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ABSTRACT: Background
The incidence of adrenal incidentalomas is increasing annually, and most types of adrenal masses require surgical intervention. Accurate classification of common adrenal masses based on tumor computed tomography (CT) images by radiologists or clinicians requires extensive experience and is often challenging, which increases the workload of radiologists and leads to unnecessary adrenal surgeries. There is an urgent need for a fully automated, noninvasive, and precise approach for the identification and accurate classification of common adrenal masses.Objective
This study aims to enhance diagnostic efficiency and transform the current clinical practice of preoperative diagnosis of adrenal masses.Methods
This study is a retrospective analysis that includes p
SUBMITTER: Yang L
PROVIDER: S-EPMC12062765 | biostudies-literature | 2025 Apr
REPOSITORIES: biostudies-literature