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Page 2 of 22 Ma et al. Hepatoma Res. 2026;12:43

Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu, China. E-mail: zhuhaidong@seu.edu.cn; Dr. Yue-Yong Li,
Department of Oncology, Changsha Central Hospital, University of South China, Changsha 410004, Hunan, China. E-mail:
305017674@qq.com; Dr. Guo-Jun Li, Department of Interventional Minimally Invasive Surgery, Xuzhou Central Hospital, Southeast
University, Xuzhou 221000, Jiangsu, China. E-mail: lyg0027014@163.com

characteristics to a dynamic model that integrates treatment response. Frontier research has further explored
prognostic prediction models based on genomics, radiomics, and machine learning, providing a direction for
achieving higher accuracy of individualized prognostic prediction. This narrative review aims to synthesize the
current evidence, explore the relationship between the number of TACE treatments and the prognosis of HCC
patients, compare TACE alone versus TACE combined with systemic therapy, and review the methodological
progress of prognosis-prediction models.

INTRODUCTION
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. China has
the largest number of HCC cases worldwide, accounting for nearly half of the new cases and deaths
worldwide every year . HCC has a concealed onset and often has no obvious symptoms in the early stages.
[1]
Approximately 64% of Chinese patients with HCC are in the intermediate and advanced stages at the time of
initial diagnosis and are no longer suitable for surgical resection [2,3] . Transarterial chemoembolization
(TACE) is commonly used and preferred local treatment for unresectable hepatocellular carcinoma
(uHCC) . It achieves the purpose of tumor ischemia and necrosis by embolizing the tumor blood supply
[4]
artery and combining with local chemotherapy drugs . HCC has the biological characteristics of easy
[5]
recurrence and metastasis; therefore, in clinical practice, TACE usually needs to be repeated to remove
residual cancer cells and control the formation of new lesions. With an increase in the number of TACE
treatments, the tumor remission rate showed an upward trend in the short term. However, as the tumor
continues to progress, the effective rate of TACE treatment gradually declines . At the same time, the side
[6]
effects related to TACE treatment also gradually appear, especially liver function injury, which is closely
related to the prognosis of patients . Although the guidelines [8-11] have principled recommendations on the
[7]
use of TACE, there is a lack of evidence-based medical evidence to clarify the optimal treatment frequency
interval, which brings great uncertainty to clinical decision-making.

In recent years, TACE combined with targeted therapy plus immunotherapy has become the core treatment
strategy for advanced HCC . At present, a number of clinical studies have found that the number of TACE
[12]
in the “TACE + targeted therapy plus immunotherapy” group was significantly reduced compared with that
in the simple TACE group, and the prognosis of patients was significantly improved. Combination therapy
has shown good efficacy in both clinical trials and real-world studies. It not only enhanced the tumor
response but also overcame the decline in efficacy and deterioration of liver function caused by repeated
TACE [13-16] .

Simultaneously, methodological progress of prediction models provides a new way to optimize the treatment
of patients with HCC. Traditional staging systems stage patients at the time of disease diagnosis, but this
staging does not change with disease progression and treatment response. This means that by the late stage
of the disease, the initial diagnosis stage of patients has been unable to effectively distinguish their prognosis.
In recent years, dynamic prediction models integrating circulating biomarkers, imaging omics features, and
machine learning have made methodological breakthroughs, providing the possibility of more accurate and
individualized prognosis prediction for HCC patients. This emphasizes the need for a methodological shift
from static to dynamic approaches to support more precise and individualized treatment decisions.