Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images.
Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images.
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DOI：
10.4103/jpi.jpi_10_20
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发表时间：
2020
影响因子：
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通讯作者：
Sethi A
中科院分区：
文献类型：
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作者：
Anand D;Kurian NC;Dhage S;Kumar N;Rane S;Gann PH;Sethi A
使用免疫组织化学（IHC）经济地检测癌症中的几种治疗上重要的突变，其突出了与突变相关的特异性抗原的过表达。然而，在低收入环境中，IHC面板可能不精确且相对昂贵。另一方面，尽管用于可视化一般组织形态的苏木精和伊红（H&E）染色是常规且低成本的，但其不突出任何特异性抗原或突变。以乳腺癌中的人表皮生长因子受体2（HER 2）突变为例，我们加强了H& E染色组织中HER 2蛋白过表达的成本效益检测和筛选的案例。我们使用的计算方法可以直接从H&E图像中可靠地检测与突变特异性蛋白过度表达相关的细微形态变化。我们训练了分类管道以确定H&E染色的整个载玻片图像的HER 2过表达状态。我们的训练数据集来自一家医院，包含26例（11例HER 2+和15例HER 2-）病例。我们对来自同一医院的26例（8例HER 2+和18例HER 2-）保留病例和来自TCGA-BRCA队列的45例独立病例（23例HER 2+和22例HER 2-）进行了分类管道测试。该流水线由一个染色分离模块和三个串联的深度神经网络模块组成，以实现鲁棒性和可解释性。我们通过曲线下面积（AUC）-受试者操作特征来评估我们的训练模型。我们的管道在保留病例上实现了AUC 0.82（置信区间[CI]：0.65-0.98），在TCGA的独立数据集上实现了AUC 0.76（CI：0.61-0.89）。我们还证明了区域水平的对应关系，HER 2过表达之间的患者的IHC和H&E连续切片。我们的工作加强了自动量化H& E染色数字病理学中突变特异性蛋白质过表达的情况，并强调了多阶段机器学习管道对于增加鲁棒性和可解释性的重要性。
Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation. Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue. We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images. We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability. We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic. Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0.65–0.98) on held-out cases and an AUC of 0.76 (CI: 0.61–0.89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections. Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.