Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Abstract

Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.

Existing and emerging biomarkers in hepatocellular carcinoma: relevance in staging, determination of minimal residual disease, and monitoring treatment response: a narrative review

“…Therefore, advances in tissue based biomarkers will assuredly be accompanied by ongoing advances in imaging technology, and importantly, by new applications of artificial intelligence (AI) throughout all phases of HCC management. Use of AI allows for the rapid and comprehensive incorporation of information from massive datasets, that creates the potential for integration of multiple sources of information such as patient characteristics, histopathologic data, molecular profiling, and imaging features, to aid in clinical decision-making ( 118 , 119 ). Additionally, the field of radiomics (the extraction of mineable data from medical imaging) has significant potential for the development of an imaging based biomarker.…”