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Background <p>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).</p> Methods <p>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.</p> Results <p>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, <i>P</i> = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1–1.5, <i>P</i> = 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月14日

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  • Xinyu Guo,
  • Jingjing Song,
  • Lingyi Zhu,
  • Shuang Liu,
  • Chaoming Huang,
  • Lingling Zhou,
  • Weiyue Chen,
  • Guihan Lin,
  • Zhongwei Zhao,
  • Jianfei Tu,
  • Minjiang Chen,
  • Feng Chen,
  • Liyun Zheng,
  • Jiansong Ji

摘要

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.