Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin
Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin
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DOI：
10.1117/1.jmi.7.1.012706
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发表时间：
2020-01-01
影响因子：
2.4
通讯作者：
Chang, Young H.
中科院分区：
文献类型：
--
作者：
Schau, Geoffrey F.;Burlingame, Erik A.;Chang, Young H.
目的：病理学家依靠相关的临床信息、染色组织切片形态的肉眼观察和先进的分子诊断学来准确地推断继发性转移癌的生物学来源。虽然这个过程非常有效，但就时间和临床资源而言，它是昂贵的。我们试图开发和评估一个计算机视觉系统，该系统设计用于直接从肝脏活检的数字化组织病理学完整幻灯片图像中合理地推断继发性肝癌的转移来源。方法：我们说明了一个两阶段深度学习方法来完成这项任务。我们首先训练一个模型，根据病理学家对几张完整的幻灯片图像的注释，在数字化的苏木精-伊红(H&E)染色的继发性肝癌组织切片中识别肿瘤的空间定位区域。然后，第二个模型被训练来生成由免疫组织化学确认的属于三个不同临床相关类别之一的癌症转移来源的预测。结果：我们的方法在从坚持的测试集中确定整个幻灯片图像的转移来源方面获得了90.2%的分类准确率，与三个委员会认证的病理学家建立的临床基准相比是有利的，他们在相同的预测任务上的准确率从90.2%到94.1%。结论：我们阐明了深度学习系统的潜在影响，利用H&E染色组织切片的形态和结构特征来指导病理和临床确定继发性肝癌的转移来源。(C)2020年光学仪器工程师学会(SPIE)
Purpose: Pathologists rely on relevant clinical information, visual inspection of stained tissue slide morphology, and sophisticated molecular diagnostics to accurately infer the biological origin of secondary metastatic cancer. While highly effective, this process is expensive in terms of time and clinical resources. We seek to develop and evaluate a computer vision system designed to reasonably infer metastatic origin of secondary liver cancer directly from digitized histopathological whole slide images of liver biopsy.Approach: We illustrate a two-stage deep learning approach to accomplish this task. We first train a model to identify spatially localized regions of cancerous tumor within digitized hematoxylin and eosin (H&E)-stained tissue sections of secondary liver cancer based on a pathologist's annotation of several whole slide images. Then, a second model is trained to generate predictions of the cancers' metastatic origin belonging to one of three distinct clinically relevant classes as confirmed by immunohistochemistry.Results: Our approach achieves a classification accuracy of 90.2% in determining metastatic origin of whole slide images from a held-out test set, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task.Conclusions: We illustrate the potential impact of deep learning systems to leverage morphological and structural features of H&E-stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)