Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound.

ABSTRACT:

Objectives

To noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.

Methods

The study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong's test was used for performance comparison. The mod

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11424596 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature