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Xu et al. Art Int Surg 2023;3:48-63 https://dx.doi.org/10.20517/ais.2022.33 Page 50

data [31,32] . CNNs have connective patterns resembling the visual cortex and can detect inherent spatial
[33]
features of high-dimensional images . RNNs have connections forming a graph over a temporal sequence,
[34]
thus being useful in time series prediction . In DL models, a significant “black box problem” remains as
[35]
the programs have low interpretability and users may not completely understand how they work .
In this narrative review, we will outline the frontiers of AI research in the diagnosis, prognostication, and
treatment of HCC.

DIAGNOSIS OF HCC
There have been remarkable advances in the application of AI to aid traditional diagnostic techniques for
HCC in recent years. This is primarily due to the use of DL algorithms using CNN, which is a multilayer
ANN interconnected such that all input data is processed through multiple layers to produce valuable
output data . CNN algorithms trained on various imaging modalities such as ultrasound (US), computed
[36]
tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) have been
shown to increase the diagnostic yield in terms of identification, classification, staging and survival
[37]
prediction in HCC . All findings are summarized in Table 1.
Prediction of cirrhosis and HCC development
[49]
HCC often occurs on a backdrop of longstanding cirrhosis , yet cirrhotic changes can remain elusive until
[50]
its later stages . Standard radiological features on imaging include a nodular hepatic contour, changes in
volume distribution with enlargement of the caudate lobe and the left lateral segment, atrophy of the right
and left lobe medial segments, widening of the fissures and the porta hepatis, and the formation of
regenerative nodules . In response to this problem, Liu et al. designed an algorithm to determine the
[50]
presence or absence of cirrhosis in US images with an area under the curve (AUC) of 0.968 . Using their
[38]
analysis of liver capsule morphology, the DL program could identify early cirrhotic changes often invisible
to the human eye. Expanding on this, a novel ML model by Ksiazek et al. forecasted the risk of HCC
development based on 23 quantitative and 26 qualitative features gleaned from biochemistry, and clinical
factors like viral status and comorbidities, ultimately achieving 88.5% accuracy . Such predictive models,
[39]
when coupled with other noninvasive methods in predicting fibrosis and cirrhosis, are likely to be
developed further and be seen routinely in clinical practice in early disease detection.

Radiological identification
Ultrasound
Current clinical guidelines recommend regular abdominal US surveillance for the identification of HCC in
high-risk patients with chronic viral hepatitis or cirrhosis . US is, therefore, usually the primary tool to
[51]
evaluate early liver disease and detect new lesions. However, image interpretation is subject to limitations
such as inter-observer variability and patient body habitus, resulting in a sensitivity of only 63% . For
[51]
example, liver neoplasms can be difficult to distinguish from liver parenchyma, particularly with small
[53]
indeterminate lesions or diffuse HCC in the setting of cirrhosis . To address this, several studies have
[52]
proposed AI algorithms with data from various imaging modalities to improve the diagnostic accuracy of
HCC.

To delineate HCC from background cirrhosis, Bharti et al. devised an ANN to classify US images into four
stages of liver disease (normal liver, chronic liver disease, cirrhosis, and HCC) with an accuracy of 96.6% .
[14]
More recently, Brehar et al. also proposed a CNN model built on two independent datasets of US images
that outperformed conventional ML methods (SVM, RF, multilayer perceptron, and AdaBoost) .
[40]