method was used to further select the 7 best combinations. Logistic regression analysis was used to obtain the regression coefficients and establish the final diabetic foot classification prediction model. ROC curve was drawn, and AUC, sensitivity, specificity, and accuracy of the training and test sets were calculated to verify its prediction performance. Results:We screened 7 fundus radiomics markers for diabetic foot patients, and based on this established a DF/DM classification prediction model. The AUC, sensitivity, specificity, and accuracy of the model were 0.958 6, 0.984 0, 0.920 0, and 0.928 0 in the training set, and 0.927 1, 0.988 9, 0.881 0, and 0.896 9 in the test set, respectively.Conclusion:In this study, seven DF fundus markers were screened using radiomics technology. Based on this, a highly accurate and easy-to-use DF/DM classification model was constructed. This technology has the potential to increase the efficiency of DF screening programs.
