Prognosis nomogram for transhepatic arterial chemoembolisation with 125I seed implantation for first-branch portal vein tumour thrombus hepatocellular carcinoma：a comparative study of two statistical analysis methods
التفاصيل البيبلوغرافية
العنوان:
Prognosis nomogram for transhepatic arterial chemoembolisation with 125I seed implantation for first-branch portal vein tumour thrombus hepatocellular carcinoma：a comparative study of two statistical analysis methods
Purpose: To investigate the efficacy of transhepatic arterial chemoembolisation (TACE) combined with 125I seed implantation in patients with first-branch portal vein tumour thrombus (PVTT) hepatocellular carcinoma (HCC), we tried to establish a nomogram to determine the prognosis of individual patients by comparing different statistical methods. Materials and Methods: 97 patients who underwent TACE combined with PVTT 125I seed implantation served as the primary cohort. The median overall survival (mOS) was the main indicator of prognosis nomogram. Univariate analysis and LASSO regression were used to screen for variables. Selected variables were included in the multivariate Cox survival regression model, and a prognostic nomogram was developed. The performance of the nomogram was assessed in terms of calibration, discrimination, and clinical usefulness. The internal and external verifications were also evaluated. Results: The mOS of all patients was 12.5 months (95%CI: 11.78–13.22). The final determination was made using the tumour burden, PVTT types, and platelet (PLT) count to establish a nomogram prediction model [C-index: 0.770 (95% confidence interval: 0.723–0.817)]. This model demonstrated good discrimination and calibration. Application of the nomogram to external verification data still yielded good discrimination [C-index: 0.764] and good calibration. The decision curve analysis demonstrated that the clinical validity of the model was clinically useful. Conclusion: This nomogram can be used to predict the prognostic survival time of such patients and can help doctors make decisions in clinical practice. At the same time, the appropriate statistical methods should be selected based on clinical needs.
