期刊
ENERGY STORAGE MATERIALS
卷 56, 期 -, 页码 e41-e46出版社
ELSEVIER
DOI: 10.5114/pjr.2023.124435
关键词
non-functioning adrenal incidentalomas; adrenal Cushing?s syndrome; magnetic resonance imaging; ma-chine learning
This study aimed to develop a radiomics signature-based MRI method for diagnosing adrenal Cushing's syndrome in adrenal incidentalomas. By studying 50 patients with adrenal incidentalomas, it was found that MRI-based radiomic scores can be used to predict adrenal Cushing's syndrome.
Purpose: The aim of this study was to develop radiomics signature-based magnetic resonance imaging (MRI) to determine adrenal Cushing's syndrome (ACS) in adrenal incidentalomas (AI).Material and methods: A total of 50 patients with AI were included in this study. The patients were grouped as non-functional adrenal incidentaloma (NFAI) and ACS. The lesions were segmented on unenhanced T1-weighted (T1W) in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3-Tesla MRIs. The LASSO regression model was used for the selection of potential predictors from 111 texture features for each sequence. The radiomics scores were compared between the groups.Results: The median radiomics score in T1W-Op for the NFAI and ACS were -1.17 and -0.17, respectively (p < 0.001). Patients with ACS had significantly higher radiomics scores than NFAI patients in all phases (p < 0.001 for all). The AUCs for radiomics scores in T1W-Op, T1W-Ip, and T2W were 0.862 (95% CI: 0.742-0.983), 0.892 (95% CI: 0.774-0.999), and 0.994 (95% CI: 0.982-0.999), respectively.Conclusion: The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS.
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