Integrating deep learning features from mammography with SHAP values for a machine learning model predicting over 5-year recurrence of breast ductal carcinoma <i>In Situ</i> post-lumpectomy.

ABSTRACT:

Background

In women with ductal carcinoma in situ (DCIS) undergoing breast-conserving surgery, still part will progress to invasive breast cancer (IBC) in the future. Mammograms offer rich tumor data for patient stratification, but current prediction methods focus on clinicopathological factors, overlooking imaging insights.

Methods

We retrospectively analyzed 140 DCIS patients from Harbin Medical University Cancer Hospital (2011-2020, followed up to 2025). Preoperative digital mammograms and clinicopathological data were collected, with mammographic features extracted using pyradiomics and supervised by a senior radiologist. Feature selection employed 10-fold cross-validated LASSO regression. The dataset was split into training (n=100) and validation (n=40) sets (10:

SUBMITTER: Sha Y 

PROVIDER: S-EPMC12477132 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature