Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

ABSTRACT:

Objective

This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.

Methods and materials

All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort (n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound

SUBMITTER: Wenwen 

PROVIDER: S-EPMC11837701 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature