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已发表论文
基于 CT 的影像组学特征结合甲胎蛋白预测肝细胞癌肿瘤簇血管包绕及预后
Authors Wei Y , Huang S, Huang L, Pei W, Zuo Y, Liao H
Received 22 May 2025
Accepted for publication 9 September 2025
Published 13 September 2025 Volume 2025:12 Pages 2069—2081
DOI https://doi.org/10.2147/JHC.S542092
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Ahmed Kaseb
Yunyun Wei,1,2,* Shiyuan Huang,3,* Luyu Huang,4,* Wei Pei,1,2 Yang Zuo,1,2 Hai Liao1,2
1Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 2Guangxi Key Clinical Specialty Department (Medical imaging Department), Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 3Hospital Administrative Department, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, People’s Republic of China; 4Department of Pathology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hai Liao, Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; Guangxi Key Clinical Specialty Department (Medical imaging Department), Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China, Tel +86-771-5334951, Email 42442427@qq.com
Objective: This study aims to develop a CT-based radiomics nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma (HCC).
Patients and Methods: The retrospective, single-center study included 231 (77 VETC+ and 154 VETC-) HCC patients who underwent preoperative CT scan, and were randomly divided into training and validation cohorts at a 7:3 ratio. Radiomics features were extracted from CT images during the plain, arterial and venous phases. These features were then selected using the Least Absolute Shrinkage and Selection Operator (LASSO). Predictive factors were chosen through univariate and multivariate logistic regression. A prognostic nomogram integrating clinical factor and radiomics features was developed and validated. The model’s predictive accuracy was systematically evaluated using the area under the receiver operating characteristic curve (AUC), while calibration curves assessed agreement between predicted and observed outcomes. To quantify clinical utility, decision curve analysis (DCA) was implemented. Furthermore, the model’s prognostic performance for postoperative disease-free survival (DFS) was examined through Kaplan-Meier analysis.
Results: The nomogram integrating four radiomics features and alpha-fetoprotein (AFP) exhibited robust predictive performance, with AUC values of 0.782 (95% confidence interval [CI]: 0.708– 0.856) in the training cohort and 0.755 (95% CI: 0.628– 0.882) in the validation cohort. Calibration curves demonstrated excellent agreement between predicted and observed outcomes in both cohorts. DCA revealed significant clinical utility of the nomogram. Additionally, the model-stratified VETC+ HCC patients showed significantly worse DFS compared to VETC- counterparts (log-rank p = 0.035).
Conclusion: The CT-based radiomics nomogram, integrating radiomics features and AFP, provides a non-invasive and reliable tool for predicting VETC and stratifying prognosis in HCC patients.
Keywords: hepatocellular carcinoma, radiomics, computed tomography, vessels encapsulating tumor clusters, prognosis
1Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 2Guangxi Key Clinical Specialty Department (Medical imaging Department), Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 3Hospital Administrative Department, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, People’s Republic of China; 4Department of Pathology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hai Liao, Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; Guangxi Key Clinical Specialty Department (Medical imaging Department), Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China, Tel +86-771-5334951, Email 42442427@qq.com
Objective: This study aims to develop a CT-based radiomics nomogram for preoperative prediction of vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma (HCC).
Patients and Methods: The retrospective, single-center study included 231 (77 VETC+ and 154 VETC-) HCC patients who underwent preoperative CT scan, and were randomly divided into training and validation cohorts at a 7:3 ratio. Radiomics features were extracted from CT images during the plain, arterial and venous phases. These features were then selected using the Least Absolute Shrinkage and Selection Operator (LASSO). Predictive factors were chosen through univariate and multivariate logistic regression. A prognostic nomogram integrating clinical factor and radiomics features was developed and validated. The model’s predictive accuracy was systematically evaluated using the area under the receiver operating characteristic curve (AUC), while calibration curves assessed agreement between predicted and observed outcomes. To quantify clinical utility, decision curve analysis (DCA) was implemented. Furthermore, the model’s prognostic performance for postoperative disease-free survival (DFS) was examined through Kaplan-Meier analysis.
Results: The nomogram integrating four radiomics features and alpha-fetoprotein (AFP) exhibited robust predictive performance, with AUC values of 0.782 (95% confidence interval [CI]: 0.708– 0.856) in the training cohort and 0.755 (95% CI: 0.628– 0.882) in the validation cohort. Calibration curves demonstrated excellent agreement between predicted and observed outcomes in both cohorts. DCA revealed significant clinical utility of the nomogram. Additionally, the model-stratified VETC+ HCC patients showed significantly worse DFS compared to VETC- counterparts (log-rank p = 0.035).
Conclusion: The CT-based radiomics nomogram, integrating radiomics features and AFP, provides a non-invasive and reliable tool for predicting VETC and stratifying prognosis in HCC patients.
Keywords: hepatocellular carcinoma, radiomics, computed tomography, vessels encapsulating tumor clusters, prognosis