Affiliation:
1. First Department of Surgery National and Kapodistrian University of Athens, Laikon General Hospital Athens Greece
2. Laboratory of Medical Physics, Medical School National and Kapodistrian University of Athens Athens Greece
3. Second Department of Propaedeutic Surgery National and Kapodistrian University of Athens, Laikon General Hospital Athens Greece
4. Second Department of Radiology, National and Kapodistrian University of Athens Attikon University Hospital Athens Greece
Abstract
AbstractBackground and AimHepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis.MethodsA comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms “Artificial Intelligence,” “Liver Cancer,” “Hepatocellular Carcinoma,” “Machine Learning,” and “Deep Learning” were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information.ResultsOur search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B‐mode US and a contrast‐enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI‐based model achieved HCC diagnosis with an AUC of 0.970.ConclusionsAI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC‐related diagnostic tasks.