ArticleAmerican journal of cancer research2025
Prognostic prediction of primary liver cancer following transcatheter arterial chemoembolization (TACE) combined with targeted immunotherapy based on CT morphological characteristics.
Article in American journal of cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo develop and validate a predictive model based on computed tomography (CT) imaging features for predicting prognosis in primary liver cancer patients undergoing transcatheter arterial chemoembolization (TACE) combined with targeted immunotherapy.
methodsThis retrospective cohort study included 200 patients (training cohort) treated from May 2021 to May 2024. Patients were classified into Good Prognosis (complete response/partial response/stable disease [CR/PR/SD], n=97) or Poor Prognosis (PD/death, n=103) groups based on Response Evaluation Criteria in Solid Tumors (RECIST) criteria at post-treatment one year. Clinical data, biochemical markers, and multiphase CT features (Non-contrast, Arterial, Venous, Delayed) were analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for patients' prognosis. A predictive model was built and validated internally and externally (n=85).
resultsChild-Pugh class, C-reactive protein (CRP), alpha-fetoprotein (AFP), aspartate aminotransferase (AST), and CT density characteristics across phases (non-contrast phase [NP], arterial phase [AP], venous phase [VP], delayed phase [DP], All-Phases [All-P]) significantly differed between groups (P < 0.05). Multivariate analysis identified Child-Pugh class (odds ratio [OR]=0.345, P=0.030), AFP (OR=0.989, P=0.022), and Non-contrast Phase density (OR=4.378, P=0.032) as independent predictors. The model showed good prediction ability, with an area under the curve (AUC) of 0.811 in the training cohort and 0.931 in the test cohort, demonstrating robust predictive performance.
conclusionThe predictive model integrating CT imaging features, especially tumor density in non-contrast phase, alongside Child-Pugh class and AFP, demonstrate robust predictive performance for risk stratification and personalized treatment planning.
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