The added value of artificial intelligence to LI-RADS categorization: A systematic review. Issue 150 (May 2022)

• Record Type: Journal Article
• Authors: Laino, Maria Elena; Viganò, Luca; Ammirabile, Angela; Lofino, Ludovica; Generali, Elena; Francone, Marco; Lleo, Ana; Saba, Luca; Savevski, Victor
• Is Part Of: European journal of radiology. Issue 150(2022)
• Publication Date: 2022-05
• Subjects: Deep learning -- Radiomics -- MRI -- CT -- Classification -- LI-RADS; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110251
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: DL and radiomics have been applied to CT and MR images with good performances. DL supports detection, segmentation, classification and follow-up of HCC lesions. Radiomics features contribute to a better characterization of HCC lesions. DL/Radiomics can support diagnosis and classification of HCC following LI-RADS. Abstract: Purpose: The objective of this systematic review was to critically assess the available literature on deep learning (DL) and radiomics applied to the Liver Imaging Reporting and Data System (LI-RADS) in terms of 1) automatic LI-RADS classification of liver nodules; 2) the contribution of DL and radiomics to human evaluation in the classification of liver nodules following LI-RADS protocol. Materials and methods: A literature search was conducted to identify original research studies published up to April 2021. The inclusion criteria were: English language, focus on computed tomography (CT) and/or magnetic resonance (MR) with specified number of patients and lesions, adoption of LI-RADS classification for the detected hepatic lesions, and application of AI in the classification of liver nodules. Review articles, conference papers, editorials and commentaries, animal studies or studies with absence of AI and/or LI-RADS were excluded. After screening 221 articles, 11 studies were included in this review. Results: All the included studies proved that DL and radiomics have high performances in liver nodules classification, sometimes similar or better than human evaluation. The best performances of DL was an AUC of 0.95 on MR and the best performance of radiomics was AUC of 0.98 either on CT and MR, while the lower ones were respectively AUC of 0.63 either on CT and MR for DL and AUC of 0.70 on CT for radiomics. Conclusion: DL and radiomics could be a useful tool in assisting radiologists in the diagnosis and classification of liver nodules according to LI-RADS.