Accurate molecular subtype classification is essential for personalized breast cancer treatment, yet conventional immunohistochemical analysis relies on invasive biopsies and is prone to sampling bias. Although dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables non-invasive tumor characterization, clinical workflows typically acquire only single-phase post-contrast images to reduce scan time and contrast agent dose. In this study, we propose a spatial multi-task learning framework for breast cancer molecular subtype prediction from clinically practical single-phase DCE-MRI. The framework simultaneously predicts estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and the Ki-67 proliferation index -- biomarkers that collectively define molecular subtypes. The architecture integrates a deep feature extraction network with multi-scale spatial attention to capture intratumoral and peritumoral characteristics, together with a region-of-interest weighting module that emphasizes the tumor core, rim, and surrounding tissue. Multi-task learning exploits biological correlations among biomarkers through shared representations with task-specific prediction branches. Experiments on a dataset of 960 cases (886 internal cases split 7:1:2 for training/validation/testing, and 74 external cases evaluated via five-fold cross-validation) demonstrate that the proposed method achieves an AUC of 0.893, 0.824, and 0.857 for ER, PR, and HER2 classification, respectively, and a mean absolute error of 8.2\% for Ki-67 regression, significantly outperforming radiomics and single-task deep learning baselines. These results indicate the feasibility of accurate, non-invasive molecular subtype prediction using standard imaging protocols.

翻译：准确的分子亚型分类对于乳腺癌个体化治疗至关重要，然而传统的免疫组织化学分析依赖于侵入性活检且易受取样偏差影响。尽管动态对比增强磁共振成像（DCE-MRI）能够实现无创肿瘤表征，但临床工作流程通常仅采集单期相对比后图像以缩短扫描时间并减少对比剂剂量。本研究提出一种空间多任务学习框架，用于从临床实用的单期相DCE-MRI预测乳腺癌分子亚型。该框架同时预测雌激素受体（ER）、孕激素受体（PR）、人表皮生长因子受体2（HER2）状态以及Ki-67增殖指数——这些生物标志物共同定义了分子亚型。该架构整合了具有多尺度空间注意力的深度特征提取网络以捕获瘤内和瘤周特征，并结合关注肿瘤核心、边缘及周围组织的感兴趣区域加权模块。多任务学习通过共享表征与任务特异性预测分支，利用生物标志物间的生物学相关性。在包含960例病例的数据集上（886例内部病例按7:1:2划分训练/验证/测试集，74例外部病例通过五折交叉验证评估）进行的实验表明，所提方法在ER、PR和HER2分类中分别达到0.893、0.824和0.857的AUC值，在Ki-67回归中取得8.2%的平均绝对误差，显著优于放射组学与单任务深度学习基线方法。这些结果表明，采用标准成像协议实现准确无创的分子亚型预测具有可行性。