Applications of artificial intelligence in liver cancer: A scoping review.
Authors
Affiliations (7)
Affiliations (7)
- Department of Digestive Surgery, University Hospital of Nice, Nice, France; Department of Digestive Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Université Côte d'Azur, INRIA, Epione Team, Sophia Antipolis, France; Université Côte d'Azur, CNRS, UMR7370, LP2M, Nice, France. Electronic address: [email protected].
- Université Côte d'Azur, CNRS, UMR7370, LP2M, Nice, France; Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Fédération Hospitalo-Universitaire FHU Plan&Go, Nice, France.
- Department of Digestive Surgery, University Hospital of Nice, Nice, France.
- Department of Digestive Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France.
- Université Côte d'Azur, INRIA, Epione Team, Sophia Antipolis, France.
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France.
- Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France; 3IA Institute, Université Côte d'Azur, Nice, France.
Abstract
This review explores the application of Artificial Intelligence (AI) in managing primary liver cancer, focusing on recent advancements. AI, particularly machine learning (ML) and deep learning (DL), shows potential in improving screening, diagnosis, treatment planning, efficacy assessment, prognosis prediction, and follow-up-crucial elements given the high mortality of liver cancer. A systematic search was conducted in the PubMed, Scopus, Embase, and Web of Science databases, focusing on original research published until June 2024 on AI's clinical applications in liver cancer. Studies not relevant or lacking clinical evaluation were excluded. Out of 13,122 screened articles, 62 were selected for full review. The studies highlight significant improvements in detecting hepatocellular carcinoma and intrahepatic cholangiocarcinoma through AI. DL models show high sensitivity and specificity, particularly in early detection. In diagnosis, AI models using CT and MRI data improve precision in distinguishing benign from malignant lesions through multimodal data integration. Recent AI models outperform earlier non-neural network versions, though a gap remains between development and clinical implementation. Many models lack thorough clinical applicability assessments and external validation. AI integration in primary liver cancer management is promising but requires rigorous development and validation practices to enhance clinical outcomes fully.