A Refined Prognostic Model for Postoperative Overall Survival in Hepatocellular Carcinoma Based on CODEX-Based Multiproteomics and Radiomics.

Abstract

Purpose: This study aimed to develop a predictive model for the prognosis of patients with hepatocellular carcinoma (HCC) after resection.

Methods: Eighty-two HCC patients were randomly divided into a training cohort (n = 62) and a validation cohort (n = 20). Clinicopathological, multiproteomics features based on CO-Detection by Indexing (Codex), and radiomics features extracted from magnetic resonance imaging (MRI) were used to construct four models: clinicopathological model, radiomics model, proteomics model, and combined model. Model performance was evaluated using the C-index, calibration curves, receiver operating characteristic (ROC) curves, survival curves, and decision curve analysis (DCA).

Results: The combined model, integrating clinicopathological, radiomics, and multi-proteomic features, demonstrated the best performance of overall survival (OS) prediction in both the training cohort (C-index = 0.821, 95% CI: 0.745-0.897) and validation cohort (C-index = 0.791, 95% CI: 0.628-0.954). The calibration curve showed high accuracy of the combined nomogram in predicting OS.

Conclusion: This study innovatively integrates CODEX-based multiproteomics, radiomics, and clinicopathological features to construct a prognostic prediction model for HCC. The combined model demonstrates improved prognostic predictive efficacy compared with single-modality models. This approach establishes a theoretical foundation for personalized diagnosis and treatment. However, its clinical utility requires further validation through large-scale, multi-center studies.