A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

作者：Ning Mao, Yi Dai, Heng Zhou, Fan Lin, Tiantian Zheng, Ziyin Li, Ping Yang, Feng Zhao, Li Qin, Weiwei Wang, Yun Liang, Haizhu Xie, Heng Ma, Lína Zhang, Yuan Guo, Xicheng Song, Haicheng Zhang, Jie Lu · 发表于：Science Advances · 年份：2025 · DOI：10.1126/sciadv.adr1576 · 被引用次数：40 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Gene expression and cancer classification

Accurately predicting pathological complete response (pCR) before neoadjuvant chemotherapy (NAC) is crucial for patients with breast cancer. In this study, we developed a multimodal integrated fully automated pipeline system (MIFAPS) in forecasting pCR to NAC, using a multicenter and prospective dataset of 1004 patients with locally advanced breast cancer, incorporating pretreatment magnetic resonance imaging, whole slide image, and clinical risk factors. The results demonstrated that MIFAPS offered a favorable predictive performance in both the pooled external test set [area under the curve (AUC) = 0.882] and the prospective test set (AUC = 0.909). In addition, MIFAPS significantly outperformed single-modality models ( P < 0.05). Furthermore, the high deep learning scores were associated with immune-related pathways and the promotion of antitumor cells in the microenvironment during biological basis exploration. Overall, our study demonstrates a promising approach for improving the prediction of pCR to NAC in patients with breast cancer through the integration of multimodal data.