Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy

作者：Shaojie Xu, Yushi Ying, Qilan Hu, Xingyin Li, Yulin Li, Hao Xiong, Yanyan Chen, Qing Ye, Xingrui Li, Yue Liu, Tao Ai, Yaying Du · 发表于：Cancer Imaging · 年份：2025 · DOI：10.1186/s40644-025-00929-2 · 被引用次数：13 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies、MRI in cancer diagnosis

BACKGROUND: This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy (NAT). METHODS: A retrospective analysis included 203 breast cancer patients treated with NAT from May 2018 to January 2023. Patients were divided into training (n = 162) and test (n = 41) sets. Radiomics features were extracted from intratumoral and peritumoral regions in multi-sequence MRI (T2WI, DWI, and DCE-MRI) datasets. Habitat imaging was employed to analyze tumor subregions, characterizing heterogeneity within the tumor. We constructed and validated machine learning models, including a fusion model integrating all features, using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, decision curve analysis (DCA), and confusion matrices. Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses were performed for model interpretability. RESULTS: The fusion model achieved superior predictive performance compared to single-region models, with AUCs of 0.913 (95% CI: 0.770-1.000) in the test set. PR curve analysis showed improved precision-recall balance, while DCA indicated higher clinical benefit. Confusion matrix analysis confirmed the model's classification accuracy. SHAP revealed DCE_LLL_DependenceUniformity as the most critical feature for predicting pCR ...