LI-RADS-aligned artificial intelligence for liver cancer diagnosis: methods, evidence, and clinical readiness.
Authors
Affiliations (8)
Affiliations (8)
- Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Jordan, Amman, 11942, Jordan.
- Faculty of Allied Medical Sciences, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan. [email protected].
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
- Department of Endocrinology, IMS and SUM Hospital, Siksha 'O' Anusandhan, Bhubaneswar, Odisha, 751003, India.
- Department of Biotechnology, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
- Department of Biotechnology, University Institute of Biotechnology, Chandigarh University, Mohali,, Punjab,, India.
- Uttaranchal Institute of Pharmaceutical Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, Uttarakhand, India.
- Department of Oral Surgery and Dental Implantology, Samarkand State Medical University, Samarkand, Uzbekistan.
Abstract
Liver tumor diagnosis relies heavily on imaging, and the liver imaging reporting and data system (LI-RADS) provides a structured framework for evaluating hepatocellular carcinoma (HCC) and related entities in at-risk populations. Artificial intelligence (AI) has rapidly expanded across CT, MRI, and ultrasound/CEUS, yet its clinical credibility depends on adherence to modality-appropriate tasks, robust validation, and alignment with LI-RADS semantics. This narrative review synthesizes methodological patterns, diagnostic performance, and readiness for clinical translation of AI systems designed for liver tumor characterization across major imaging modalities. We examine modality-task alignment-including CT-based differential diagnosis of HCC, intrahepatic cholangiocarcinoma, metastases, and benign mimics; LI-RADS feature detection and category assignment on MRI; LR-M disambiguation on MRI and CEUS; and surveillance-era triage on ultrasound. Evidence quality is appraised through external validation, reader studies, robustness analyses, calibration, and uncertainty reporting. A minimal reporting checklist is provided to support methodological transparency and facilitate reproducibility. Across modalities, AI systems show strong potential to enhance liver tumor diagnosis when they mirror radiologist reasoning, explicitly handle temporal enhancement dynamics, and incorporate clinically relevant priors. Translation into practice will require protocol-aware robustness, calibrated confidence estimates, size-stratified performance reporting, and broader multi-center validation. When developed under these principles, LI-RADS-aligned AI may meaningfully improve consistency, interpretability, and scalability in imaging-based liver cancer care.