肝细胞癌
医学
人工智能
生存分析
卷积神经网络
肿瘤科
深度学习
内科学
机器学习
放射科
比例危险模型
计算机科学
作者
Charlie Saillard,Benoît Schmauch,Oumeima Laifa,Matahi Moarii,Sylvain Toldo,Mikhail Zaslavskiy,Elodie Pronier,Alexis Laurent,Giuliana Amaddeo,Hélène Regnault,Danièle Sommacale,Marianne Ziol,Jean–Michel Pawlotsky,Sebastien Mulé,Alain Luciani,Gilles Wainrib,Thomas Clozel,Pierre Courtiol,Julien Caldéraro
出处
期刊:Hepatology
[Wiley]
日期:2020-12-01
卷期号:72 (6): 2000-2013
被引量:198
摘要
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.
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