La Radiologia medica · 2026
xsCT-based radiomics for adrenal incidentaloma characterization.
СтатьяНазвание и аннотация: Перевод в очереди
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Anichini Matilde, Grazzini Giulia, Mattolini Lavinia, Codignola Giorgio, Paolucci Sebastiano, Pradella Silvia, Miele Vittorio
Аннотация
PURPOSE: The aim of this retrospective monocentric study is to examine the role of radiomics in distinguishing benign and malignant adrenal incidentalomas (AIs) analyzing computed tomography (CT) images.
MATERIAL AND METHODS: We retrospectively selected patients with clinical or histologic diagnosis of adrenal pathology studied in the period 2011-2025 with pre-operative or pre-diagnostic CT in our center. Two radiology residents and a radiologist with 10 years of experience delineated the volume of interest of the AI using 3D-slicer software. All 135 patients included in the analysis underwent segmentation of the non-contrast acquisition; among these, 115 were also segmented on the portal phase acquisition. Statistical analysis was performed by dividing patients into two groups: group 0, benign adrenal pathology (adenoma, myelolipoma, ganglioneuroma, pseudocyst); group 1, malignant adrenal pathology (pheochromocytoma, adrenal cortical carcinoma, metastasis, lymphoma).
RESULTS: 135 patients were included. On non-contrast acquisition, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish patients belonging to Group 0 or Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.88-95% CI 0.83-0.94). For portal phase, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish Group 0 and Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.84- 95% CI 0.77-0.91).
CONCLUSION: The radiomic models developed from both non-contrast and portal CT phases were effective in differentiating benign from malignant AIs.
MATERIAL AND METHODS: We retrospectively selected patients with clinical or histologic diagnosis of adrenal pathology studied in the period 2011-2025 with pre-operative or pre-diagnostic CT in our center. Two radiology residents and a radiologist with 10 years of experience delineated the volume of interest of the AI using 3D-slicer software. All 135 patients included in the analysis underwent segmentation of the non-contrast acquisition; among these, 115 were also segmented on the portal phase acquisition. Statistical analysis was performed by dividing patients into two groups: group 0, benign adrenal pathology (adenoma, myelolipoma, ganglioneuroma, pseudocyst); group 1, malignant adrenal pathology (pheochromocytoma, adrenal cortical carcinoma, metastasis, lymphoma).
RESULTS: 135 patients were included. On non-contrast acquisition, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish patients belonging to Group 0 or Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.88-95% CI 0.83-0.94). For portal phase, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish Group 0 and Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.84- 95% CI 0.77-0.91).
CONCLUSION: The radiomic models developed from both non-contrast and portal CT phases were effective in differentiating benign from malignant AIs.