Frontiers in endocrinology · 2025
Accurate screening of patients with diabetes based on physical examination: pancreas imaging features from chest CT and laboratory data.
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Yi Jixing, Lin Jiarun, Feng Qing, Huang Zhou, Li Shu, Liang Bumin, Wu Haohua, Yan Haiwei, Yang Eqing, Wu Jiaquan, Liang Yiqiong, Liu Xuanhan, Chen Jin, Xu Fengming, Li Tao
Аннотация
BACKGROUND: Fasting plasma glucose (FBG) was used in the large-scale primary screening of diabetes mellitus (DM). However, some people who actually have DM have normal fasting glucose (NFG), which shows a high false negative rate.
OBJECTIVE: To investigate the application value of pancreatic radiomics features and laboratory data from chest CT scan in screening DM patients.
METHODS: Patients with DM were diagnosed according to HbA1c≥48 mmol/mol(6.5%). The radiomics features of pancreas in lung window (L) and soft tissue window (S) of chest CT and laboratory data of 3587 patients from D1 (model training and testing) and D2/D3/D4/D5 (external validation) were retrospectively analyzed to construct a diagnostic model for DM screening.
RESULTS: The AUC of the lung window-laboratory models (L-Lab-LR and L-Lab-SVM) in the test set were 0.958/0.965. The AUC of soft tissue windows-clinical models (S-Lab-LR and S-Lab-SVM) were 0.958/0.969, 0.875/0.881, 0.935/0.959, 0.905/0.919, respectively.
CONCLUSIONS: The diagnostic model based on chest CT pancreatic radiomics features and laboratory data has a very high diagnostic efficiency, which is accurate and reliable for the initial screening of DM patients. This method will facilitate early diagnosis of those individuals with DM who may have been missed.
OBJECTIVE: To investigate the application value of pancreatic radiomics features and laboratory data from chest CT scan in screening DM patients.
METHODS: Patients with DM were diagnosed according to HbA1c≥48 mmol/mol(6.5%). The radiomics features of pancreas in lung window (L) and soft tissue window (S) of chest CT and laboratory data of 3587 patients from D1 (model training and testing) and D2/D3/D4/D5 (external validation) were retrospectively analyzed to construct a diagnostic model for DM screening.
RESULTS: The AUC of the lung window-laboratory models (L-Lab-LR and L-Lab-SVM) in the test set were 0.958/0.965. The AUC of soft tissue windows-clinical models (S-Lab-LR and S-Lab-SVM) were 0.958/0.969, 0.875/0.881, 0.935/0.959, 0.905/0.919, respectively.
CONCLUSIONS: The diagnostic model based on chest CT pancreatic radiomics features and laboratory data has a very high diagnostic efficiency, which is accurate and reliable for the initial screening of DM patients. This method will facilitate early diagnosis of those individuals with DM who may have been missed.