Development of a prediction model by combining tumor diameter and clinical parameters of adrenal incidentaloma.

ABSTRACT: When adrenal incidentalomas are detected, diagnostic procedures are complicated by the need for endocrine-stimulating tests and imaging using various modalities to evaluate whether the tumor is a hormone-producing adrenal tumor. This study aimed to develop a machine-learning-based clinical model that combines computed tomography (CT) imaging and clinical parameters for adrenal tumor classification. This was a retrospective cohort study involving 162 patients who underwent hormone testing for adrenal incidentalomas at our institution. Nominal logistic regression analysis was used to identify the predictive factors for hormone-producing adrenal tumors, and three random forest classification models were developed using clinical and imaging parameters. The study included 55 patients with non-f

SUBMITTER: Iwamoto Y 

PROVIDER: S-EPMC12510781 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature