Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.
Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.
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DOI：
10.1038/srep46450
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发表时间：
2017-04-18
影响因子：
4.6
通讯作者：
Madabhushi A
中科院分区：
文献类型：
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作者：
Cruz-Roa A;Gilmore H;Basavanhally A;Feldman M;Ganesan S;Shih NNC;Tomaszewski J;González FA;Madabhushi A
随着用载玻片扫描仪常规地和快速地扫描整个载玻片图像的能力的增加，人们对开发用于从数字病理学图像自动检测疾病程度的计算机化图像分析算法感兴趣。由病理学家手动识别乳腺癌的存在和程度对于肿瘤分期和评估治疗反应的患者管理至关重要。然而，该过程是繁琐的并且受到阅读器间和阅读器内可变性的影响。为了使计算机化方法作为决策支持工具有用，它们需要对从不同来源、不同染色和切割方案以及不同扫描仪获得的数据具有弹性。本研究的目的是评估基于深度学习的方法在数字化图像上自动识别浸润性肿瘤范围的准确性和鲁棒性。在这里，我们提出了一种新的方法，采用卷积神经网络检测整个载玻片图像上是否存在侵袭性肿瘤。我们的方法包括在来自多个不同站点和扫描仪的近400个样本上训练分类器，然后在来自癌症基因组图谱的近200个病例上进行独立验证。我们的方法产生了75.86%的骰子系数，阳性预测值为71.62%，阴性预测值为96.77%，在逐像素评估相比，手动注释的浸润性导管癌的区域。
With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.