Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.

ABSTRACT:

Background

Proper stratification of recurrence risk in breast cancer is crucial for guiding treatment decisions. This study aims to predict the recurrence risk of breast cancer patients using a multimodal deep learning model that integrates multiple sequence MRI imaging features with clinicopathologic characteristics.

Methods

In this retrospective study, we enrolled 574 patients with non-metastatic invasive breast cancer from two Chinese institutions between September 2012 and July 2019. We developed a multimodal deep learning (MDL) model by constructing a multi-instance learning framework based on convolutional neural networks. We integrated imaging features from T2WI, DWI, and DCE-MRI sequences with clinicopathologic features for breast cancer recurrence risk stratificatio

SUBMITTER: Zhang R 

PROVIDER: S-EPMC12522914 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature