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Page 16 of 22                                                    Ma et al. Hepatoma Res. 2026;12:43





               Critical appraisal of prognostic models: from derivation to clinical utility
               While the models described above represent significant methodological advancements, their readiness for
               routine clinical practice varies considerably. A critical assessment requires examining four key aspects: (1)
               external validation (has the model’s performance been confirmed in independent, diverse populations?); (2)
               calibration (does the predicted survival probability match the observed outcome?); (3) discrimination (how
               well does the model separate high-risk from low-risk patients, often measured by C-index/AUC?); (4)
               clinical utility (does using the model lead to better patient decisions and outcomes?). The ART, mART,
               ABCR, and 6-and-12 models have undergone external validation in multiple cohorts and demonstrate
               acceptable discrimination (C-index typically 0.65-0.75). Their simplicity is a key strength, enabling point-of-
               care calculation. However, calibration is rarely reported, and prospective studies demonstrating improved
               clinical outcomes (e.g., decision curve analysis) are largely lacking. In contrast, frontier models based on cf-
               mtDNA or radiomics with machine learning (CatBoost) are highly promising but remain at an
               investigational stage. Their limitations include the following: (1) lack of prospective, multicenter external
               validation; (2) potential for overfitting; (3) need for specialized technologies; (4) unclear cost-effectiveness.


               CONCLUSIONS AND FUTURE PROSPECTS
               This review summarizes the complex relationship between the number of TACE treatments and the
               prognosis of patients with HCC. Existing evidence suggests that although TACE is an important local
               treatment for uHCC, its efficacy tends to first increase and then decrease with an increasing number of
               treatment sessions. The third TACE is often regarded as a potential key node. Research indicates that, for
               many patients, the incremental benefit of TACE may diminish after the third session, and a fourth session is
               unlikely to confer a significant survival advantage in certain patient subgroups. Additionally, repeated TACE
               is associated with an increased risk of liver function deterioration and the development of TACE
               refractoriness. Therefore, while TACE remains an effective treatment, blindly increasing the number of
               sessions without clear objective response cannot improve survival and may accelerate liver failure. The
               decision to repeat TACE beyond the third session must be highly individualized, requiring comprehensive
               assessment based on multiple indicators, including tumor response, preserved hepatic reserve, and
               performance status. Figure 2 presents a clinical decision algorithm for repeated TACE in HCC, integrating
               tumor response assessment, liver function reserve, and treatment response to guide the decision to continue
               or switch to systemic therapy.


               TACE combined with targeted therapy plus immunotherapy has significantly changed the treatment patterns
               of advanced HCC. Combination therapy not only improved the objective remission rate of the tumor but
               also significantly reduced the number of TACE sessions required, achieving the goal of maximum protection
               of liver function while controlling the tumor. Many trials and real-world data support the advantages of
               combined strategies in prolonging OS and PFS.


               In terms of prognosis prediction, the traditional static model provides a basic tool for pre-treatment risk
               stratification; however, its “static” attribute is difficult to adapt to the dynamic evolution of HCC. The
               dynamic prediction model realizes closed-loop management of “treatment evaluation adjustment” by
               integrating the treatment response, which greatly promotes individualized treatment. Nowadays, cutting-
               edge models based on cf-mtDNA, machine learning, and multimodal imaging omics show the potential to
               achieve accurate, dynamic, and prospective prognosis prediction in a higher dimension.

               In the future, TACE treatment of HCC will pay more attention to the following three principles: (1) equal
               emphasis on curative effect and liver function: The cumulative risk of liver function deterioration increases
               with each TACE session. Therefore, treatment decisions should prioritize both tumor response and the
               protection of hepatic reserve, avoiding futile repeated embolization that may accelerate liver failure without
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