Your browser doesn't support javascript.
loading
Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.
Wang, Qi; Sheng, Shugui; Xiong, Yiqi; Han, Ming; Jin, Ronghua; Hu, Caixia.
  • Wang Q; Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
  • Sheng S; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Xiong Y; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Han M; Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.
  • Jin R; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Hu C; National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Front Immunol ; 15: 1409443, 2024.
Article en En | MEDLINE | ID: mdl-38863693
ABSTRACT

Introduction:

This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment.

Methods:

We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance.

Results:

The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Conclusion:

Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bilirrubina / Carcinoma Hepatocelular / Nomogramas / Aprendizaje Automático / Neoplasias Hepáticas / Recurrencia Local de Neoplasia Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bilirrubina / Carcinoma Hepatocelular / Nomogramas / Aprendizaje Automático / Neoplasias Hepáticas / Recurrencia Local de Neoplasia Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2024 Tipo del documento: Article