Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

Background Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine...

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Vydáno v:Journal of translational medicine Ročník 22; číslo 1; s. 455 - 10
Hlavní autoři: Dong, Bingtian, Zhang, Hua, Duan, Yayang, Yao, Senbang, Chen, Yongjian, Zhang, Chaoxue
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 13.05.2024
BioMed Central Ltd
BMC
Témata:
Algorithms
alpha-Fetoproteins - metabolism
Area Under Curve
Biomedical and Life Sciences
Biomedicine
Calibration
Carcinoma, Hepatocellular - blood
Carcinoma, Hepatocellular - diagnosis
Carcinoma, Hepatocellular - mortality
Carcinoma, Hepatocellular - pathology
Computational Modelling and Epidemiology
Epidemiology
Female
Glycoproteins
Hepatocellular carcinoma
Hepatoma
Humans
Liver Neoplasms - blood
Liver Neoplasms - diagnosis
Liver Neoplasms - mortality
Liver Neoplasms - pathology
Machine Learning
Male
Medical research
Medicine, Experimental
Medicine/Public Health
Predictive analytics
Prognosis
ROC Curve
Survival
XGBoost algorithm
Taiwan
Hepatocellular carcinoma
XGBoost algorithm
Predictive analytics
Survival
Machine learning
ISSN:1479-5876, 1479-5876
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Shrnutí:Background Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. Methods Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. Results A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. Conclusions The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-024-05203-w