The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model identified specific features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a slide could facilitate the diagnosis of primary liver cancers, particularly cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and external validation sets: 90, 29 and 47 samples. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, 256x256 pixel tiles were extracted from the WSIs and used to train a ResNet18. The network was used to extract new tile features. An unsupervised clustering algorithm was then applied to the new tile features. In a two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (Cluster 0: 5-97%; Cluster 1: 2-94%).

通过弱监督学习方法，在常规染色的活检病理切片上自动分类了原发性肝癌，特别是混合型肝细胞-胆管细胞癌。使用Resnet18神经网络训练了一个弱肿瘤/非肿瘤区域的标注作为标签，并利用网络的最后一个卷积层提取新的肿瘤切片特征，然后应用了一种无监督聚类算法。模型识别了肝细胞癌和肝内胆管癌的特定特征，尽管未识别到混合型肝细胞-胆管细胞癌的特定特征，但在综合诊断原发性肝癌，特别是混合型肝细胞-胆管细胞癌方面，识别出肝细胞癌和肝内胆管癌的切片可能会有帮助。

利用弱监督深度学习从常规肿瘤活检中对原发性肝癌进行分类