发表机构
University of Oviedo; Rabindra University; University of A Coruña; The University of Texas at Austin（奥维耶多大学; 拉宾德拉大学; 拉科鲁尼亚大学; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于DW-MRI和EfficientNet时间模型的深度学习框架，仅用基线和第一周期后影像早期预测乳腺癌pCR，AUC达0.90，支持个性化治疗调整。
AI 中文摘要
早期识别对新辅助化疗（NACT）无反应的患者对于乳腺癌的及时治疗调整至关重要。然而，许多现有的预测模型依赖于多参数磁共振成像（MRI）、较晚的治疗时间点或大量的临床数据，这限制了它们的适用性。本研究提出了一种深度学习框架，仅使用在基线和第一个NACT周期后获取的扩散加权MRI（DW-MRI）来早期预测病理完全缓解（pCR）。该框架将裁剪的肿瘤中心斑块输入到基于EfficientNet的时间模型中，该模型直接学习肿瘤形状和局部组织特征，无需显式的影像组学特征工程。该模型通过10折交叉验证进行训练，在一个周期后实现了0.90的pCR预测受试者工作特征曲线下面积（AUC），在单个治疗周期后提供了可操作的信息，同时避免了钆剂给药并减少了对异质性临床数据的依赖。通过关注基线到第一周期的窗口而非后期阶段，该方法支持更早的NACT升级或降级，并且其仅依赖DW-MRI有助于协议标准化、多中心部署和隐私保护的数据共享。这些结果表明，基于DW-MRI的肿瘤中心斑块深度学习构成了一种微创、临床可部署的早期pCR预测策略，对乳腺癌新辅助治疗中的个性化治疗调整具有直接影响。
英文摘要
Early identification of non-responders to neoadjuvant chemotherapy (NACT) is crucial for timely treatment adaptation in breast cancer. However, many existing predictive models rely on multiparametric magnetic resonance imaging (MRI), late treatment time points, or extensive clinical data, which limits their applicability. This study proposes a deep learning framework for early prediction of pathological complete response (pCR) using only diffusion-weighted MRI (DW-MRI) acquired at baseline and after the first NACT cycle. This framework feeds cropped tumor-centered patches to an EfficientNet-based temporal model that directly learns tumor shape and local tissue characteristics without explicit radiomic feature engineering. The model, trained with 10-fold cross-validation, achieved an area under the receiver operating characteristic curve (AUC) of 0.90 for pCR prediction after one cycle, providing actionable information after a single treatment cycle while avoiding gadolinium administration and reducing dependence on heterogeneous clinical data. By focusing on the baseline-to-first-cycle window instead of later stages, the approach supports earlier escalation or de-escalation of NACT, and its exclusive reliance on DW-MRI facilitates protocol standardization, multi-centre deployment and privacy-preserving data sharing. These results demonstrate that DW-MRI-based deep learning on tumor-centered patches constitutes a minimally invasive, clinically deployable strategy for early pCR prediction, with direct implications for personalized treatment adaptation in neoadjuvant breast cancer therapy.
