Intermediate-stage hepatocellular carcinoma (HCC) encompasses a diverse patient population that requires individualized treatment strategies and a multidisciplinary approach. Recent advancements in systemic therapy have expanded the therapeutic options for intermediate-stage HCC, allowing for combination strategies such as systemic therapy with transarterial chemoembolization (TACE) and upfront systemic therapy for individuals deemed unsuitable for TACE. Additionally, the ongoing development of treatment modalities for intermediate-stage HCC has improved the potential for curative conversion and tumor downstaging. Nevertheless, consensus on the optimal management of intermediate-stage HCC remains limited. Thus, the primary aim of this study was to develop a set of consensus guidelines for the management of intermediate-stage HCC. To address this gap, the Taiwan Liver Cancer Association (TLCA) established a working group to develop a multidisciplinary strategy for managing intermediate-stage HCC. Here, we present eight consensus statements formulated by this expert panel, which outline criteria for TACE unsuitability, treatment recommendations based on TACE eligibility, and considerations for various modalities, including conventional TACE, drug-eluting bead TACE, and transarterial radioembolization, as well as the appropriate timing for initiating systemic therapy to enable curative conversion and downstaging. These statements provide specific, evidence-based recommendations for clinicians, addressing treatment pathways based on TACE eligibility and other key considerations for intermediate-stage HCC management. The development of this consensus guideline is intended to aid clinicians in selecting the most appropriate treatment pathway for intermediate-stage HCC, support personalized treatment planning, and ultimately enhance the feasibility of achieving curative conversion.
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Correspondence to letter to the editor 1 on “Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma” Chun-Ting Ho, Elise Chia-Hui Tan, Chien-Wei Su Clinical and Molecular Hepatology.2025; 31(1): e96. CrossRef
Background/Aims The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.
Methods The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort.
Result s: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores.
Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.
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