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Automatic recognition of adrenal incidentalomas using a two-stage cascade network: a multicenter study.

Xiao Xie¹, Sheng-Xiao Ma^1,2, Xiang-De Luo^3,4

¹Department of Urology, Third Affiliated Hospital, Southern Medical University, Guangzhou, China.

Annals of Medicine

|August 7, 2025

Summary

A deep learning model can automatically detect adrenal incidentalomas (AIs) on CT scans. This AI tool shows high accuracy, aiding early diagnosis of adrenal diseases.

Keywords:

Adrenal incidentalomas CT deep learning machine learning radiomics

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

The increasing incidence of adrenal incidentalomas (AIs) necessitates efficient detection methods.
Early identification of AIs is crucial for managing adrenal diseases like Cushing's syndrome and pheochromocytoma.

Purpose of the Study:

To develop and validate a deep learning-based automated system for adrenal incidentaloma detection.
To assess the performance of a two-stage cascade network for AI identification in nonenhanced CT scans.

Main Methods:

A multicenter retrospective study involving 778 patients.
Development of a two-stage cascade network comprising a 3D Res-Unet for adrenal gland segmentation and a classifier for AI detection.
Evaluation using Dice similarity coefficient (DSC), Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, sensitivity, and specificity.

Main Results:

The model achieved high performance in the validation set with AUCs of 88.15% for left AIs and 87.90% for right AIs.
No significant difference was found between the deep learning model and manual segmentation (p > 0.05).
In the test cohort, the cascade network demonstrated AUCs >80% and accuracy >75% for both adrenal glands.

Conclusions:

A two-stage cascade network utilizing deep learning enables automatic recognition of adrenal incidentalomas.
This deep learning algorithm is effective for AI detection in nonenhanced CT scans across different medical centers.