Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

ABSTRACT:

Background

The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).

Methods

We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 9

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11190375 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature