Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.

ABSTRACT:

Background

Several studies have confirmed the potential value of applying radiomics to predict prognosis of breast cancer. However, the tumor segmentation in these studies depended on delineation or annotation of breast cancer by radiologist, which is often laborious, tedious, and vulnerable to inter- and intra-observer variability. Automatic segmentation is expected to overcome this difficulty. Herein, we aim to investigate the value of automatic segmentation-based multi-modal radiomics signature and magnetic resonance imaging (MRI) features in predicting disease-free survival (DFS) of patients diagnosed with invasive breast cancer.

Methods

This retrospective multicenter study included a total of 643 female patients with invasive breast cancer who underwent preoperative ult

SUBMITTER: Xiong L 

PROVIDER: S-EPMC11555850 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature