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已发表论文
基于 CT 影像组学的肝细胞癌患者经 TACE 治疗后生存时间预测模型的建立：一项多中心研究
Authors Yang H, Zhao J, Wang Y, Zhu D, Gu J, Ren W
Received 20 April 2025
Accepted for publication 19 September 2025
Published 7 October 2025 Volume 2025:12 Pages 2263—2277
DOI https://doi.org/10.2147/JHC.S535606
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Imam Waked
Han Yang,1,* Juan Zhao,2,* Yingwei Wang,3 Diwen Zhu,1 Junpeng Gu,1 Weixin Ren1
1Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University (Xinjiang Interventional Medicine Clinical Research Center), Xinjiang, Urumqi, 830054, People’s Republic of China; 2Department of Anesthesiology, Sichuan Cancer Hospital & Institute (Sichuan Cancer Center, or Cancer Hospital Affiliated to University of Electronic Scienceand Technology of China), Sichuan, Chengdu, 610041, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Southwest Medical University, Sichuan, Luzhou, 646000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Weixin Ren, Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137 South Li Yu Shan Street, Xinjiang, Urumqi, 830054, People’s Republic of China, Tel +86 18011425277, Email rwx1031@163.com
Purpose: This research constructs a prognostic model for overall survival (OS) in hepatocellular carcinoma (HCC) patients using radiomic features from non-contrast CT scans obtained within 24 hours after transarterial chemoembolization (TACE).
Patients and Methods: Patients were retrospectively enrolled from three institutions to form training (n = 112) and validation (n = 56) cohorts from January 2016 to December 2023. All patients underwent a minimum of three TACE treatment sessions. January 2019 served as the cutoff point for dividing the dataset into training and validation cohorts. Univariate and multivariate Cox regression analyses were employed to obtain clinical variables related to OS for constructing the clinical model. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to construct the radiomics model from lipiodol deposits in the target lesions (TL) within 24 hours after the initial TACE, and the clinical-radiomics model was further constructed. Model prediction performance was subsequently assessed by the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. Additionally, Kaplan-Meier analysis was used to evaluate the model’s value in predicting OS.
Results: The clinical-radiomics model predicted OS at 1, 2, and 3 years more accurately than the clinical or radiomics model alone (training group, AUC = 0.787, 0.765 and 0.827, respectively; validation group, AUC = 0.731, 0.713 and 0.798, respectively). The predicted high-risk subgroup based on the clinical-radiomics model had shorter mOS than predicted low-risk subgroup (training group, 16 m vs 37 m, p = 0.0002; validation group 14 m vs 35 m, p< 0.0001), enabling risk stratification of various clinical subgroups.
Conclusion: The radiomic signature derived from lipiodol within 24 hours post-TACE functions as a prognostic biomarker for OS in HCC patients. The clinical-radiomics model demonstrates robust predictive performance, providing a valuable tool for prognostic evaluation in HCC.
Keywords: hepatocellular carcinoma, radiomics, transcatheter arterial chemoembolization, tomography, x-ray computed, overall survival
1Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University (Xinjiang Interventional Medicine Clinical Research Center), Xinjiang, Urumqi, 830054, People’s Republic of China; 2Department of Anesthesiology, Sichuan Cancer Hospital & Institute (Sichuan Cancer Center, or Cancer Hospital Affiliated to University of Electronic Scienceand Technology of China), Sichuan, Chengdu, 610041, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Southwest Medical University, Sichuan, Luzhou, 646000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Weixin Ren, Department of Interventional Radiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137 South Li Yu Shan Street, Xinjiang, Urumqi, 830054, People’s Republic of China, Tel +86 18011425277, Email rwx1031@163.com
Purpose: This research constructs a prognostic model for overall survival (OS) in hepatocellular carcinoma (HCC) patients using radiomic features from non-contrast CT scans obtained within 24 hours after transarterial chemoembolization (TACE).
Patients and Methods: Patients were retrospectively enrolled from three institutions to form training (n = 112) and validation (n = 56) cohorts from January 2016 to December 2023. All patients underwent a minimum of three TACE treatment sessions. January 2019 served as the cutoff point for dividing the dataset into training and validation cohorts. Univariate and multivariate Cox regression analyses were employed to obtain clinical variables related to OS for constructing the clinical model. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to construct the radiomics model from lipiodol deposits in the target lesions (TL) within 24 hours after the initial TACE, and the clinical-radiomics model was further constructed. Model prediction performance was subsequently assessed by the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curve. Additionally, Kaplan-Meier analysis was used to evaluate the model’s value in predicting OS.
Results: The clinical-radiomics model predicted OS at 1, 2, and 3 years more accurately than the clinical or radiomics model alone (training group, AUC = 0.787, 0.765 and 0.827, respectively; validation group, AUC = 0.731, 0.713 and 0.798, respectively). The predicted high-risk subgroup based on the clinical-radiomics model had shorter mOS than predicted low-risk subgroup (training group, 16 m vs 37 m, p = 0.0002; validation group 14 m vs 35 m, p< 0.0001), enabling risk stratification of various clinical subgroups.
Conclusion: The radiomic signature derived from lipiodol within 24 hours post-TACE functions as a prognostic biomarker for OS in HCC patients. The clinical-radiomics model demonstrates robust predictive performance, providing a valuable tool for prognostic evaluation in HCC.
Keywords: hepatocellular carcinoma, radiomics, transcatheter arterial chemoembolization, tomography, x-ray computed, overall survival