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BaCKgRoUND aND aIMS: 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. appRoaCH aND ReSUltS: 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. CoNClUSIoNS: 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.

Background Patients affected by HCC represent a vulnerable population during the COVID-19 pandemic and may suffer from the unusual allocation of healthcare resources. The aim of this study was to determine the impact of the COVID-19 pandemic on the management of HCC patients within six French referral centers of the metropolitan area of Paris. Materials and methods We performed a multicenter, retrospective, cross-sectional study on the management of patients affected by HCC during the first six weeks of COVID-19 pandemic (exposed), compared to the same period in 2019 (unexposed). Were included all patients discussed in multidisciplinary tumor meeting (MTB) and/or undergoing radiological or surgical programmed procedure during the study period, in a curative or palliative intent. Endpoints were the number of patients with a modification in the treatment strategy, or a delay in decision-to-treatment. Results After screening, n=670 patients were included (n=293 Exposed to COVID, n=377 Unexposed to COVID). A decrease of the numbers of patients with HCC presented in MTB in 2020 (p=0.034) and with a first diagnosis of HCC (n=104 Exposed to COVID, n=143 Unexposed to COVID, p=0.083) was find. Modification in the treatment strategy was observed in 13.1% of patients, with no differences between the two periods. Nevertheless 21.5% versus 9.5% of patients experienced a treatment delay longer than 1 month in 2020 compared to 2019 (p<0.001). In 2020, 7.1% (21/293) of patients had a diagnosis of an active COVID-19 infection: 11 (52.4%) were hospitalized, and 4 (19.1%) died. Conclusions In a metropolitan area highly impacted by COVID-19 pandemic, we observed a decreased number of cases of HCC, and similar rates of modification in treatment strategy, but with a treatment delay significantly longer in 2020 versus 2019.