Machine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients.
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 l...
| Publicado en: | Clinical Medicine Insights: Oncology pp. 1 - 8 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
3/24/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149469626&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149469626 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795549 B3KT jtl: Clinical Medicine Insights: Oncology issn: 11795549 maglogo: Y pubinfo: dt: 3/24/2021 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 149469626 149469626 149469626 10.1177/11795549211000017 149469626 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients. aug: au: Tong, Jianhua Liu, Panmiao Ji, Muhuo Wang, Ying Xue, Qiong Yang, Jian-Jun Zhou, Cheng-Mao affil: Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China sug: subj: Machine Learning Liver Neoplasms Mortality Radiofrequency Ablation Human Secondary Analysis Algorithms Liver Neoplasms Prognosis Carcinoma, Hepatocellular Mortality Carcinoma, Hepatocellular Prognosis ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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