Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model’s predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
目的：乳腺癌免疫组化（IHC）定量评估的自动化对于减轻病理医生工作负担、提升诊断客观性具有关键作用。然而，由于将肿瘤区域精确分割为导管原位癌（DCIS）和浸润性癌（IC）区域的复杂性，现有方法难以实现全自动的免疫组化定量分析。此外，免疫组化定量分析需特别聚焦于浸润性癌区域。方法与结果：本研究提出一种创新方法，用于自动识别乳腺癌免疫组化全切片图像（WSI）中的浸润性癌区域。该方法利用结合多尺度形态学特征与边界特征的神经网络，无需额外H&E和P63染色切片即可实现浸润性癌区域的精确分割。此外，我们引入一种先进的半监督学习算法，能够利用未标注数据高效训练模型。为评估方法的有效性，我们构建了包含170例患者618张IHC染色WSI的数据集，涵盖四种染色类型（ER、PR、HER2和Ki-67）。值得注意的是，该模型在测试集上取得了超过80%的交并比（IoU）评分。进一步地，为验证模型在IHC定量评估中的实际应用价值，我们基于模型预测构建了全自动Ki-67评分系统。对比实验证实，该系统与资深病理医生给出的评分具有高度一致性。结论：本研究开发的模型能够准确区分乳腺癌免疫组化WSI中的DCIS与浸润性癌区域，为构建临床可用的全自动免疫组化定量评分系统奠定了基础。
肿瘤(癌症)患者之家