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<p>To establish and validate a novel prognostic model to predict outcomes for intermediate hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE). Clinical data from intermediate-stage HCC patients who underwent TACE between January 2007 and December 2020 were retrospectively analyzed. Patients were divided into a training cohort and a validation cohort. Univariate and multivariate analyses identified risk factors associated with overall survival (OS), leading to the development of a predictive model. The model’s accuracy, consistency, and clinical utility were validated both internally and externally and compared with the Albumin–Bilirubin (ALBI) grading, Platelet–Albumin–Bilirubin (PALBI) grading, Child-Pugh grading, mChild-Pugh grading, and the Model for End-Stage Liver Disease (MELD). A total of 737 intermediate-stage HCC patients were included, with 481 in the training cohort and 256 in the validation cohort. Multivariate analysis identified maximum tumor diameter, tumor number, prealbumin, and total bilirubin as independent factors for OS. A prealbumin–bilirubin (PABI) predictive model was developed. The PABI model’s concordance indices (C-index) in the training and validation cohorts were 0.730 (95% CI 0.701–0.759) and 0.706 (95% CI 0.661–0.751), respectively. The area under the curve (AUC) values at 6, 12, 18, and 24 months in both cohorts were above 0.7. Among the six models, the PABI model had the highest C-index (0.713) and the lowest Akaike information criterion (AIC) value (5897.814) and the best performance in clinical decision curve analysis, suggesting better predictive performance and potential clinical utility. The PABI nomogram model appears to accurately predict survival in intermediate-stage HCC patients treated with TACE, providing clinicians with a valuable tool for candidate selection and prognosis stratification.</p>

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Development and validation of a prealbumin–bilirubin model for prognostic prediction in intermediate hepatocellular carcinoma undergoing transarterial chemoembolization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月25日

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  • Nan Jiang,
  • Yi Zhan,
  • Shuai Zhang,
  • Binyan Zhong,
  • Jun Yang,
  • Yu Yin,
  • Wanci Li,
  • Mingming Li,
  • Jian Shen,
  • Zhi Li,
  • Xiaoli Zhu,
  • Caifang Ni

摘要

To establish and validate a novel prognostic model to predict outcomes for intermediate hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE). Clinical data from intermediate-stage HCC patients who underwent TACE between January 2007 and December 2020 were retrospectively analyzed. Patients were divided into a training cohort and a validation cohort. Univariate and multivariate analyses identified risk factors associated with overall survival (OS), leading to the development of a predictive model. The model’s accuracy, consistency, and clinical utility were validated both internally and externally and compared with the Albumin–Bilirubin (ALBI) grading, Platelet–Albumin–Bilirubin (PALBI) grading, Child-Pugh grading, mChild-Pugh grading, and the Model for End-Stage Liver Disease (MELD). A total of 737 intermediate-stage HCC patients were included, with 481 in the training cohort and 256 in the validation cohort. Multivariate analysis identified maximum tumor diameter, tumor number, prealbumin, and total bilirubin as independent factors for OS. A prealbumin–bilirubin (PABI) predictive model was developed. The PABI model’s concordance indices (C-index) in the training and validation cohorts were 0.730 (95% CI 0.701–0.759) and 0.706 (95% CI 0.661–0.751), respectively. The area under the curve (AUC) values at 6, 12, 18, and 24 months in both cohorts were above 0.7. Among the six models, the PABI model had the highest C-index (0.713) and the lowest Akaike information criterion (AIC) value (5897.814) and the best performance in clinical decision curve analysis, suggesting better predictive performance and potential clinical utility. The PABI nomogram model appears to accurately predict survival in intermediate-stage HCC patients treated with TACE, providing clinicians with a valuable tool for candidate selection and prognosis stratification.