A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation
Background: Prediction of early recurrence (ER) of HCC after radical treatment is of great significance for follow-up and subsequent treatment, and there is a lot of unmet needs. Here, our goal is to develop and validate a radiomics nomogram that can predict ER after curative ablation. Objective: Th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Asian Journal of Surgery |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1015958422014099 |
| _version_ | 1797790903528587264 |
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| author | Wei Peng Xinhua Jiang Weidong Zhang Jianmin Hu YaoJun Zhang Ling Zhang |
| author_facet | Wei Peng Xinhua Jiang Weidong Zhang Jianmin Hu YaoJun Zhang Ling Zhang |
| author_sort | Wei Peng |
| collection | DOAJ |
| description | Background: Prediction of early recurrence (ER) of HCC after radical treatment is of great significance for follow-up and subsequent treatment, and there is a lot of unmet needs. Here, our goal is to develop and validate a radiomics nomogram that can predict ER after curative ablation. Objective: The aim of this study was to evaluate the efficacy and safety of regorafenib after disease progression with sorafenib in Chinese patients with advanced HCC through this retrospective analysis. Methods: 149 HCC patients treated between November 2008 and February 2018 were enrolled and randomly divided into training cohort (n = 105) and validation cohort (n = 44). The survival endpoint was recurrence-free survival (RFS). A total of 16908 radiomics features were extracted from the contrast-enhanced MR images of each patient. The minimum redundancy maximum relevance algorithm (mRMR) and random survival forest (RSF) were used for feature selection. Twelve kinds of support vector machine (SVM) models, a Cox regression model (Cox PH), a random survival forest (RSF) model and a gradient boosting model (GBoost) were used to build a radiomics signature. These models were trained after adjusting the model parameters using 5-fold cross-validation. The best models were selected according to the C-index. Results: Using the machine learning (ML) framework, 40 features were identified that demonstrated good prediction of HCC recurrence across all cohorts. The random survival forest (RSF) model showed higher prognostic value, with a C-index of 0.733–0.801 and an integrated Brier score of 0.147–0.165, compared with other SVM models, Cox regression models, etc. (all P < 0.05). Time-dependent receiver operating characteristic (ROC) curve analysis, survival analysis, and decision curve analysis (DCA) were used to verify the performance of the RSF model in predicting tumor recurrence. Conclusion: We successfully built a radiomics-based RSF model with integrated radiomics and clinicopathological features that can potentially be used to predict ER after curative ablation in HCC patients. |
| first_indexed | 2024-03-13T02:11:09Z |
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| institution | Directory Open Access Journal |
| issn | 1015-9584 |
| language | English |
| last_indexed | 2024-03-13T02:11:09Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Asian Journal of Surgery |
| spelling | doaj.art-841f268b1ee548ee87807765681bd97d2023-07-01T04:33:48ZengElsevierAsian Journal of Surgery1015-95842023-07-0146726892696A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablationWei Peng0Xinhua Jiang1Weidong Zhang2Jianmin Hu3YaoJun Zhang4Ling Zhang5Department of Liver Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, 510060, Guangdong, PR ChinaDepartment of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, PR ChinaDepartment of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, PR ChinaDepartment of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, PR ChinaDepartment of Liver Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, 510060, Guangdong, PR China; Corresponding author. Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, 510060, PR China.Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, PR China; Corresponding author. Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, 510060, PR China.Background: Prediction of early recurrence (ER) of HCC after radical treatment is of great significance for follow-up and subsequent treatment, and there is a lot of unmet needs. Here, our goal is to develop and validate a radiomics nomogram that can predict ER after curative ablation. Objective: The aim of this study was to evaluate the efficacy and safety of regorafenib after disease progression with sorafenib in Chinese patients with advanced HCC through this retrospective analysis. Methods: 149 HCC patients treated between November 2008 and February 2018 were enrolled and randomly divided into training cohort (n = 105) and validation cohort (n = 44). The survival endpoint was recurrence-free survival (RFS). A total of 16908 radiomics features were extracted from the contrast-enhanced MR images of each patient. The minimum redundancy maximum relevance algorithm (mRMR) and random survival forest (RSF) were used for feature selection. Twelve kinds of support vector machine (SVM) models, a Cox regression model (Cox PH), a random survival forest (RSF) model and a gradient boosting model (GBoost) were used to build a radiomics signature. These models were trained after adjusting the model parameters using 5-fold cross-validation. The best models were selected according to the C-index. Results: Using the machine learning (ML) framework, 40 features were identified that demonstrated good prediction of HCC recurrence across all cohorts. The random survival forest (RSF) model showed higher prognostic value, with a C-index of 0.733–0.801 and an integrated Brier score of 0.147–0.165, compared with other SVM models, Cox regression models, etc. (all P < 0.05). Time-dependent receiver operating characteristic (ROC) curve analysis, survival analysis, and decision curve analysis (DCA) were used to verify the performance of the RSF model in predicting tumor recurrence. Conclusion: We successfully built a radiomics-based RSF model with integrated radiomics and clinicopathological features that can potentially be used to predict ER after curative ablation in HCC patients.http://www.sciencedirect.com/science/article/pii/S1015958422014099Hepatocellular carcinoma. curative ablation. radiomics-based model |
| spellingShingle | Wei Peng Xinhua Jiang Weidong Zhang Jianmin Hu YaoJun Zhang Ling Zhang A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation Asian Journal of Surgery Hepatocellular carcinoma. curative ablation. radiomics-based model |
| title | A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation |
| title_full | A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation |
| title_fullStr | A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation |
| title_full_unstemmed | A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation |
| title_short | A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation |
| title_sort | radiomics based model can predict recurrence free survival of hepatocellular carcinoma after curative ablation |
| topic | Hepatocellular carcinoma. curative ablation. radiomics-based model |
| url | http://www.sciencedirect.com/science/article/pii/S1015958422014099 |
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