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. 2025 Apr 14;25(1):683.
doi: 10.1186/s12885-025-14079-y.

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

Xinyu Guo #  1   2 , Jingjing Song #  1 , Lingyi Zhu  3 , Shuang Liu  1   4 , Chaoming Huang  3 , Lingling Zhou  1   2 , Weiyue Chen  1   4 , Guihan Lin  1   4 , Zhongwei Zhao  1   4 , Jianfei Tu  1   4 , Minjiang Chen  1   4 , Feng Chen  2 , Liyun Zheng  5   6 , Jiansong Ji  7   8
Affiliations

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

Xinyu Guo et al. BMC Cancer. .

Abstract

Background: This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

Methods: In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

Results: Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

Conclusion: A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

Keywords: Hepatocellular carcinoma; Nomogram; Postoperative adjuvant transarterial chemoembolization; Radiomics; Recurrence.

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Conflict of interest statement

Declarations. Ethics approval: This retrospective study was received approval by the Ethical Committee of Lishui Hospital of Zhejiang University and was carried out in accordance with the Declaration of Helsinki. Consent to participate: The requirement for informed consent was waived by the Ethical Committee of Lishui Hospital of Zhejiang University due to the retrospective nature of the research. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of patients enrollment
Fig. 2
Fig. 2
Workflow of radiomics analysis
Fig. 3
Fig. 3
Kaplan-Meier survival curves based on Rad-score, NLR and tumor size in the training, internal validation and external cohorts. (a) Kaplan-Meier survival analysis based on Rad-score. (b) Kaplan-Meier survival analysis based on NLR. (c) Kaplan-Meier survival analysis based on tumor size
Fig. 4
Fig. 4
Multivariable Cox analyses of RFS
Fig. 5
Fig. 5
Time-dependent ROC curves of the three different models in the training and validation cohorts. (a) The time-dependent ROC curves based on Rad-score in both the training and cohorts. AUCs at 1-, 3-, and 5-year RFS were calculated from time-dependent ROC curves to evaluate the prognostic accuracy. (b) The time-dependent ROC curves based on NLR and tumor size. (c) The time-dependent ROC curves based on clinical-radiomics model
Fig. 6
Fig. 6
The nomogram and calibration curves based on the clinical-radiomics model. (a) Clinical-Radiomics nomogram for predicting RFS of HCC patients treated with PA-TACE. (b) The calibration curves for the Clinical-Radiomics nomogram in both the training and validation cohorts
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