Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), and Ki67, exhibit distinct symptom characteristics and sensitivity to different treatment. The immunohistochemical method, one of the most common detecting tools for tumour markers, is heavily relied on artificial judgment and in clinical practice, with an inherent limitation in interpreting stability and operating efficiency. Here, a holistic intelligent breast tumour diagnosis system has been developed for tumour-markeromic analysis, combining the automatic interpretation and clinical suggestion. The holistic intelligent breast tumour diagnosis system included two main modules. The interpreting modules were constructed based on convolutional neural network, for comprehensively extracting and analyzing the multi-features of immunostaining. Referring to the clinical classification criteria, the interpreting results were encoded in a low-dimensional feature representation in the subtyping module, to efficiently output a holistic detecting result of the critical tumour-markeromic with diagnosis suggestions on molecular subtypes. The overexpression rates of HER2, ER, PR, and Ki67, as well as an effective determination of molecular subtypes were successfully obtained by this diagnosis system, with an average sensitivity of 97.6 % and an average specificity of 96.1 %, among those, the sensitivity and specificity for interpreting HER2 were up to 99.8 % and 96.9 %. The holistic intelligent breast tumour diagnosis system shows improved performance in the interpretation of immunohistochemical images over pathologist-level, which can be expected to overcome the limitations of conventional manual interpretation in efficiency, precision, and repeatability.

中文翻译：

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基于深度学习的免疫组织化学解读和分子分型整体诊断系统

不同分子亚型的乳腺癌由人表皮生长因子受体2（HER2）、雌激素受体（ER）、孕激素受体（PR）和Ki67的过表达率决定，表现出不同的症状特征和对不同治疗的敏感性。免疫组化方法是最常见的肿瘤标志物检测工具之一，在临床实践中严重依赖人工判断，在解释稳定性和操作效率方面存在固有的局限性。这里，我们开发了一个整体智能乳腺肿瘤诊断系统，用于肿瘤标记组学分析，结合自动判读和临床建议。整体智能乳腺肿瘤诊断系统包括两个主要模块。基于卷积神经网络构建解释模块，用于全面提取和分析免疫染色的多特征。参照临床分类标准，将判读结果编码为亚型模块中的低维特征表示，高效输出关键肿瘤标志组的整体检测结果以及分子亚型的诊断建议。该诊断系统成功获得了HER2、ER、PR、Ki67的过表达率以及分子亚型的有效判定，平均敏感性为97.6%，平均特异性为96.1%，其中敏感性为HER2 解读的特异性分别高达 99.8% 和 96.9%。整体智能乳腺肿瘤诊断系统在免疫组化图像判读方面的性能优于病理学家水平，有望克服传统人工判读在效率、精度和可重复性方面的局限性。