Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models
Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models
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DOI：
10.1371/journal.pone.0210706
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发表时间：
2019-04-17
期刊：
影响因子：
3.7
通讯作者：
Leavey, Patrick
中科院分区：
文献类型：
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作者：
Arunachalam, Harish;Mishra, Rashika;Leavey, Patrick
骨肉瘤患者化疗后肿瘤坏死的病理评估是非常重要的。这项研究报告了第一个完全自动化的工具来评估骨肉瘤中的存活和坏死肿瘤，采用组织病理学数字化和自动学习的进步。我们选择了40个数字化的全切片图像，代表了骨肉瘤和化疗反应的异质性。为了将数字化组织的不同区域标记为存活肿瘤、坏死肿瘤和非肿瘤，我们训练了13个机器学习模型，并根据报告的准确性选择了表现最好的一个（支持向量机）。我们还开发了一个深度学习架构，并在相同的数据集上进行了训练。我们计算了用于区分非肿瘤和肿瘤的接收者-操作者特征，然后有条件地区分坏死和存活肿瘤，发现我们的模型表现得非常好。然后，我们使用经过训练的模型来识别从测试整个载玻片图像生成的图像块上的感兴趣区域。分类输出可视化为肿瘤预测图，显示载玻片图像中存活和坏死肿瘤的程度。因此，我们为从原始组织学图像到肿瘤预测图生成的完整肿瘤评估管道奠定了基础。所提出的管道也可以用于其他类型的肿瘤。
Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.