Xu Shaojie, Ying Yushi, Hu Qilan, Li Xingyin, Li Yulin, Xiong Hao, Chen Yanyan, Ye Qing, Li Xingrui, Liu Yue, Ai Tao, Du Yaying
Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
Cancer Imaging. 2025 Aug 29;25(1):108. doi: 10.1186/s40644-025-00929-2.
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).
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.
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 and PC72 for non-pCR. LIME provided patient-specific insights into feature contributions.
Integrating multi-dimensional MRI features with habitat imaging enhances pCR prediction in breast cancer. The fusion model offers a robust, non-invasive tool for guiding individualized treatment strategies while providing transparent interpretability through SHAP and LIME analyses.
本研究旨在开发一种整合多序列MRI影像组学、深度学习特征和瘤床成像的预测模型，以预测接受新辅助治疗（NAT）的乳腺癌患者的病理完全缓解（pCR）情况。
一项回顾性分析纳入了2018年5月至2023年1月接受NAT治疗的203例乳腺癌患者。患者被分为训练集（n = 162）和测试集（n = 41）。从多序列MRI（T2WI、DWI和DCE-MRI）数据集中的瘤内和瘤周区域提取影像组学特征。采用瘤床成像分析肿瘤亚区域，以表征肿瘤内部的异质性。我们构建并验证了机器学习模型，包括整合所有特征的融合模型，使用受试者操作特征（ROC）曲线、精确召回率（PR）曲线、决策曲线分析（DCA）和混淆矩阵。进行了Shapley值加法解释（SHAP）和局部可解释模型无关解释（LIME）分析以实现模型的可解释性。
与单区域模型相比，融合模型具有更优的预测性能，在测试集中的曲线下面积（AUC）为0.913（95%置信区间：0.770 - 1.000）。PR曲线分析显示精确召回率平衡得到改善，而DCA表明具有更高的临床获益。混淆矩阵分析证实了模型的分类准确性。SHAP显示DCE_LLL_DependenceUniformity是预测pCR的最关键特征，而PC72是预测非pCR的最关键特征。LIME提供了针对患者个体的特征贡献见解。
将多维度MRI特征与瘤床成像相结合可提高乳腺癌pCR的预测能力。融合模型提供了一种强大的非侵入性工具，用于指导个体化治疗策略，同时通过SHAP和LIME分析提供透明的可解释性。
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