Owkin Lab, Owkin, Paris, France.
Assistance Publique-Hôpitaux de Paris, Department of Hepatobiliary and Digestive Surgery, Henri Mondor Hospital, Créteil, France.
Hepatology. 2020 Dec;72(6):2000-2013. doi: 10.1002/hep.31207.
Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation.
In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (whole-slide imaging; WSI) to build models for predicting survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n = 194) used to develop our algorithms and an independent validation set (The Cancer Genome Atlas [TCGA], n = 328). WSIs were first divided into small squares ("tiles"), and features were extracted with a pretrained convolutional neural network (preprocessing step). The first deep-learning-based algorithm ("SCHMOWDER") uses an attention mechanism on tumoral areas annotated by a pathologist whereas the second ("CHOWDER") does not require human expertise. In the discovery set, c-indices for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75, respectively. Both models outperformed a composite score incorporating all baseline variables associated with survival. Prognostic value of the models was further validated in the TCGA data set, and, as observed in the discovery series, both models had a higher discriminatory power than a score combining all baseline variables associated with survival. Pathological review showed that the tumoral areas most predictive of poor survival were characterized by vascular spaces, the macrotrabecular architectural pattern, and a lack of immune infiltration.
This study shows that artificial intelligence can help refine the prediction of HCC prognosis. It highlights the importance of pathologist/machine interactions for the construction of deep-learning algorithms that benefit from expert knowledge and allow a biological understanding of their output.
需要有标准化和稳健的风险分层系统来对肝细胞癌（HCC）患者进行评估，以便改进治疗策略并研究辅助系统治疗在根治性切除/消融后的获益。
本研究使用了两种基于全切片数字化组织切片（全切片成像；WSI）的深度学习算法来构建用于预测接受手术切除治疗的 HCC 患者生存的模型。我们研究了两个独立的队列：一个发现队列（Henri Mondor 医院，n=194）用于开发我们的算法，另一个独立验证队列（癌症基因组图谱 [TCGA]，n=328）。WSI 首先被分成小方块（“瓦片”），并使用预训练的卷积神经网络（预处理步骤）提取特征。第一个基于深度学习的算法（“SCHMOWDER”）在病理学家注释的肿瘤区域上使用注意力机制，而第二个（“CHOWDER”）则不需要人类专业知识。在发现队列中，SCHMOWDER 和 CHOWDER 用于生存预测的 c 指数分别达到 0.78 和 0.75。这两个模型都优于包含所有与生存相关的基线变量的综合评分。模型的预后价值在 TCGA 数据集得到进一步验证，并且与发现系列中观察到的情况一样，这两个模型的判别能力均高于结合所有与生存相关的基线变量的评分。病理复查显示，预测生存不良的最具预测性的肿瘤区域的特征是血管空间、大的小梁结构模式和缺乏免疫浸润。
本研究表明，人工智能可以帮助改善 HCC 预后的预测。它强调了病理学家/机器交互对于构建深度学习算法的重要性，这些算法受益于专家知识，并允许对其输出进行生物学理解。
