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Updated: Sep 23, 2026
Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
Published on: January 8, 2018
CT-based radiomics analysis adds value to glycometabolism assessment in adrenal adenoma: an exploratory observational
Baofeng Wu1, Xiaojuan Tian2, Changxing Fang3
1Department of Endocrinology, First Hospital of Shanxi Medical University, Taiyuan 030000, China; First Clinical Medical College, Shanxi Medical University, Taiyuan 030000, China.
Objective:
This exploratory observational study examined CT radiomics features associated with glucose metabolism in adrenal adenoma (AA).
Methods:
A total of 114 patients were initially enrolled, 106 who completed continuous glucose monitoring (CGM) were analyzed and classified as impaired glucose metabolism (IGM; HbA1c ≥5.7%, n=58) or normal glucose metabolism (NGM; n=48). Radiomics features were extracted from non-contrast (NC), arterial-phase (AP), and venous-phase (VP) CT images. After reproducibility assessment, correlation filtering, and LASSO selection, seven features were integrated into a Rad-score. Logistic regression evaluated the association between Rad-score and IGM after adjustment for age, body mass index, and midnight cortisol. Discrimination was assessed by ROC analysis, with 1,000 bootstrap resamples for internal validation.
Results:
Rad-score was higher in IGM than NGM (0.377 ± 0.510 vs -0.029 ± 0.808; P=0.002). It was associated with IGM in univariable analysis (OR=2.718, 95% CI:1.360-5.433; P=0.005) and remained significant after multivariable adjustment (adjusted OR=2.681, 95% CI:1.304-5.512; P=0.007). AUCs were 0.623 for the clinical model, 0.673 for the radiomics model, and 0.704 for the combined model; the combined-versus-clinical difference was not significant by the DeLong test (P=0.101). The bootstrap-corrected AUC of the combined model was 0.606.
Conclusion:
A seven-feature CT radiomics signature was independently associated with IGM in patients with AA. These findings are exploratory and require validation in independent cohorts.