乳腺癌亚型分型是确定治疗选择的关键步骤，但基于免疫组织化学染色的分子检查费用昂贵且耗时。深度学习为基于血红素和噪声染色的形态学信息预测亚型提供了可能，这是一种更便宜、更快速的选择。然而，传统的预测模型训练需要大量组织学图像，这对于单个机构的收集是具有挑战性的。我们旨在开发一种数据高效的计算病理学平台——3DHistoNet，它可以通过学习z堆叠的组织学图像精确地预测乳腺癌亚型，只需小样本量。 我们回顾性地研究了韩国国家癌症中心病理科 2018 年至 2020 年间确诊的 401 例原发性乳腺癌患者病例。根据标准方案制备了乳腺癌患者的病理切片。通过查阅病历和病理记录评估了年龄、性别、组织学分级、激素受体（雌激素受体 [ER]、孕激素受体 [PR] 和雄激素受体 [AR]）状态、erb-B2 受体酪氨酸激酶 2（HER2）状态和 Ki-67 指数。 我们使用 5 折交叉验证对 3DHistoNet 平台预测 ER、PR、AR、HER2 和 Ki67 亚型生物标志物的表现进行了接收器操作特性曲线下面积和决策曲线分析。我们证明了 3DHistoNet 可以以显著优势（接收器操作特性曲线下面积：0.75-0.91 对 0.67-0.8）预测所有临床重要的生物标志物（ER、PR、AR、HER2 和 Ki67），超过了传统的多实例学习模型。我们进一步展示了我们的 z 堆叠组织学扫描方法可以在没有额外费用的情况下弥补不足的训练数据集。最后，3DHistoNet 提供了生成关注图的额外功能，可显示 Ki67 和组织形态学特征之间的相关性，从而使血红素和噪声图像在高保真度下呈现给病理学家。 我们独立、数据高效的病理学平台可以生成 z 堆叠图像并预测关键生物标志物，是一种吸引人的乳腺癌诊断工具。它的发展将鼓励基于形态学的诊断，相比基于免疫组织化学染色的蛋白质定量方法更快速、更便宜且误差更小。 ©Kideog Bae, Young Seok Jeon, Yul Hwangbo, Chong Woo Yoo, Nayoung Han, Mengling Feng. 最初发表于 JMIR Cancer（https://cancer.jmir.org），05.09.2023。

Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute.We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size.We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records.The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist.Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.©Kideog Bae, Young Seok Jeon, Yul Hwangbo, Chong Woo Yoo, Nayoung Han, Mengling Feng. Originally published in JMIR Cancer (https://cancer.jmir.org), 05.09.2023.