Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integrating histopathological imaging, genetic and clinical data. It employs vision transformers, specifically the MaxViT model, for image feature extraction, and self-attention to capture intricate image relationships at the patient level. A dual cross-attention mechanism fuses these features with genetic data, while clinical data is incorporated at the final layer to enhance predictive accuracy. Experiments on the public TCGA-BRCA dataset show that our model, trained using the negative log likelihood loss function, can achieve superior performance with a mean C-index of 0.64, surpassing existing methods. This advancement facilitates tailored treatment strategies, potentially leading to improved patient outcomes.

翻译：生存风险分层是乳腺癌临床决策中的关键步骤。我们提出了一种新颖的深度学习方法，通过整合组织病理图像、遗传数据及临床数据实现该目标。该方法采用视觉Transformer（具体为MaxViT模型）进行图像特征提取，并通过自注意力机制在患者层面捕捉复杂的图像关联。双交叉注意力机制将这些特征与遗传数据进行融合，同时在最终层引入临床数据以提升预测精度。在公开TCGA-BRCA数据集上的实验表明，使用负对数似然损失函数训练的模型实现了平均C-index为0.64的优越性能，超越了现有方法。这一进展有助于制定个性化治疗方案，有望改善患者预后。