TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma
Weilang Wang,1,* Qi Zhang,2,* Ying Cui,1,* Shuhang Zhang,1 Binrong Li,1 Tianyi Xia,1 Yang Song,3 Shuwei Zhou,1 Feng Ye,4 Wenbo Xiao,5 Kun Cao,6 Yuan Chi,7 Jinrong Qu,8 Guofeng Zhou,9,10 Zhao Chen,11 Teng Zhang,12 Xunjun Chen,13 Shenghong Ju,1 Yuan-Cheng Wang1 1Department of R...
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Dove Medical Press
2025-01-01
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| Series: | Journal of Hepatocellular Carcinoma |
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| Online Access: | https://www.dovepress.com/trace-model-predicting-treatment-response-to-transarterial-chemoemboli-peer-reviewed-fulltext-article-JHC |
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| author | Wang W Zhang Q Cui Y Zhang S Li B Xia T Song Y Zhou S Ye F Xiao W Cao K Chi Y Qu J Zhou G Chen Z Zhang T Chen X Ju S Wang YC |
| author_facet | Wang W Zhang Q Cui Y Zhang S Li B Xia T Song Y Zhou S Ye F Xiao W Cao K Chi Y Qu J Zhou G Chen Z Zhang T Chen X Ju S Wang YC |
| author_sort | Wang W |
| collection | DOAJ |
| description | Weilang Wang,1,* Qi Zhang,2,* Ying Cui,1,* Shuhang Zhang,1 Binrong Li,1 Tianyi Xia,1 Yang Song,3 Shuwei Zhou,1 Feng Ye,4 Wenbo Xiao,5 Kun Cao,6 Yuan Chi,7 Jinrong Qu,8 Guofeng Zhou,9,10 Zhao Chen,11 Teng Zhang,12 Xunjun Chen,13 Shenghong Ju,1 Yuan-Cheng Wang1 1Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China; 2Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People’s Republic of China; 3MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, People’s Republic of China; 4Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 6Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China; 7Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 8Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 9Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 10Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China; 11Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 12Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, People’s Republic of China; 13Department of Radiology, The People’s Hospital of Xuyi County, Huaian, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuan-Cheng Wang, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, Jiangsu, 210009, People’s Republic of China, Tel +086 25 83272121, Fax +086 25 83311083, Email yuancheng_wang@seu.edu.cnPurpose: To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE.Methods: A total of 108 patients with 160 hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. An external multicenter dataset (ChiCTR2100046020) consisting of 63 patients with 99 hCCs served as the test dataset. Radiomics model was constructed based on the selected features from pretreatment MR images. Univariate and multivariate logistic regression analysis of clinical and radiological factors were used to identify the independent predictors for the 6-month treatment response. A combined model was further constructed by incorporating one-month treatment response, selected clinical and radiological factors and radiomics signature.Results: Among all the clinical and radiological features, only corona enhancement and one-month treatment response were selected. The combined model, named TRACE model (Treatment response at 1 month, RAdiomics and Corona Enhancement), with AUCs of 0.91 (training cohort) and 0.84 (test cohort). The TRACE model demonstrated a significantly higher AUC than the radiomics model (P = 0.001). High-risk and low-risk groups stratified by using the TRACE model also exhibited significant differences in overall survival (OS) (P < 0.001). In contrast, none of the published scoring systems, including ART, SNACOR or ABCR score, demonstrated significant differences between the risk groups in OS prediction.Conclusion: The TRACE model exhibited favorable predictive capability for six-month TACE response, and holds potential as a marker for long-term survival outcomes.Keywords: hepatocellular carcinoma, treatment response, transarterial chemoembolization, radiomics |
| format | Article |
| id | doaj-art-3705c0f90fd3448d94e3888c842b7ec4 |
| institution | Kabale University |
| issn | 2253-5969 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Hepatocellular Carcinoma |
