Open AccessMachine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients
Author(s) -
Jianhua Tong,
Panmiao Liu,
MuHuo Ji,
Ying Wang,
Qingyun Xue,
Jianjun Yang,
Chengmao Zhou
Publication year - 2021
Publication title -
clinical medicine insights. oncology
Language(s) - English
Resource type - Journals
ISSN - 1179-5549
DOI - 10.1177/11795549211000017
Subject(s) - medicine , liver cancer , hepatocellular carcinoma , logistic regression , algorithm , radiofrequency ablation , colorectal cancer , cancer , mortality rate , survival rate , gastroenterology , oncology , machine learning , ablation , computer science
Objective: Over 1 million new cases of hepatocellular carcinoma (HCC) are diagnosed worldwide every year. Its prognosis remains poor, and the 5-year survival rate in all disease stages is estimated to be between 10% and 20%. Radiofrequency ablation (RFA) has become an important local treatment for liver cancer, and machine learning (ML) can provide many shortcuts for liver cancer medical research. Therefore, we explore the role of ML in predicting the total mortality of liver cancer patients undergoing RFA.Methods: This study is a secondary analysis of public database data from 578 liver cancer patients. We used Python for ML to establish the prognosis model.Results: The results showed that the 5 most important factors were platelet count (PLT), Alpha-fetoprotein (AFP), age, tumor size, and total bilirubin, respectively. Results of the total death model for liver cancer patients in test group: among the 5 algorithm models, the highest accuracy rate was that of gbm (0.681), followed by the Logistic algorithm (0.672); among the 5 algorithms, area under the curve (AUC) values, from high to low, were Logistic (0.738), DecisionTree (0.723), gbm (0.717), GradientBoosting (0.714), and Forest (0.693); Among the 5 algorithms, gbm had the highest precision rate (0.721), followed by the Logistic algorithm (0.714). Among the 5 algorithms, DecisionTree had the highest recall rate (0.642), followed by the GradientBoosting algorithm (0.571).Conclusion: Machine learning can predict total death after RFA in liver cancer patients. Therefore, ML research has great potential for both personalized treatment and prognosis of liver cancer.