Artificial intelligence and radiomics biomarkers for treatment response prediction in advanced HER2-negative breast cancer.

ABSTRACT:

Study aim

Despite the development of novel therapies, breast cancer mortality remains high. Improved treatment response assessment tools are needed. We aimed to test the transferability of externally validated AI/radiomics biomarkers for predicting treatment response in advanced, hormone receptor-positive (HR+), HER2-breast cancer treated with xentuzumab, exemestane, and everolimus.

Methods

Patient data from a phase Ib/II trial (May 2014-October 2016) were analyzed retrospectively. Eight imaging biomarkers (liver and overall tumor volume, two radiomics features representing tumor heterogeneity at both baseline and week 8) were validated for predicting clinical benefit using AUC analysis. An ancillary AI analysis developed a signature for predicting best overall response usin

SUBMITTER: Dercle L 

PROVIDER: S-EPMC12481087 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature