Multi-model radiomics and machine learning for differentiating lipid-poor adrenal adenomas from metastases using automatic segmentation.

ABSTRACT:

Background

Radiomics based on automatic segmentation of CT images has emerged as a highly promising approach for differentiating adrenal adenomas from metastases in clinical practice; however, its preoperative diagnostic value has not been fully evaluated in previously developed methodologies.

Objective

To fully elucidate the diagnostic value of radiomics based on automatic segmentation techniques in differentiating adrenal adenomas from metastases through a retrospective analysis of clinical and contrast-enhanced CT (CECT) data.

Methods

A retrospective analysis was conducted on the clinical and imaging data of 416 patients with adrenal masses larger than 10 mm, who had clinically indicated contrast-enhanced CT (CECT) examinations at our hospital between January 2020

SUBMITTER: Yin S 

PROVIDER: S-EPMC12313494 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature