Pretreatment arterial-phase CT-based prediction of time to progression after transarterial chemoembolization in hepatocellular carcinoma: a multicenter study

Despite the widespread use of transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC), clinical outcomes vary substantially, and pretreatment tools for identifying patients at high risk of early progression remain limited. This study aimed to develop and externally validate an integrated CT-clinical model for predicting time to progression (TTP) after TACE. This retrospective multicenter study included 508 patients from three cohorts. The public WAW-TACE dataset ( \(n = 226\) ) was used for model development and was divided into a training cohort ( \(n = 158\) ) and an internal validation cohort ( \(n = 68\) ). Two institutional cohorts, cohort A ( \(n = 252\) ) and cohort B ( \(n = 30\) ), were used for independent external validation. Eligible patients had treatment-naive unresectable HCC treated with conventional TACE as the initial therapy, preserved liver function, no extrahepatic metastasis, no major vascular invasion, and available pretreatment contrast-enhanced CT. Clinical variables, radiomics features, and 2·5D deep learning features derived from arterial-phase CT were integrated to construct a time-to-progression prediction network (TTP-Net). TTP-Net achieved the highest observed performance among the compared models, achieving C-indices of 0.719, 0.723, 0.713, and 0.707 in the training cohort, internal validation cohort, external cohort A, and external cohort B, respectively. The corresponding 12-month AUCs were 0.872, 0.862, 0.816, and 0.832. Kaplan-Meier analysis showed significant separation between high-risk and low-risk groups in all four cohorts. Twelve-month decision-curve analysis further supported its potential clinical utility. Integration of pretreatment arterial-phase CT-derived imaging features and clinical variables enabled identification of patients with HCC at high risk of early progression after the first TACE session, supporting individualized surveillance planning and timely treatment reassessment after TACE.

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Journal
Cancer Imaging
Published
2026-09-30
DOI
https://doi.org/10.1186/s40644-026-01136-3
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
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article
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article

Pretreatment arterial-phase CT-based prediction of time to progression after transarterial chemoembolization in hepatocellular carcinoma: a multicenter study

Ziyang Peng, Yongbo Wang, Xuyan Zhao, Yi Lyu et al.
Cancer Imaging
Hepatocellular Carcinoma Treatment and Prognosis
article

Pretreatment arterial-phase CT-based prediction of time to progression after transarterial chemoembolization in hepatocellular carcinoma: a multicenter study

Ziyang Peng, Yongbo Wang, Xuyan Zhao, Yi Lyu, Rongqian Wu, Zheng Wu, Lei Wang, Can Xia
article en

Abstract

Despite the widespread use of transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC), clinical outcomes vary substantially, and pretreatment tools for identifying patients at high risk of early progression remain limited. This study aimed to develop and externally validate an integrated CT-clinical model for predicting time to progression (TTP) after TACE. This retrospective multicenter study included 508 patients from three cohorts. The public WAW-TACE dataset ( \(n = 226\) ) was used for model development and was divided into a training cohort ( \(n = 158\) ) and an internal validation cohort ( \(n = 68\) ). Two institutional cohorts, cohort A ( \(n = 252\) ) and cohort B ( \(n = 30\) ), were used for independent external validation. Eligible patients had treatment-naive unresectable HCC treated with conventional TACE as the initial therapy, preserved liver function, no extrahepatic metastasis, no major vascular invasion, and available pretreatment contrast-enhanced CT. Clinical variables, radiomics features, and 2·5D deep learning features derived from arterial-phase CT were integrated to construct a time-to-progression prediction network (TTP-Net). TTP-Net achieved the highest observed performance among the compared models, achieving C-indices of 0.719, 0.723, 0.713, and 0.707 in the training cohort, internal validation cohort, external cohort A, and external cohort B, respectively. The corresponding 12-month AUCs were 0.872, 0.862, 0.816, and 0.832. Kaplan-Meier analysis showed significant separation between high-risk and low-risk groups in all four cohorts. Twelve-month decision-curve analysis further supported its potential clinical utility. Integration of pretreatment arterial-phase CT-derived imaging features and clinical variables enabled identification of patients with HCC at high risk of early progression after the first TACE session, supporting individualized surveillance planning and timely treatment reassessment after TACE.

Cancer Imaging
Sun Yat-sen University (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN), Second Affiliated Hospital of Xi'an Jiaotong University (CN), Sun Yat-sen University Cancer Center (CN), Xi'an Jiaotong University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 13%
Hepatocellular Carcinoma Treatment and Prognosis
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