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已发表论文
基于磁共振成像的深度学习和影像组学列线图预测热消融术后 6 个月内肝细胞癌复发
Authors Chen Y, Zhao Y, Guan W, Wu D , Zheng L, Chen C, Geng X, Qi H , Song HY, Hu H
Received 19 May 2025
Accepted for publication 23 September 2025
Published 6 October 2025 Volume 2025:12 Pages 2247—2261
DOI https://doi.org/10.2147/JHC.S541329
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Ali Hosni
Yao Chen,1 Yanan Zhao,1 Weiwei Guan,1 Di Wu,2 Lin Zheng,1 Chengshi Chen,1 Xiang Geng,1 Han Qi,3 Ho-Young Song,1 Hongtao Hu1
1Department of Interventional Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 2Department of Radiology, People’s Hospital of Zhengzhou, Zhengzhou, Henan, People’s Republic of China; 3Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China
Correspondence: Hongtao Hu, Department of Interventional Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, People’s Republic of China, Email huhongtaogy@163.com
Purpose: Develop a magnetic resonance imaging (MRI)-based deep learning (DL)-radiomics (Rad)-clinical nomogram for predicting early recurrence of hepatocellular carcinoma (HCC) within six months after thermal ablation.
Materials and Methods: Barcelona Clinic Liver Cancer (BCLC) stage 0-A HCC patients who underwent dynamic contrast-enhanced MRI before ablation were retrospectively included. Patients were categorized into non early recurrence and early recurrence groups. A clinical model was constructed through logistic regression analysis of clinical information and radiological features. DL score model and Rad score model were developed using DL features and manual features extracted from dynamic contrast-enhanced MRI, with principal component analysis and least absolute shrinkage and selection operator regression methods. The DL-Rad-Clinical nomogram was constructed through logistic regression analysis. The model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC).
Results: A total of 224 patients were included in this study (training set: n = 156; test set: n = 68). The DL-Rad-Clinical nomogram was constructed, including Rad score, DL score, natural logarithm alpha-fetoprotein (LnAFP), and multiple low signal lesions as predictive factors. In the training set, the DL-Rad-Clinical nomogram demonstrated better predictive performance (AUC = 0.896, P < 0.05). In the test set, the DL-Rad-Clinical nomogram had a higher AUC value compared to other models, although the difference was not statistically significant (AUC = 0.774, P > 0.05).
Conclusion: The DL-Rad-Clinical nomogram helped in identifying HCC patients with early recurrence within six months following thermal ablation.
Keywords: HCC, prediction, clinical information, radiological features
1Department of Interventional Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, People’s Republic of China; 2Department of Radiology, People’s Hospital of Zhengzhou, Zhengzhou, Henan, People’s Republic of China; 3Department of Minimally Invasive Interventional Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China
Correspondence: Hongtao Hu, Department of Interventional Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, People’s Republic of China, Email huhongtaogy@163.com
Purpose: Develop a magnetic resonance imaging (MRI)-based deep learning (DL)-radiomics (Rad)-clinical nomogram for predicting early recurrence of hepatocellular carcinoma (HCC) within six months after thermal ablation.
Materials and Methods: Barcelona Clinic Liver Cancer (BCLC) stage 0-A HCC patients who underwent dynamic contrast-enhanced MRI before ablation were retrospectively included. Patients were categorized into non early recurrence and early recurrence groups. A clinical model was constructed through logistic regression analysis of clinical information and radiological features. DL score model and Rad score model were developed using DL features and manual features extracted from dynamic contrast-enhanced MRI, with principal component analysis and least absolute shrinkage and selection operator regression methods. The DL-Rad-Clinical nomogram was constructed through logistic regression analysis. The model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC).
Results: A total of 224 patients were included in this study (training set: n = 156; test set: n = 68). The DL-Rad-Clinical nomogram was constructed, including Rad score, DL score, natural logarithm alpha-fetoprotein (LnAFP), and multiple low signal lesions as predictive factors. In the training set, the DL-Rad-Clinical nomogram demonstrated better predictive performance (AUC = 0.896, P < 0.05). In the test set, the DL-Rad-Clinical nomogram had a higher AUC value compared to other models, although the difference was not statistically significant (AUC = 0.774, P > 0.05).
Conclusion: The DL-Rad-Clinical nomogram helped in identifying HCC patients with early recurrence within six months following thermal ablation.
Keywords: HCC, prediction, clinical information, radiological features