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已发表论文
基于 CODEX 多组学蛋白质组学和影像组学的肝细胞癌术后总体生存的精细预后模型
Authors Wu Y, Wu J, Duan S, Liu D, Liu W, Song K, Zhang J, Feng Y, Zhang S, Liu Y, Dong H, Zhang H, Chen L, Jia N
Received 9 March 2025
Accepted for publication 19 September 2025
Published 26 September 2025 Volume 2025:12 Pages 2169—2182
DOI https://doi.org/10.2147/JHC.S527066
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 6
Editor who approved publication: Prof. Dr. Imam Waked
Yuxian Wu,1,* Jianmin Wu,2,3,* Shaofeng Duan,4,* Dong Liu,1,* Wanmin Liu,5 Kairong Song,1 Juan Zhang,1 Yayuan Feng,1 Sisi Zhang,1 Yiping Liu,1 Hui Dong,6 Hao Zhang,7 Lei Chen,2,8 Ningyang Jia1
1Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China; 2The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China; 3Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, People’s Republic of China; 4Precision Health Institution, GE Healthcare, Shanghai, People’s Republic of China; 5Department of Radiology, Tongji Hospital, Shanghai, People’s Republic of China; 6Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China; 7Department of Neurosurgery, Naval Medical Center of PLA, Second Military Medical University, Shanghai, People’s Republic of China; 8National Center for Liver Cancer, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ningyang Jia, Department of Radiology, Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China, Email ningyangjia@163.com Lei Chen, The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China, Email chenlei@smmu.edu.cn
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.
Keywords: hepatocellular carcinoma, radiomics, codex, magnetic resonance imaging, overall survival
1Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China; 2The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China; 3Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, People’s Republic of China; 4Precision Health Institution, GE Healthcare, Shanghai, People’s Republic of China; 5Department of Radiology, Tongji Hospital, Shanghai, People’s Republic of China; 6Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China; 7Department of Neurosurgery, Naval Medical Center of PLA, Second Military Medical University, Shanghai, People’s Republic of China; 8National Center for Liver Cancer, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ningyang Jia, Department of Radiology, Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China, Email ningyangjia@163.com Lei Chen, The International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, People’s Republic of China, Email chenlei@smmu.edu.cn
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.
Keywords: hepatocellular carcinoma, radiomics, codex, magnetic resonance imaging, overall survival