Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images.

ABSTRACT:

Background

Differentiating adrenal adenomas from metastases poses a significant challenge, particularly in patients with a history of extra-adrenal malignancy. This study investigates the performance of three-phase computed tomography (CT) based robust federal learning algorithm and traditional deep learning for distinguishing metastases from benign adrenal lesions.

Material and methods

This retrospective analysis includes 1187 instances who underwent three-phase CT scans between January 2008 and March 2021, comprising 720 benign lesions and 467 metastases. Utilizing the three-phase CT images, both a Robust Federal Learning Signature (RFLS) and a traditional Deep Learning Signature (DLS) were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) logis

SUBMITTER: Feng B 

PROVIDER: S-EPMC10873667 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature