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Page 53 Xu et al. Art Int Surg 2023;3:48-63 https://dx.doi.org/10.20517/ais.2022.33

Hamm et al. used MRI images from 494 patients to train a CNN which can classify hepatic lesions into six
different categories (benign cysts, cavernous hemangiomas, focal nodular hyperplasia, HCC, intrahepatic
cholangiocarcinoma, and colorectal metastasis, even outperforming expert radiologists in HCC detection
[47]
(90% vs. 60%-70% sensitivity) . Preis et al. improved this study and reported that incorporating lesion data
from PET-CT into an ANN achieved high sensitivity and specificity in detecting liver cancer unidentified
[48]
visually, with an AUC of 0.905 . While such endeavors in AI models for CT, MRI and PET are laudable,
the real-world clinical utility of this is likely to be limited for a clinician as a combination of these scans
already achieves high accuracy in diagnosis. However, the human-AI algorithms, such as LiSNet
(highlighted above), that can predict biology better (microvascular invasion in this instance) would be of
important clinical utility and we highlight this below.

PROGNOSTICATION
Staging
Besides serving as efficient tools in the detection and classification of liver tumors, AI models can utilize
data for staging and prognostication. One of the key prognostic factors in HCC is vascular invasion . Jiang
[54]
et al. developed two predictive models using DL and XGBoost, a distributed gradient-boosted decision tree
[55]
ML library, to detect MVI using CT images from 405 patients, with an AUC of 0.952-0.980 . Zhang et al.
also developed a 3D-CNN model to predict MVI in HCC, with an AUC of 0.81 . Findings are summarized
[56]
in Table 2. However, in a real-world context, the prediction of MVI preoperatively in resectable or
transplantable (within criteria) HCC remains a contentious one. The rapidly expanding neoadjuvant and
peri-operative systemic treatment options in the field may result in better case selection and preoperative
treatment of patients with MVI prior to resection or transplantation.

Liver segmentation
Many developed imaging modalities such as CT, MRI, PET and US are used for the liver’s morphological
and volumetric analysis and diagnosis of associated diseases . They are useful for their capability of giving
[59]
surgeons insights into the current state of organs non-invasively. With the existence of such modalities,
computer-aided detection (CAD) systems have become significantly more important . Furthermore, CT,
[60]
MRI and PET can generate 3-dimensional (3D) holistic organ volumes for more informative image slices
with accurate anatomical information. These modalities are utilized extensively for clinical applications
including cancer diagnosis, tumor burden quantification, surgical planning and organ transplantation .
[60]
Additionally, such modalities are used for adaptive radiation therapy, which is a radiation treatment plan
that is customized based on the patient’s functional changes during a course of radiation . In another
[61]
clinical procedure, a pre-procedural CT or MRI scan can help in interventional endoscopy for pancreatic
and biliary diseases, as image guidance can be supportive in intra-procedural navigation to specific
[62]
gastrointestinal positions . All the aforementioned reasons demonstrate the importance of segmenting the
liver to aid in disease diagnosis and prognosis.

Survival prediction
Beyond detecting HCC on imaging, several studies have proposed AI algorithms for survival prediction.
Using CEUS images taken prior to treatment, Liu et al. devised a DL radiomics model to project post-
treatment progression-free survival (PFS) in HCC patients as a future selection tool between treatment
options (see section 4.4) . Zhang et al. built a DL-based model predicting overall survival using CT images
[57]
from 201 patients with unresectable HCC treated with TACE and sorafenib, which achieved superior
predictive performance compared to the clinical nomogram (C-index 0.730) .
[58]