Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC. (C) 2022 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Automated summary

Hepatocellular carcinoma (HCC) is a growing malignancy with increasing incidence and mortality rates globally. Artificial intelligence (AI) provides a unique opportunity to enhance HCC risk prediction, diagnosis, and prognostication. Machine learning (ML) and deep learning (DL) models, applied to diverse data sources, have shown promising results in improving accuracy of HCC risk prediction, detection, and treatment response prediction. However, further research is needed to address challenges related to standardizing AI data and ensuring interpretability of results.