120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
增强 CT 壳层特征和深度学习用于预测肝细胞癌早期经动脉化疗栓塞难治性
Authors Zhao Q , Zhang W, Wang Z, Liu X, He X, Yang J , Cui L, Leng X
Received 2 March 2026
Accepted for publication 14 April 2026
Published 21 April 2026 Volume 2026:13 605522
DOI https://doi.org/10.2147/JHC.S605522
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Imam Waked
Qinglong Zhao,1 Wei Zhang,2 Zhuo Wang,3 Xingyuan Liu,4 Xinyu He,1 Jiayi Yang,1 Liming Cui,1 Xiaoping Leng3,5,6
1Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 2Department of Interventional Vascular Surgery, General Hospital of Beidahuang Group, Harbin, People’s Republic of China; 3Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 4Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 5Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province (International Cooperation), Harbin, People’s Republic of China; 6State Key Laboratory of Frigid Zone Cardiovascular Diseases, Ministry of Science and Technology, Harbin, People’s Republic of China
Correspondence: Xiaoping Leng, Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China, Email xpleng@ems.hrbmu.edu.cn
Purpose: The aim of this study was to develop and validate a predictive model for early refractoriness to transarterial chemoembolization (TACE)—termed early TACE refractoriness (ETR)—in patients with hepatocellular carcinoma (HCC). The model integrates contrast-enhanced CT (CECT) shell features (annular features at the tumor-liver parenchyma interface) with the Vision-Mamba (Vim) architecture, known for its efficiency in handling high-resolution medical images.
Patients and Methods: This study was a two-center and retrospective study. Patients from center 1 were divided into the training set (n=254) and validation set (n=108), while patients from center 2 were used as the testing set (n=75). A joint model was constructed to predict ETR, and four Vim models without clinical features and 14 machine learning models based on clinical features were also developed for comparison. Model performance was evaluated by the accuracy, area under the curve (AUC), calibration curve, sensitivity, specificity, decision curve analysis (DCA) and Delong test. SHapley Additive exPlanations(SHAP) analysis were used to explain the predictions.
Results: The combined model based on the Vim framework performs better than others. The AUC of the combined model in the training set, validation set and test set were 0.959, 0.956 and 0.942, respectively. The calibration curve and DCA verified the practicality of the combined model in clinical practice. SHAP provides a visual interpretation of the model.
Conclusion: The Vim-based model integrating CECT and shell features shows promise for ETR prediction, offering a preliminary stratification tool. However, it remains a promising step rather than a definitive solution, requiring prospective validation due to the retrospective design and limited validation.
Keywords: hepatocellular carcinoma, transarterial chemoembolization refractoriness, vision-mamba, contrast-enhanced CT, shell feature
1Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 2Department of Interventional Vascular Surgery, General Hospital of Beidahuang Group, Harbin, People’s Republic of China; 3Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 4Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 5Ultrasound Molecular Imaging Joint Laboratory of Heilongjiang Province (International Cooperation), Harbin, People’s Republic of China; 6State Key Laboratory of Frigid Zone Cardiovascular Diseases, Ministry of Science and Technology, Harbin, People’s Republic of China
Correspondence: Xiaoping Leng, Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China, Email xpleng@ems.hrbmu.edu.cn
Purpose: The aim of this study was to develop and validate a predictive model for early refractoriness to transarterial chemoembolization (TACE)—termed early TACE refractoriness (ETR)—in patients with hepatocellular carcinoma (HCC). The model integrates contrast-enhanced CT (CECT) shell features (annular features at the tumor-liver parenchyma interface) with the Vision-Mamba (Vim) architecture, known for its efficiency in handling high-resolution medical images.
Patients and Methods: This study was a two-center and retrospective study. Patients from center 1 were divided into the training set (n=254) and validation set (n=108), while patients from center 2 were used as the testing set (n=75). A joint model was constructed to predict ETR, and four Vim models without clinical features and 14 machine learning models based on clinical features were also developed for comparison. Model performance was evaluated by the accuracy, area under the curve (AUC), calibration curve, sensitivity, specificity, decision curve analysis (DCA) and Delong test. SHapley Additive exPlanations(SHAP) analysis were used to explain the predictions.
Results: The combined model based on the Vim framework performs better than others. The AUC of the combined model in the training set, validation set and test set were 0.959, 0.956 and 0.942, respectively. The calibration curve and DCA verified the practicality of the combined model in clinical practice. SHAP provides a visual interpretation of the model.
Conclusion: The Vim-based model integrating CECT and shell features shows promise for ETR prediction, offering a preliminary stratification tool. However, it remains a promising step rather than a definitive solution, requiring prospective validation due to the retrospective design and limited validation.
Keywords: hepatocellular carcinoma, transarterial chemoembolization refractoriness, vision-mamba, contrast-enhanced CT, shell feature