Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
Objectives: To use baseline variables to predict one-year disease control for patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as initial treatment by applying a machine learning approach based on the random survival forest (RF)...
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.618050/full |
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| author | Bin-Yan Zhong Zhi-Ping Yan Zhi-Ping Yan Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Xiao-Li Zhu Ling Wen Cai-Fang Ni |
| author_facet | Bin-Yan Zhong Zhi-Ping Yan Zhi-Ping Yan Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Xiao-Li Zhu Ling Wen Cai-Fang Ni |
| author_sort | Bin-Yan Zhong |
| collection | DOAJ |
| description | Objectives: To use baseline variables to predict one-year disease control for patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as initial treatment by applying a machine learning approach based on the random survival forest (RF) model.Materials and Methods: The multicenter retrospective study included 496 patients with HCC treated with TACE combined with sorafenib between January 2014 and December 2018. The independent risk factors associated with one-year disease control (complete response, partial response, stable disease) were identified using the RF model, and their predictive importance was determined using the Gini index. Tumor response was assessed according to modified Response Evaluation Criteria in Solid Tumors.Results: The median overall survival was 15.5 months. A total of 186 (37.5%) patients achieved positive one-year disease control. The Barcelona Clinic Liver Cancer (BCLC) stage (Gini index: 20.0), tumor size (≤7 cm, >7 cm; Gini index: 9.0), number of lobes involved (unilobar, bilobar; Gini index: 6.4), alpha-fetoprotein level (≤200 ng/dl, >200 ng/dl; Gini index: 6.1), albumin–bilirubin grade (Gini index: 5.7), and number of lesions (1, >1; Gini index: 5.3) were identified as independent risk factors, with the BCLC stage as the most important variable. The RF model achieved a higher concordance index of 0.724 compared to that for the logistic regression model (0.709).Conclusions: The RF model is a simple and accurate approach for prediction of one-year disease control for patients with HCC treated with TACE combined with sorafenib. |
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| language | English |
| last_indexed | 2024-12-21T15:09:16Z |
| publishDate | 2021-05-01 |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-c18a8c91fca24278b9333e6f9d90c9ff2022-12-21T18:59:20ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-05-01810.3389/fmolb.2021.618050618050Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With SorafenibBin-Yan Zhong0Zhi-Ping Yan1Zhi-Ping Yan2Zhi-Ping Yan3Jun-Hui Sun4Lei Zhang5Zhong-Heng Hou6Xiao-Li Zhu7Ling Wen8Cai-Fang Ni9Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, ChinaShanghai Institution of Medical Imaging, Shanghai, ChinaNational Clinical Research Center for Interventional Medicine, Shanghai, ChinaHepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaObjectives: To use baseline variables to predict one-year disease control for patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as initial treatment by applying a machine learning approach based on the random survival forest (RF) model.Materials and Methods: The multicenter retrospective study included 496 patients with HCC treated with TACE combined with sorafenib between January 2014 and December 2018. The independent risk factors associated with one-year disease control (complete response, partial response, stable disease) were identified using the RF model, and their predictive importance was determined using the Gini index. Tumor response was assessed according to modified Response Evaluation Criteria in Solid Tumors.Results: The median overall survival was 15.5 months. A total of 186 (37.5%) patients achieved positive one-year disease control. The Barcelona Clinic Liver Cancer (BCLC) stage (Gini index: 20.0), tumor size (≤7 cm, >7 cm; Gini index: 9.0), number of lobes involved (unilobar, bilobar; Gini index: 6.4), alpha-fetoprotein level (≤200 ng/dl, >200 ng/dl; Gini index: 6.1), albumin–bilirubin grade (Gini index: 5.7), and number of lesions (1, >1; Gini index: 5.3) were identified as independent risk factors, with the BCLC stage as the most important variable. The RF model achieved a higher concordance index of 0.724 compared to that for the logistic regression model (0.709).Conclusions: The RF model is a simple and accurate approach for prediction of one-year disease control for patients with HCC treated with TACE combined with sorafenib.https://www.frontiersin.org/articles/10.3389/fmolb.2021.618050/fullhepatocellular carcinomatransarterial chemoembolizationsorafenibdisease controlrandom survival forest |
| spellingShingle | Bin-Yan Zhong Zhi-Ping Yan Zhi-Ping Yan Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Xiao-Li Zhu Ling Wen Cai-Fang Ni Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib Frontiers in Molecular Biosciences hepatocellular carcinoma transarterial chemoembolization sorafenib disease control random survival forest |
| title | Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib |
| title_full | Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib |
| title_fullStr | Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib |
| title_full_unstemmed | Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib |
| title_short | Random Survival Forests to Predict Disease Control for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib |
| title_sort | random survival forests to predict disease control for hepatocellular carcinoma treated with transarterial chemoembolization combined with sorafenib |
| topic | hepatocellular carcinoma transarterial chemoembolization sorafenib disease control random survival forest |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.618050/full |
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