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基于机器学习的模型,用于预测术前血小板-白蛋白-胆红素评分低的 HBV 相关肝细胞癌患者介入治疗后肿瘤复发。

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

机构信息

Interventional Therapy Center for Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China.

National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

出版信息

Front Immunol. 2024 May 28;15:1409443. doi: 10.3389/fimmu.2024.1409443. eCollection 2024.


DOI:10.3389/fimmu.2024.1409443
PMID:38863693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11165108/
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.

摘要

简介:本研究旨在为经肝动脉化疗栓塞(TACE)联合局部消融治疗后术前血小板-白蛋白-胆红素(PALBI)评分较低的乙型肝炎病毒(HBV)相关肝细胞癌(HCC)患者建立预测无复发生存率(RFS)的预后列线图。

方法:我们收集了 2014 年 1 月至 2020 年 1 月期间在北京佑安医院接受联合治疗的 632 例 HBV 相关 HCC 患者的临床资料。根据 PALBI 评分,患者分为两组:低 PALBI 组(n=247)和高 PALBI 组(n=385)。低 PALBI 组进一步分为训练队列(n=172)和验证队列(n=75)。我们利用极端梯度提升(XGBoost)、随机生存森林(RSF)和多变量 Cox 分析确定 RFS 的危险因素。然后,我们根据筛选出的因素建立列线图,并评估其风险分层能力和预测性能。

结果:研究最终确定年龄、天门冬氨酸氨基转移酶(AST)和凝血酶原时间活动度(PTA)为关键预测因素。将这三个变量纳入列线图,以预测 HCC 患者的 1、3 和 5 年 RFS。我们通过 Kaplan-Meier 曲线证实了该列线图有效区分高危和低危患者的能力。我们通过 C 指数、曲线下面积(AUC)、校准曲线和决策曲线分析(DCA)进一步评估验证了该列线图的良好区分度、一致性和临床实用性。

结论:本研究成功构建了一个稳健的列线图,可有效预测 TACE 联合局部消融治疗后术前 PALBI 评分较低的 HBV 相关 HCC 患者的 1、3 和 5 年 RFS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/d2cdff23ebb8/fimmu-15-1409443-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/c8a5401590f2/fimmu-15-1409443-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/d35305098e34/fimmu-15-1409443-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/9c3427c379e6/fimmu-15-1409443-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/f8ab8ff7c274/fimmu-15-1409443-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/a41bcc45eb0c/fimmu-15-1409443-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/48ba03779b76/fimmu-15-1409443-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/63cfc306bc88/fimmu-15-1409443-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/d2cdff23ebb8/fimmu-15-1409443-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/c8a5401590f2/fimmu-15-1409443-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/d35305098e34/fimmu-15-1409443-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/9c3427c379e6/fimmu-15-1409443-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/f8ab8ff7c274/fimmu-15-1409443-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/a41bcc45eb0c/fimmu-15-1409443-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/48ba03779b76/fimmu-15-1409443-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/63cfc306bc88/fimmu-15-1409443-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1296/11165108/d2cdff23ebb8/fimmu-15-1409443-g008.jpg

相似文献

[1]
Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score.

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[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
The EZ-ALBI and PALBI scores contribute to the clinical application of ALBI in predicting postoperative recurrence of HCC.

Sci Rep. 2025-3-17

[2]
Triglyceride-glucose index predicts postoperative overall survival in hepatocellular carcinoma: a retrospective cohort study.

Discov Oncol. 2024-11-13

[3]
Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Jpn J Radiol. 2025-2

本文引用的文献

[1]
Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation.

J Hepatocell Carcinoma. 2023-11-29

[2]
Noninvasive diagnosis of significant liver inflammation in patients with chronic hepatitis B in the indeterminate phase.

Virulence. 2023-12

[3]
Oncological prognosis and morbidity of hepatectomy in elderly patients with hepatocellular carcinoma: a propensity score matching and multicentre study.

BMC Surg. 2023-10-24

[4]
The Novel-Natural-Killer-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Hepatocellular Carcinoma.

Int J Mol Sci. 2023-5-31

[5]
Development of hepatocellular carcinoma in treated and untreated patients with chronic hepatitis B virus infection.

Clin Mol Hepatol. 2023-7

[6]
Construction and validation of NSMC-ASIL, a predictive model for risk assessment of malignant hepatic nodules.

Am J Cancer Res. 2022-11-15

[7]
Global burden of primary liver cancer in 2020 and predictions to 2040.

J Hepatol. 2022-12

[8]
Development and experimental verification of a prognosis model for cuproptosis-related subtypes in HCC.

Hepatol Int. 2022-12

[9]
Immunosenescence, aging and successful aging.

Front Immunol. 2022

[10]
Prognostic Value of Platelet-Albumin-Bilirubin Grade in Child-Pugh A and B Patients With Hepatocellular Carcinoma: A Meta-Analysis.

Front Oncol. 2022-7-13

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