Deep learning model for automatic detection of incidental adrenal abnormalities on low-dose computed tomography images.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Peking University First Hospital, Beijing, 100034, China.
- Department of Urology, Peking University First Hospital; Institution of Urology, Peking University; Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center; National Urological Cancer Center, Beijing, 100034, China.
- Peking University First Hospital-Miyun Hospital, Beijing, China.
- Beijing Smart Tree Medical Technology Co., Ltd, Beijing, 102200, China.
- Department of Urology, Peking University First Hospital; Institution of Urology, Peking University; Beijing Key Laboratory of Urogenital Diseases (Male) Molecular Diagnosis and Treatment Center; National Urological Cancer Center, Beijing, 100034, China. [email protected].
Abstract
To investigate the feasibility of employing deep learning models for automated segmentation and classification of adrenal incidental abnormalities on low-dose CT images. Four distinct CT cohorts were retrospectively collected for deep learning models development (cohort A, n = 2574; cohort B, n = 1205), internal evaluation (cohort C, n = 3681), and external evaluation (cohort D, n = 779). Two experienced uroradiologists independently reviewed the CT images and labeled the adrenal glands as normal or abnormal based on predefined criteria encompassing both density and morphological abnormalities, with any discrepancies resolved through consultation. The model development cohorts were divided into a training set, a validation set, and a test set. Deep learning models for segmentation and classification were trained and evaluated on internal and external sets, with the dice similarity coefficient (DSC), area under precision-recall curves (AUPRC), and area under receiver operating characteristic curves (AUROC) as evaluation metrics. Adrenal descriptions from radiology reports were extracted to compare with the model's performance. For adrenal gland segmentation, the DSC values for the test set, internal validation cohort, and external validation cohort were 0.839 (IQR: 0.783-0.871), 0.870 (IQR: 0.819-0.902), and 0.799 (IQR: 0.729-0.849), respectively. For adrenal gland classification, the AI model achieved AUPRC values of 0.913, 0.753, and 0.927 in the test set, internal validation cohort, and external validation cohort, respectively, outperforming routine radiology reporting (AUPRC: 0.809, 0.708, 0.591; all P < 0.05). Corresponding AUROC values were 0.956, 0.942, and 0.977 for the AI model, which also outperformed routine radiology reporting (AUROC: 0.889, 0.705, 0.551; all P < 0.05). The deep learning models showed promise in automated adrenal segmentation and classification, highlighting AI's potential to improve detection of adrenal abnormalities in LDCT scans. This study has been registered on ClinicalTrials.gov on August 25, 2025, with the unique identifier NCT07198152.