增殖肿瘤标记网络 (PTM-NET) 用于识别 Ki67 染色的乳腺癌全切片图像中的肿瘤区域。,Scientific Reports

当前位置： X-MOL 学术 › Sci. Rep. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

增殖肿瘤标记网络 (PTM-NET) 用于识别 Ki67 染色的乳腺癌全切片图像中的肿瘤区域。
Scientific Reports ( IF 3.8 ) Pub Date : 2019-09-06 , DOI: 10.1038/s41598-019-49139-4
Jesuchristopher Joseph ₁ , Martine P Roudier ₁ , Priya Lakshmi Narayanan ₂ , Renaldas Augulis ₃ , Vidalba Rocher Ros ₁ , Alison Pritchard ₁ , Joe Gerrard ₁ , Arvydas Laurinavicius ₃ , Elizabeth A Harrington ₁ , J Carl Barrett ₁ , William J Howat ₁

Affiliation

不受控制的增殖是癌症的一个标志，可以通过使用免疫组织化学标记乳腺组织来评估 Ki67（一种与细胞增殖相关的蛋白质）。Ki67 阳性肿瘤细胞核的准确测量至关重要，但需要病理学家对肿瘤区域进行注释。这种手动注释过程非常主观、耗时，并且受到注释者间和注释者内部经验的影响。为了应对这一挑战，我们开发了增殖肿瘤标记网络（PTM-NET），这是一种深度学习模型，可使用卷积神经网络客观地注释 Ki67 标记的乳腺癌数字病理图像中的肿瘤区域。我们定制设计的深度学习模型在 45 张免疫组织化学 Ki67 标记的完整幻灯片图像上进行训练，以对肿瘤和非肿瘤区域进行分类，并在来自两个不同来源的 45 张完整幻灯片图像上进行验证，这些图像使用不同的协议进行染色。我们的结果显示，相对于组合数据集的手动地面实况注释，Dice 系数为 0.74，阳性预测值为 70%，阴性预测值为 88.3%。不同来源的图像之间存在微小差异，并且在雌激素受体和孕激素受体标记的图像中进一步测试该模型。最后，利用模型的扩展，我们可以识别肿瘤内可能的高增殖热点区域。将来，这种方法可能有助于识别活检样本和组织微阵列图像中的肿瘤区域。

"点击查看英文标题和摘要"

Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images.

Uncontrolled proliferation is a hallmark of cancer and can be assessed by labelling breast tissue using immunohistochemistry for Ki67, a protein associated with cell proliferation. Accurate measurement of Ki67-positive tumour nuclei is of critical importance, but requires annotation of the tumour regions by a pathologist. This manual annotation process is highly subjective, time-consuming and subject to inter- and intra-annotator experience. To address this challenge, we have developed Proliferation Tumour Marker Network (PTM-NET), a deep learning model that objectively annotates the tumour regions in Ki67-labelled breast cancer digital pathology images using a convolution neural network. Our custom designed deep learning model was trained on 45 immunohistochemical Ki67-labelled whole slide images to classify tumour and non-tumour regions and was validated on 45 whole slide images from two different sources that were stained using different protocols. Our results show a Dice coefficient of 0.74, positive predictive value of 70% and negative predictive value of 88.3% against the manual ground truth annotation for the combined dataset. There were minimal differences between the images from different sources and the model was further tested in oestrogen receptor and progesterone receptor-labelled images. Finally, using an extension of the model, we could identify possible hotspot regions of high proliferation within the tumour. In the future, this approach could be useful in identifying tumour regions in biopsy samples and tissue microarray images.

更新日期：2019-09-06

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南