Multi-algorithm radiomics machine learning models integrating ultrasound imaging and inflammation-immune features for hepatic metastases identification.

ABSTRACT:

Background

Hepatic metastases (HM) and primary liver malignant tumor (PLMC) share partially similar pathological foundations, which can make it difficult to differentiate them based on visual imaging findings. This study will explore the value of multi-algorithm radiomics machine learning models that integrate ultrasound imaging and inflammation-immune features in the identification of HM.

Methods

Patients with hepatic malignancies who had undergone ultrasound-guided biopsy and image acquisition were retrospectively included. A total of 104 patients were randomly divided into training and internal validation cohorts at a 6:4 ratio, while other 45 patients were assigned as the external validation cohort. The PyRadiomics package was utilized to extract 107 radiomics original f

SUBMITTER: Wu L 

PROVIDER: S-EPMC12581483 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature