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Page 49 Xu et al. Art Int Surg 2023;3:48-63 https://dx.doi.org/10.20517/ais.2022.33

phenomenon, are addressed here. The importance of AI in the future of HCC makes it important for clinicians to
have a good understanding of different AI techniques, their benefits, and potential pitfalls.

Keywords: Hepatocellular cancer, liver cancer, liver imaging, liver surgery, artificial intelligence, machine learning,
neural network

INTRODUCTION
Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide, with an
[1]
estimated incidence of 700,000 annually . While the past decade has seen paradigm shifts in the way HCC
is diagnosed and treated , prognosis remains poor (5-year overall survival stands at less than 20% ) owing
[4]
[2,3]
[5]
to multiple factors including the difficulty in identifying HCC in its early stages . Early surveillance of high-
risk patients is done via six-monthly abdominal ultrasound and serum α-fetoprotein (AFP)
[6]
measurements , but both confer limited accuracy in identifying early-stage HCC, where nodules are small
[7,8]
and indeterminate . Sensitivity is particularly low in patients with underlying cirrhosis, steatosis, or
obesity . Moreover, the success of liver resection and transplantation for HCC is primarily dependent on
[7,8]
patient selection, for which existing clinical scores rely heavily on rudimentary quantitative measures such
as the size and multicentricity of the main nodule [2,9,10] . With mounting evidence to suggest that early
diagnosis, biological stratification and treatment of HCC drastically improves survival outcomes [5,11,12] , it is
paramount that clinicians identify better tools for such purposes and rethink the way we approach
diagnostication.

In recent years, advancements in artificial intelligence (AI) capabilities have shown great potential to
redefine the way we navigate clinical care for HCC patients. AI has the capacity to improve risk prediction
[13],
in chronic hepatitis patients accelerate the diagnostic process with early identification of HCC [14-16] ,
increase accuracy in the classification of liver lesions and HCC subtypes [17-20] , tumor staging , and survival
[21]
prediction [22,23] . Decisions regarding candidate selection and optimal treatment methods may also utilize AI
in the prediction of treatment response, progression-free and overall survival [24,25] and risk of HCC
[26]
recurrence .

Broadly, AI comprises machine learning (ML), deep learning (DL), and neural networks (NN). Each differs
in terms of how the predictive model is built, the type of input data required, and the interpretability of the
model itself. ML models are primarily built with the intent of improving predictions and decision-making
accuracy. These models can be further distinguished into supervised and unsupervised learning .
[27]
Supervised learning algorithms train on sample input data with labeled outcome data, and their goal is to
learn the relationship between the input data and the outcomes to make accurate predictions about the
outcome when provided with a new set of input data . Examples of supervised learning algorithms include
[28]
traditional techniques such as linear regression and logistic regression, as well as more sophisticated
[28]
techniques including support vector machines, random forest, and gradient boosting . Unsupervised
learning algorithms train on unlabeled sample data and analyze the underlying structure or distribution
[29]
within the data to discover new clusters or patterns . Examples of unsupervised learning algorithms
[29]
include various other techniques such as K-means and principal component analysis .
Deep Learning (DL) aims to form computing systems that emulate biological neural networks. DL methods
include the use of multilayered artificial neural networks (ANNs), convolutional neural networks (CNNs),
and recurrent neural networks (RNNs) . ANNs are formed by a network of perceptrons or neurons
[30]
processed in a feed-forward fashion and are good for mapping nonlinear functions in text, tabular, or image