Background/Objectives: Breast cancer (BC) is the most commonly diagnosed cancer in women and the leading cause of cancer-related deaths globally. Molecular subtyping is crucial for prognosis and treatment planning, with immunohistochemistry (IHC) being the most commonly used method. However, IHC has limitations, including observer variability, a lack of standardization, and a lack of reproducibility. Gene expression profiling is considered the ground truth for molecular subtyping; unfortunately, this is expensive and inaccessible to many institutions. This study investigates the potential of an artificial intelligence (AI) model to predict BC molecular subtypes directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs).Methods: A pretrained deep learning framework based on multiple-instance learning (MIL) was validated on the Stavanger Breast Cancer (SBC) dataset, consisting of 538 BC cases. Three classification tasks were assessed, including two-class [triple negative BC (TNBC) vs. non-TNBC], three-class (luminal vs. HER2-positive vs. TNBC), and four-class (luminal A vs. luminal B vs. HER2-positive vs. TNBC) groups. Performance metrics were used for the evaluation of the AI model.Results: The AI model demonstrated strong performance in distinguishing TNBC from non-TNBC (AUC = 0.823, accuracy = 0.833, F1-score = 0.824). However, performance declined with an increasing number of classes.Conclusions: The study highlights the potential of AI in BC molecular subtyping from H&E WSIs, offering an easily applicable and standardized method to IHC. Future improvements should focus on optimizing multi-class classification and validating AI-based methods against gene expression analyses for enhanced clinical applicability.
背景/目的：乳腺癌是全球女性最常见的恶性肿瘤，也是癌症相关死亡的主要原因。分子分型对预后评估和治疗方案制定至关重要，其中免疫组织化学是最常用的检测手段。然而该方法存在观察者差异性、缺乏标准化及可重复性不足等局限性。基因表达谱分析被视为分子分型的金标准，但成本高昂且难以在多数医疗机构普及。本研究旨在探讨人工智能模型直接通过苏木精-伊红染色全切片图像预测乳腺癌分子亚型的可行性。 方法：基于多示例学习框架的预训练深度学习模型在斯塔万格乳腺癌数据集（含538例病例）上进行验证。评估任务包括二分类[三阴性乳腺癌与非三阴性乳腺癌]、三分类[管腔型/人表皮生长因子受体2阳性型/三阴性型]及四分类[管腔A型/管腔B型/人表皮生长因子受体2阳性型/三阴性型]。采用多种性能指标对模型进行评估。 结果：该人工智能模型在区分三阴性乳腺癌与非三阴性乳腺癌任务中表现优异（受试者工作特征曲线下面积=0.823，准确率=0.833，F1分数=0.824）。但随着分类类别增加，模型性能呈现下降趋势。 结论：本研究证实了人工智能基于苏木精-伊红染色全切片图像进行乳腺癌分子分型的潜力，为免疫组织化学检测提供了更易实施且标准化的替代方案。未来改进应聚焦于优化多类别分类性能，并通过与基因表达谱分析对比验证来提升人工智能方法的临床适用性。
肿瘤(癌症)患者之家