目前缺乏对乳腺癌肿瘤微环境中各种细胞类型密度与患者预后之间关系的综合评估。此外，缺乏带注释细胞类型的乳腺癌大型斑块级全切片成像 (WSI) 数据集阻碍了人工智能评估乳腺癌 WSI 中细胞密度的能力。我们首先在群体研究中采用 Lasso-Cox 回归建立基于细胞密度的乳腺癌预后评估模型。病理学专家手动注释了包含 70,000 多个斑块的数据集，并使用基于 ResNet152 的迁移学习来开发人工智能模型，用于识别这些斑块中的不同细胞类型。结果显示，按细胞密度评分分层的乳腺癌患者预后存在显着差异（P = 0.0018），其中细胞密度评分被确定为乳腺癌患者的独立预后因素（P < 0.05）。在验证队列中，总生存 (OS) 的预测性能令人满意，1 年曲线下面积 (AUC) 值为 0.893 (OS)，3 年为 0.823 (OS)，0.861 (OS)每 5 年一次。我们训练了一个基于 ResNet152 的稳健模型，对斑块中不同细胞类型的分类准确率达到了 99% 以上。这些成就为个性化治疗和预后评估提供了新的公共资源和工具。

A comprehensive evaluation of the relationship between the densities of various cell types in the breast cancer tumor microenvironment and patient prognosis is currently lacking. Additionally, the absence of a large patch-level whole slide imaging (WSI) dataset of breast cancer with annotated cell types hinders the ability of artificial intelligence to evaluate cell density in breast cancer WSI. We first employed Lasso-Cox regression to build a breast cancer prognosis assessment model based on cell density in a population study. Pathology experts manually annotated a dataset containing over 70,000 patches and used transfer learning based on ResNet152 to develop an artificial intelligence model for identifying different cell types in these patches. The results showed that significant prognostic differences were observed among breast cancer patients stratified by cell density score (P = 0.0018), with the cell density score identified as an independent prognostic factor for breast cancer patients (P < 0.05). In the validation cohort, the predictive performance for overall survival (OS) was satisfactory, with area under the curve (AUC) values of 0.893 (OS) at 1-year, 0.823 (OS) at 3-year, and 0.861 (OS) at 5-year intervals. We trained a robust model based on ResNet152, achieving over 99% classification accuracy for different cell types in patches. These achievements offer new public resources and tools for personalized treatment and prognosis assessment.