ArticleFrontiers in immunology2026
Dynamic nomogram integrating Gd-EOB-DTPA enhanced MRI semantic features, nutritional-inflammatory indices, and early treatment response to predict long-term survival in unresectable HCC treated with interventional, targeted, and immunotherapy: a multicenter retrospective study.
Article in Frontiers in immunology, 2026. 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
Objective: This study developed and validated a dynamic prediction model combining MRI features, clinical factors, and early treatment response to noninvasively predict long-term survival in unresectable hepatocellular carcinoma patients undergoing triple therapy (hepatic arterial infusion chemotherapy, targeted therapy, and immunotherapy). Methods: This retrospective study enrolled 223 patients from two centers, with 127 in the training set and 96 in the external set, evaluating imaging features and constructing a dynamic nomogram to predict 1-year and 2-year survival. Model performance was evaluated via bootstrap resampling and external validation. Decision curve analysis and SHapley Additive exPlanations (SHAP) analysis were employed to assess clinical utility and model interpretability. An R/Shiny application was used for online deployment. Results: An imaging score was created based on five semantic features that suggest poor prognosis. The dynamic model, incorporating five semantic imaging features indicating poor prognosis, showed good discrimination, calibration, and clinical utility, especially when combining baseline and early treatment response. The predictive performance of the dynamic nomogram was numerically improved to that of BCLC staging and the traditional liver function grading system. SHAP analysis highlighted the imaging score as the most important predictor. The dynamic nomogram effectively stratified patients into low-, medium-, and high-risk groups, with significantly different survival outcomes. Conclusion: A dynamic nomogram model that integrates baseline features and therapeutic response provides a noninvasive and effective tool for predicting prognosis and stratifying risk in uHCC patients receiving triple therapy.
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