Yu Yunfang, Ren Wei, Mao Luhui, Ouyang Wenhao, Hu Qiugen, Yao Qinyue, Tan Yujie, He Zifan, Ban Xiaohua, Hu Huijun, Lin Ruichong, Wang Zehua, Chen Yongjian, Wu Zhuo, Chen Kai, Ouyang Jie, Li Tang, Zhang Zebang, Liu Guoying, Chen Xiuxing, Li Zhuo, Duan Xiaohui, Wang Jin, Yao Herui
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Cancer Pathogenesis and Precision Diagnosis and Treatment, Joint Big Data Laboratory, Department of Medical Oncology, Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University, Shanwei, China; Institute for AI in Medicine and faculty of Medicine, Macau University of Science and Technology, Taipa, Macao, China; Department of Breast Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Pharmacol Res. 2025 Jun;216:107765. doi: 10.1016/j.phrs.2025.107765. Epub 2025 May 7.
Timely intervention and improved prognosis for breast cancer patients rely on early metastasis risk detection and accurate treatment predictions. This study introduces an advanced multimodal MRI and AI-driven 3D deep learning model, termed the 3D-MMR-model, designed to predict recurrence risk in non-metastatic breast cancer patients. We conducted a multicenter study involving 1199 non-metastatic breast cancer patients from four institutions in China, with comprehensive MRI and clinical data retrospectively collected. Our model employed multimodal-data fusion, utilizing contrast-enhanced T1-weighted imaging (T1 + C) and T2-weighted imaging (T2WI) volumes, processed through a modified 3D-UNet for tumor segmentation and a DenseNet121-based architecture for disease-free survival (DFS) prediction. Additionally, we performed RNA-seq analysis to delve further into the relationship between concentrated hotspots within the tumor region and the tumor microenvironment. The 3D-MR-model demonstrated superior predictive performance, with time-dependent ROC analysis yielding AUC values of 0.90, 0.89, and 0.88 for 2-, 3-, and 4-year DFS predictions, respectively, in the training cohort. External validation cohorts corroborated these findings, highlighting the model's robustness across diverse clinical settings. Integration of clinicopathological features further enhanced the model's accuracy, with a multimodal approach significantly improving risk stratification and decision-making in clinical practice. Visualization techniques provided insights into the decision-making process, correlating predictions with tumor microenvironment characteristics. In summary, the 3D-MMR-model represents a significant advancement in breast cancer prognosis, combining cutting-edge AI technology with multimodal imaging to deliver precise and clinically relevant predictions of recurrence risk. This innovative approach holds promise for enhancing patient outcomes and guiding individualized treatment plans in breast cancer care.
乳腺癌患者的及时干预和预后改善依赖于早期转移风险检测和准确的治疗预测。本研究引入了一种先进的多模态磁共振成像（MRI）和人工智能驱动的3D深度学习模型，称为3D-MMR模型，旨在预测非转移性乳腺癌患者的复发风险。我们进行了一项多中心研究，纳入了来自中国四个机构的1199例非转移性乳腺癌患者，回顾性收集了全面的MRI和临床数据。我们的模型采用多模态数据融合，利用对比增强T1加权成像（T1 + C）和T2加权成像（T2WI）容积，通过改进的3D-UNet进行肿瘤分割，并采用基于DenseNet121的架构进行无病生存期（DFS）预测。此外，我们进行了RNA测序分析，以进一步探究肿瘤区域内集中热点与肿瘤微环境之间的关系。3D-MR模型表现出卓越的预测性能，在训练队列中，时间依赖性ROC分析显示，对于2年、3年和4年DFS预测，AUC值分别为0.90、0.89和0.88。外部验证队列证实了这些发现，突出了该模型在不同临床环境中的稳健性。临床病理特征的整合进一步提高了模型的准确性，多模态方法显著改善了临床实践中的风险分层和决策制定。可视化技术为决策过程提供了见解，将预测结果与肿瘤微环境特征相关联。总之，3D-MMR模型代表了乳腺癌预后方面的重大进展，将前沿人工智能技术与多模态成像相结合，能够对复发风险进行精确且与临床相关的预测。这种创新方法有望改善患者预后，并指导乳腺癌治疗中的个体化治疗方案。
