Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Abstract

Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advanced disease at diagnosis. Recently, significant progress has been made in image recognition through advances in the field of artificial intelligence (AI) (or machine learning), especially deep learning. AI is a multidisciplinary field that draws on the fields of computer science and mathematics for developing and implementing computer algorithms capable of maximizing the predictive accuracy from static or dynamic data sources using analytic or probabilistic models. Because of the multifactorial and complex nature of liver diseases, the machine learning approach to integrate multiple factors would appear to be an advantageous approach to improve the likelihood of making a precise diagnosis and predicting the response of treatment and prognosis of liver diseases. In this review, we attempted to summarize the potential use of AI in the diagnosis and management of liver diseases, especially hepatocellular carcinoma.

[Retracted] Preliminary Evaluation of Artificial Intelligence‐Based Anti‐Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

“…Hepatocellular carcinoma has a wide range of biological variability, including from very low metastatic potential to highly aggressive phenotypes. Some unique molecular or pathological subtypes of HCC are strongly associated with good or poor prognosis [ 4 ]. The classification criteria for hepatocellular carcinoma advocated by pathologists are evolving, and pathology itself is somewhat divergent in terms of histologic grading and assessment of vascular invasion.…”

Development of novel deep multimodal representation learning‐based model for the differentiation of liver tumors on B‐mode ultrasound images

“…[25][26][27][28][29] As B-mode US itself provides structural information, an objective recognition of B-mode images using the ML approach has the potential to become a powerful tool for the qualitative diagnosis of liver tumors. 30 In some fields, computer technology performs better than humans because of its ability to manage large amounts of information and to repeat the same routines exactly time after time. 31 A previous study by Brehar et al investigated the performance in differentiating HCC from cirrhotic parenchyma using B-mode US and reported a higher performance of the DL approach as compared with that of classical ML classifiers such as gradient boosting, support vector machines, or random forest-based classifications.…”

“…Image recognition technology has also improved dramatically, and its use in the medical field is increasing rapidly 25–29 . As B‐mode US itself provides structural information, an objective recognition of B‐mode images using the ML approach has the potential to become a powerful tool for the qualitative diagnosis of liver tumors 30 . In some fields, computer technology performs better than humans because of its ability to manage large amounts of information and to repeat the same routines exactly time after time 31 .…”

A Novel Prognostic Score Based on Artificial Intelligence in Hepatocellular Carcinoma: A Long-Term Follow-Up Analysis

“…Recently, machine learning has been successful in cancer detection, prognostic risk stratification, and clinical decision-making for breast, prostate, lung, and other cancers ( 22 , 23 , 26 , 27 ). Although artificial intelligence has been applied in various imaging diagnoses and prognosis evaluations after different therapies of HCC patients, it is rarely applied to the OS of HCC patients ( 28 ). In this study, a machine learning method was used to build an ANN prediction model suitable for individual applications, which can calculate the death probability of HCC patients.…”