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Multicenter Study
. 2025 Dec;19(6):1397-1411.
doi: 10.1007/s12072-025-10877-5. Epub 2025 Jul 23.

CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study

Zihao Liu #  1 , Xinge Li #  2 , Yong Huang  3 , Xu Chang  1 , Hong Zhang  3 , Xiaodong Wu  3 , Yanzhao Diao  4 , Fengling He  5 , Junyong Sun  3 , Baomin Feng  3 , Hexin Liang  6
Affiliations
Multicenter Study

CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study

Zihao Liu et al. Hepatol Int. 2025 Dec.

Abstract

Background: Noninvasive and precise tools for treatment response estimation in patients with high-risk hepatocellular carcinoma (HCC) who could benefit from hepatic arterial infusion chemotherapy (HAIC) plus lenvatinib and humanized programmed death receptor-1 inhibitors (PD-1) (HAIC-LEN-PD1) are lacking. This study aimed to evaluate the predictive potential of intratumoral and peritumoral radiomics for preoperative treatment response assessment to HAIC-LEN-PD1 in high-risk HCC cases.

Materials and methods: Totally 630 high-risk HCC cases administered HAIC-LEN-PD1 at three institutions were retrospectively identified and assigned to training, validation and external test sets. Totally 1834 radiomic features were, respectively, obtained from intratumoral and peritumoral regions and radiomics models were established using five classifiers. Based on the optimal model, a nomogram was developed and evaluated using areas under the curves (AUCs), calibration curves and decision curve analysis (DCA). Overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier curves.

Results: The Intratumoral + Peritumoral 10 mm (Intra + Peri10) radiomics models were superior to the intratumor models and peritumor models, with AUCs of 0.919 (95%CI 0.889-0.949) in the training set, 0.874 (95%CI 0.812-0.936) in validation set and 0.893 (95%CI 0.839-0.948) in external test sets. The nomogram had good calibration ability and clinical value, with the AUCs of 0.936 (95%CI 0.907-0.965) in the training set, 0.878 (95%CI 0.916-0.940) in validation set and 0.902 (95%CI 0.848-0.957) in external test sets. The Kaplan-Meier analysis showed that high-score patients had significantly shorter OS and PFS than the low-score patients (median OS: 11.7 vs. 29.6 months, the whole set, p < 0.001; median PFS: 6.0 vs. 12.0 months, the whole set, p < 0.001).

Conclusions: The Intra + Peri10 model can effectively predict the treatment response of high-risk HCC cases administered HAIC-LEN-PD1. The nomogram could provide an effective tool to evaluate the treatment response and risk stratification.

Keywords: Decision curve analyses; Hepatic arterial infusion chemotherapy; High-risk hepatocellular carcinoma; Intratumoral and peritumoral; Lenvatinib; Machine learning; Nomogram; Radiomics; Risk stratification; Treatment response.

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Conflict of interest statement

Declarations. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose. Ethical approval: This study was approved by the ethics committee (Ethics Approval ID: SDTHEC 2024007021) of each of participating centers, and the requirement for informed consent was waived, since this was a retrospective study. All procedures involving human participants were followed in accordance with the Declaration of Helsinki and its subsequent amendments.

Comment in

References

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