Deep learning model based on DCE-MRI: fusion of 3D features of tumor, peritumoral vessels and metastatic lymph nodes for prediction of pathological complete response to neoadjuvant therapy in breast cancer

作者：Yue Du, Yue Zhu, Mengting Yang, Aoqi Zhang, Lei Zhang, Zhihuai Zhou · 发表于：Frontiers in Oncology · 年份：2025 · DOI：10.3389/fonc.2025.1664631 · 被引用次数：3 · 研究领域：MRI in cancer diagnosis、Breast Cancer Treatment Studies、Radiomics and Machine Learning in Medical Imaging

Objective: The aim of this study is to develop a deep learning-based radiomic (DLR) model by fusing 3D features of tumor, peritumoral vessels, and metastatic lymph nodes from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with the goal of predicting pathological complete response (pCR) in breast cancer patients receiving neoadjuvant therapy. Materials and methods: A total of 200 breast cancer (BC) cases were retrospectively collected from the First and Second Affiliated Hospitals of Bengbu Medical University between January 2020 and December 2024. The cases were randomly allocated to a training set and a test set at a 1:1 ratio. For dynamic contrast-enhanced MRI (DCE-MRI) sequence imaging, 3D UNet technology was utilized to facilitate layer-by-layer semi-automated segmentation of tumors, peritumoral vessels, and metastatic lymph nodes. Concurrently, we used deep learning methods to extract features and constructed a predictive model for pCR status in breast cancer patients after NAT. The Clinical Combined Deep Learning Radiomic (CCDLR) model was developed by integrating clinical characteristics into the DLR model. The performance of the CCDLR and DLR models was compared and validated in a test set. Results: The training set contained 45 cases in the pCR group and 55 cases in the non-pCR group, while the test set contained 47 cases in the pCR group and 53 cases in the non-pCR group. The efficacy of the CCDLR model in predicting the NAT pCR of breast cancer was...