| spelling | doaj-art-3705c0f90fd3448d94e3888c842b7ec42025-01-30T18:07:17ZengDove Medical PressJournal of Hepatocellular Carcinoma2253-59692025-01-01Volume 1219320399700TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular CarcinomaWang WZhang QCui YZhang SLi BXia TSong YZhou SYe FXiao WCao KChi YQu JZhou GChen ZZhang TChen XJu SWang YCWeilang Wang,1,* Qi Zhang,2,* Ying Cui,1,* Shuhang Zhang,1 Binrong Li,1 Tianyi Xia,1 Yang Song,3 Shuwei Zhou,1 Feng Ye,4 Wenbo Xiao,5 Kun Cao,6 Yuan Chi,7 Jinrong Qu,8 Guofeng Zhou,9,10 Zhao Chen,11 Teng Zhang,12 Xunjun Chen,13 Shenghong Ju,1 Yuan-Cheng Wang1 1Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China; 2Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, People’s Republic of China; 3MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, People’s Republic of China; 4Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China; 6Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, People’s Republic of China; 7Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 8Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 9Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 10Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China; 11Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 12Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, People’s Republic of China; 13Department of Radiology, The People’s Hospital of Xuyi County, Huaian, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuan-Cheng Wang, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, Jiangsu, 210009, People’s Republic of China, Tel +086 25 83272121, Fax +086 25 83311083, Email yuancheng_wang@seu.edu.cnPurpose: To develop and validate a predictive model for predicting six-month outcome by integrating pretreatment MRI features and one-month treatment response after TACE.Methods: A total of 108 patients with 160 hCCs from a single-arm, multicenter clinical trial (NCT03113955) were analyzed and served as the training cohort. An external multicenter dataset (ChiCTR2100046020) consisting of 63 patients with 99 hCCs served as the test dataset. Radiomics model was constructed based on the selected features from pretreatment MR images. Univariate and multivariate logistic regression analysis of clinical and radiological factors were used to identify the independent predictors for the 6-month treatment response. A combined model was further constructed by incorporating one-month treatment response, selected clinical and radiological factors and radiomics signature.Results: Among all the clinical and radiological features, only corona enhancement and one-month treatment response were selected. The combined model, named TRACE model (Treatment response at 1 month, RAdiomics and Corona Enhancement), with AUCs of 0.91 (training cohort) and 0.84 (test cohort). The TRACE model demonstrated a significantly higher AUC than the radiomics model (P = 0.001). High-risk and low-risk groups stratified by using the TRACE model also exhibited significant differences in overall survival (OS) (P < 0.001). In contrast, none of the published scoring systems, including ART, SNACOR or ABCR score, demonstrated significant differences between the risk groups in OS prediction.Conclusion: The TRACE model exhibited favorable predictive capability for six-month TACE response, and holds potential as a marker for long-term survival outcomes.Keywords: hepatocellular carcinoma, treatment response, transarterial chemoembolization, radiomicshttps://www.dovepress.com/trace-model-predicting-treatment-response-to-transarterial-chemoemboli-peer-reviewed-fulltext-article-JHChepatocellular carcinomatreatment responsetransarterial chemoembolizationradiomics. |
| spellingShingle | Wang W Zhang Q Cui Y Zhang S Li B Xia T Song Y Zhou S Ye F Xiao W Cao K Chi Y Qu J Zhou G Chen Z Zhang T Chen X Ju S Wang YC TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma Journal of Hepatocellular Carcinoma hepatocellular carcinoma treatment response transarterial chemoembolization radiomics. |
| title | TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma |
| title_full | TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma |
| title_fullStr | TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma |
| title_full_unstemmed | TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma |
| title_short | TRACE Model: Predicting Treatment Response to Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma |
| title_sort | trace model predicting treatment response to transarterial chemoembolization in unresectable hepatocellular carcinoma |
| topic | hepatocellular carcinoma treatment response transarterial chemoembolization radiomics. |
| url | https://www.dovepress.com/trace-model-predicting-treatment-response-to-transarterial-chemoemboli-peer-reviewed-fulltext-article-JHC |
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