Affiliated Cancer Hospital, Dalian University of Technology, Dalian, China.
School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.
Front Immunol. 2024 Feb 2;15:1334348. doi: 10.3389/fimmu.2024.1334348. eCollection 2024.
Immunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis.
In this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients' survival outcomes.
Experiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients.
To the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.
免疫组织化学（IHC）是一种广泛应用于癌症诊断的实验室技术，它选择性地将特定抗体结合到组织样本中的靶蛋白上，然后通过化学染色使结合的蛋白可见。深度学习方法有可能应用于数字化 IHC 组织学切片中肿瘤免疫微环境（TIME）的定量分析。然而，目前缺乏专门为深入的 TIME 分析而收集的公开可用的 IHC 数据集。
在本文中，基于病理学家的手动注释创建了一个显著的多色免疫组化组织学图像分类（MIHIC）数据集，该数据集可供探索深度学习模型使用，以定量分析与肺癌中的 TIME 相关的变量。MIHIC 数据集共包含 309698 个多色 IHC 染色组织学图像斑块，涵盖了七种不同的组织类型：肺泡、免疫细胞、坏死、基质、肿瘤、其他和背景。我们使用 MIHIC 数据集进行了一系列实验，利用卷积神经网络（CNNs）和变压器模型来对 IHC 染色组织学图像分类进行基准测试。最后，我们使用在组织微阵列（TMA）核心上表现最好的模型来量化肺癌免疫微环境变量，然后使用这些变量来预测患者的生存结果。
实验表明，尽管两种类型的模型在 MIHIC 测试数据集中都提供了 0.811 的最高准确率，但在组织学图像分类方面，变压器模型往往比 CNN 模型提供略好的性能。自动量化的 TIME 变量反映了免疫细胞相对于基质和肿瘤相对于组织核心的比例，这些变量对肺癌患者的总生存具有预后价值。
据我们所知，MIHIC 是第一个公开的包含 12 种不同 IHC 染色图像的肺癌 IHC 组织病理学数据集，由多位病理学家在 7 个不同类别中进行了精心注释。该数据集为研究人员探索用于定量分析 TIME 的新技术并深入了解免疫系统与肿瘤之间的相互作用提供了重要潜力。
