Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.
Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.
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DOI：
10.5858/arpa.2022-0035-oa
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发表时间：
2023-11-01
影响因子：
4.6
通讯作者：
Hassanpour S
中科院分区：
文献类型：
--
作者：
Wu W;Liu X;Hamilton RB;Suriawinata AA;Hassanpour S
胰腺导管腺癌在各种癌症类型中具有一些最差的预后结果。胰腺肿瘤组织学类型的检测对判断预后和治疗方案的选择至关重要。这种组织学分类即使在专业病理学家之间也有很大程度的差异。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。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，我们使用一个图形架构来聚合从这些区域中提取的特征及其位置信息，以捕获整个幻灯片级结构并进行最终预测。我们在一个独立的测试集上评估了我们的模型，并在检测肿瘤细胞和导管腺癌方面达到了0.85的F1评分，显著优于其他基线方法。如果在前瞻性研究中得到验证，这种方法有很大的潜力，以帮助病理学家在临床环境中确定腺癌和其他类型的胰腺肿瘤。
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. To detect aggressive adenocarcinoma and less aggressive pancreatic tumors from nonneoplasm cases using a graph convolutional network–based deep learning model. 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. 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. 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.