Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Abstract

Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.

Experimental Models of Hepatocellular Carcinoma—A Preclinical Perspective

“…The AI approach stands as an ideal strategy for HCC modeling, performing a combined evaluation of clinical, histological, and radiological data that predicts numerous outcomes such as cancer diagnosis, pathological features, treatment response, and survival rate, which, in real life, encounter difficulties due to the heterogeneous nature of the disease [179,185,186]. In a comprehensive review by Lai and colleagues, it has been brought to the spotlight that the majority of AI studies (60%) focus on HCC diagnosis [179].…”

“…AI plays a significant role in HCC therapy by offering predictions regarding the tumor response to treatment, thus allowing the accurate selection of the most suitable option [186]. By associating magnetic resonance imaging with clinical data, Abajian et al developed an ML-based framework for the pre-procedural prediction of HCC patients' therapeutic outcome after trans-arterial chemoembolization, showing 78% accuracy [188].…”

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

“…Recent technological developments and research breakthroughs have led to a wider introduction of advanced techniques for data and image analysis, aiming to surpass the limitations of current clinical practice. This is reflected by the growing number of radiomics and ML studies that have been published across medicine, and in oncology and radiology specifically, sometimes showing results that are competitive or surpass expert radiologists [ 10 , 11 , 12 , 13 , 14 , 15 ].…”

“…Despite these challenges, as highlighted in our review, the potential of AI techniques is huge in each phase of HCC management, ranging from initial diagnosis to treatment selection and prognostic and therapeutic response prediction. These tools could further the evolution towards precision and personalized medicine to support clinical practice and optimize costs and resources [ 11 , 15 , 81 , 89 ]. It should also be noted that the results from several investigations support the integration of the ML models with clinical-pathological data and established clinical scores or biomarkers.…”

"Will Artificial Intelligence Unveil Hepatocellular Carcinoma?"

“…AI is able to in the diagnostic accuracy, tumor staging, treatment planning by utilizing several types of radiological images (ultrasound, CTscan, MRI-scan, etc), WHO classifications, histopathological findings (malignant tumors non-HCC, indeterminate masses, dysplastic nodules etc.) [1]. Interestingly, the use of AI and ML techniques has also been applied on the predictivity of response both in terms of HCC recurrence after resection and after transarterial chemoembolization (TACE).…”