A multi-temporal MRI radiomics model for early post-ablation recurrence risk stratification in hepatocellular carcinoma

OBJECTIVES: To develop and validate a multi-temporal magnetic resonance imaging (MRI)-based radiomics model for predicting one-year recurrence in hepatocellular carcinoma (HCC) after ablation. METHODS: This retrospective study included 204 patients with HCC who underwent curative ablation between 2016 and 2021. Multi-sequence MRI was performed within one month before and three months after ablation. Radiomics features were extracted from arterial phase (T1A), portal venous phase (T1V), and T2-weighted images using pre-ablation, post-ablation, and delta features. Patients were randomly divided into training (n = 142) and validation (n = 62) cohorts. Models were evaluated using receiver operating characteristic analysis, and differences in AUC between models were assessed. RESULTS: During a median follow-up of 750 days, 50 patients (24.5%) developed recurrence within one year. The integrated radiomics model (IRM) achieved the best discriminatory performance, with area under the curve values of 0.888 (95% CI: 0.801-0.976) in the training cohort and 0.858 (95% CI: 0.718-0.998) in the validation cohort. Kaplan-Meier analysis showed significant differences in recurrence-free survival between IRM-defined low- and high-risk groups in both cohorts (p < 0.001). CONCLUSION: The multi-temporal MRI radiomics model showed promising performance for early recurrence risk stratification after HCC ablation in the internal validation cohort, suggesting potential translational value for non-invasive recurrence-risk assessment. Further prospective external validation is warranted.

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Publication Details

Journal
Translational Oncology
Published
2026-09-05
DOI
https://doi.org/10.1016/j.tranon.2026.103014
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
0.00

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article

A multi-temporal MRI radiomics model for early post-ablation recurrence risk stratification in hepatocellular carcinoma

Junbin Liao, JingXian Shen, Xiaofei Qu, Yihang Xu et al.
Translational Oncology
Hepatocellular Carcinoma Treatment and Prognosis
article

A multi-temporal MRI radiomics model for early post-ablation recurrence risk stratification in hepatocellular carcinoma

Junbin Liao, JingXian Shen, Xiaofei Qu, Yihang Xu, Yuxi Huang, L Peng, Shuo Wang, Manxia Lin, Ruiming Liang, Zixin Huang, Shiyi Liu
article en

Abstract

OBJECTIVES: To develop and validate a multi-temporal magnetic resonance imaging (MRI)-based radiomics model for predicting one-year recurrence in hepatocellular carcinoma (HCC) after ablation. METHODS: This retrospective study included 204 patients with HCC who underwent curative ablation between 2016 and 2021. Multi-sequence MRI was performed within one month before and three months after ablation. Radiomics features were extracted from arterial phase (T1A), portal venous phase (T1V), and T2-weighted images using pre-ablation, post-ablation, and delta features. Patients were randomly divided into training (n = 142) and validation (n = 62) cohorts. Models were evaluated using receiver operating characteristic analysis, and differences in AUC between models were assessed. RESULTS: During a median follow-up of 750 days, 50 patients (24.5%) developed recurrence within one year. The integrated radiomics model (IRM) achieved the best discriminatory performance, with area under the curve values of 0.888 (95% CI: 0.801-0.976) in the training cohort and 0.858 (95% CI: 0.718-0.998) in the validation cohort. Kaplan-Meier analysis showed significant differences in recurrence-free survival between IRM-defined low- and high-risk groups in both cohorts (p < 0.001). CONCLUSION: The multi-temporal MRI radiomics model showed promising performance for early recurrence risk stratification after HCC ablation in the internal validation cohort, suggesting potential translational value for non-invasive recurrence-risk assessment. Further prospective external validation is warranted.

Translational OncologyVol. 73
Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN)
National Natural Science Foundation of China, Sun Yat-sen University, Natural Science Foundation of Guangdong Province, Sun Yat-sen University Cancer Center
Reduced inequalities
Openalex Percentile: Top 13%
Hepatocellular Carcinoma Treatment and Prognosis
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