Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.

IF 3.7 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Weiyi Wu, Xiaoying Liu, Robert B Hamilton, Arief A Suriawinata, Saeed Hassanpour
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引用次数: 0

Abstract

Context.—: Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists.

Objective.—: To detect aggressive adenocarcinoma and less aggressive pancreatic tumors from nonneoplasm cases using a graph convolutional network-based deep learning model.

Design.—: Our model uses a convolutional neural network to extract detailed information from every small region in a whole slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole slide-level structure and make the final prediction.

Results.—: We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods.

Conclusions.—: If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.

图形卷积神经网络在癌症组织学分类中的应用。
上下文。--:在各种癌症类型中,胰腺导管腺癌的预后最差。胰腺肿瘤组织学模式的检测对于预测预后和决定患者的治疗至关重要。即使在专业病理学家中,这种组织学分类也可能有很大程度的可变性。目标。--:使用基于图卷积网络的深度学习模型从非肿瘤病例中检测侵袭性腺癌和侵袭性较低的胰腺肿瘤。设计。--:我们的模型使用卷积神经网络从整个幻灯片图像中的每个小区域提取详细信息。然后,我们使用图架构来聚合从这些区域提取的特征及其位置信息,以捕获整个幻灯片级别的结构并进行最终预测。结果。--:我们在一个独立的测试集上评估了我们的模型,在检测肿瘤细胞和导管腺癌方面获得了0.85的F1分数,显著优于其他基线方法。结论。--:如果在前瞻性研究中得到验证,这种方法有很大的潜力帮助病理学家在临床环境中识别腺癌和其他类型的胰腺肿瘤。
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来源期刊
CiteScore
9.20
自引率
2.20%
发文量
369
审稿时长
3-8 weeks
期刊介绍: Welcome to the website of the Archives of Pathology & Laboratory Medicine (APLM). This monthly, peer-reviewed journal of the College of American Pathologists offers global reach and highest measured readership among pathology journals. Published since 1926, ARCHIVES was voted in 2009 the only pathology journal among the top 100 most influential journals of the past 100 years by the BioMedical and Life Sciences Division of the Special Libraries Association. Online access to the full-text and PDF files of APLM articles is free.
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