Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study

作者：Wenjie Tang, Jin Chen, Qingcong Kong, Chunling Liu, Si–Yi Chen, Shishen Ding, Bihua Liu, Zaihui Feng, Ying Li, Yi Dai, Lei Zhang, Yongxin Chen, Xiaorui Han, Shuang Liu, Dandan Chen, Zijin Weng, Weifeng Liu, Xinhua Wei, Xinqing Jiang, Qianwei Zhou, Ning Mao, Yuan Guo · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103298 · 被引用次数：8 · 研究领域：MRI in cancer diagnosis、Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies

Background: The accurate and early evaluation of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for optimizing treatment strategies and minimizing unnecessary interventions. While deep learning (DL)-based approaches have shown promise in medical imaging analysis, existing models often fail to comprehensively integrate spatial and temporal tumor dynamics. This study aims to develop and validate a spatiotemporal interaction (STI) model based on longitudinal MRI data to predict pathological complete response (pCR) to NAC in breast cancer patients. Methods: This study included retrospective and prospective datasets from five medical centers in China, collected from June 2018 to December 2024. These datasets were assigned to the primary cohort (including training and internal validation sets), external validation cohorts, and a prospective validation cohort. DCE-MRI scans from both pre-NAC (T0) and early-NAC (T1) stages were collected for each patient, along with surgical pathology results. A Siamese network-based STI model was developed, integrating spatial features from tumor segmentation with temporal dependencies using a transformer-based multi-head attention mechanism. This model was designed to simultaneously capture spatial heterogeneity and temporal dynamics, enabling accurate prediction of NAC response. The STI model's performance was evaluated using the area under the ROC curve (AUC) and Precision-Recall curve (AP), accuracy, sensitivity, and spec...