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the liver reserve fraction. Previous studies have shown that with an increase in the number of TACE sessions,
the ALBI grade continues to deteriorate. This study compared the changes in ALBI grade after one, two, and
three TACE treatments. Compared with single TACE, two procedures increased the degree of deterioration
by 78% [odds ratio (OR) =1.78, 95%CI: 1.11-2.85, P = 0.02], while three procedures showed a 222% increase
in risk (OR = 3.22, 95%CI: 1.96-5.29, P <​ 0.01). This result also proved that there was a significant association
between the increase in TACE frequency and the deterioration of liver function [7,41,42] .

CONSTRUCTION OF HCC PROGNOSIS PREDICTION MODEL
The 5-year OS rate of HCC patients worldwide is about 5%-30%, while the rate for Chinese patients is
approximately 12.1%, and the 5-year survival rate of advanced patients is less than 10% [43,44] . The prognosis of
patients with HCC varies greatly; therefore, early diagnosis, standardized treatment, and comprehensive
management are key to improving prognosis. Many previous studies have built prediction models to predict
prognosis by identifying characteristic indicators in the course of HCC and guiding follow-up treatment of
patients by identifying important nodes. With research in statistics, artificial intelligence, and machine
learning, new, dynamic, and individual prediction models are being explored and studied. This review aims
to synthesize and evaluate prognostic models for HCC patients by examining the following three key aspects
[Table 3].

Static model: prediction based on “starting point”
Due to different conditions and physical conditions, different patients have different responses to TACE
treatment. At the same time, TACE treatment technology itself will also bring risks to patients, such as
bleeding, side effects of chemotherapy drugs, and liver function damage. Therefore, it is important to
evaluate patients before treatment and determine whether they should undergo TACE. This type of model is
based on the basic condition of patients before treatment to carry out one-time risk stratification and predict
survival after the first TACE.

SNACOR model
The SNACOR model is an easy-to-use prediction tool constructed by Kim et al., which is based on the
baseline characteristics of HCC patients before treatment and the imaging response after treatment . The
[45]
model combines five predictors significantly related to OS: tumor size (≥ 5 cm vs. <​ 5 cm), tumor number (≥
4 vs. <​ 4), baseline alpha-fetoprotein level (≥ 400 ng/mL vs. <​ 400 ng/mL), Child-Pugh grade (B vs. A), and
objective imaging response after the first TACE (mRECIST criteria: CR/PR vs. SD/PD), and constructs a
score system of 0-10 points. Patients were divided into three groups: low-risk group (0-2 points), moderate-
risk group (3-6 points), and high-risk group (7-10 points), with median survival times of 49.8, 30.7, and 12.4
months, respectively. The SNACOR model was the first to introduce treatment response, which combines
the three elements of tumor burden, liver function, and imaging response. It provides a simple and intuitive
risk stratification tool that helps identify patients with poor prognosis early after the first TACE to adjust the
follow-up treatment strategy. The limitations of this score include the development solely on cTACE patients
without validation in other therapies [e.g., drug-eluting bead transarterial chemoembolization (DEB-
TACE)]; a high initial CR rate limiting generalizability to patients with more severe disease; and the absence
of emerging prognostic factors, such as biomarkers.

Six-and-twelve score
The original 6-and-12 model, developed by Wang et al., is an easy-to-use prognostic tool based solely on
baseline characteristics, incorporating the largest tumor diameter (cm) and tumor number as the sum (score
= size + number) . Using cut-offs of 6 and 12, patients were stratified into three risk groups: low (≤ 6),
[46]
intermediate (6-12), and high (> 12), with mOS of 49.1, 32.0, and 15.8 months, respectively. As the first