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Objectives <p>This study aims to develop a predictive model that combines MRI radiomics features with clinical factors to anticipate the preoperative presence of Vessels Encapsulating Tumor Clusters (VETC) and early recurrence in hepatocellular carcinoma (HCC) with tumors ≤ 3&#xa0;cm.</p> Methods <p>A total of 336 patients were included in the study, with 271 from Hospital 1, divided into a training group (<i>n</i> = 192) and a validation group (<i>n</i> = 79). An additional 65 patients from Hospital 2 were used as an external validation group. Key features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analyses. Clinical, radiomics, and combined clinical radiomics models were then constructed using machine learning algorithms. Model performance was evaluated using the Area Under the Roc Curve (AUC), calibration curves, and decision curves. Kaplan-Meier survival analysis assessed early recurrence in VETC positive and VETC negative HCC patients with tumors ≤ 3&#xa0;cm.</p> Results <p>Both the clinical model utilizing logistic regression with GGT, AFP, non-smooth margin, and arterial peritumoral enhancement as independent risk factors, and the radiomics model incorporating 15 radiomics features with logistic regression, exhibited superior AUC values across the three groups (training, test, and external validation) when compared to other algorithms. Notably, the clinical radiomics model exhibited higher AUC values in the training group (0.885), validation group (0.867), and external validation group (0.849) compared to the standalone clinical and radiomics models. There was a significant difference (<i>p</i> &lt; 0.05) in predicting early recurrence between VETC positive and VETC negative HCC patients (≤ 3&#xa0;cm) when using the clinical radiomics models.</p> Conclusions <p>The clinical radiomics model demonstrates robust performance in predicting VETC positivity in HCC patients (≤ 3&#xa0;cm), thereby holding the potential to forecast early recurrence in these patients.</p>

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MRI radiomics combined with clinical factors to predict vessels encapsulating tumor clusters and early recurrence in hepatocellular carcinoma (≤ 3 cm)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Huilin Chen,
  • Wenjie Zou,
  • Ruilin He,
  • Qingri Wang,
  • Ningyang Jia,
  • Wanmin Liu,
  • Peijun Wang

摘要

Objectives

This study aims to develop a predictive model that combines MRI radiomics features with clinical factors to anticipate the preoperative presence of Vessels Encapsulating Tumor Clusters (VETC) and early recurrence in hepatocellular carcinoma (HCC) with tumors ≤ 3 cm.

Methods

A total of 336 patients were included in the study, with 271 from Hospital 1, divided into a training group (n = 192) and a validation group (n = 79). An additional 65 patients from Hospital 2 were used as an external validation group. Key features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analyses. Clinical, radiomics, and combined clinical radiomics models were then constructed using machine learning algorithms. Model performance was evaluated using the Area Under the Roc Curve (AUC), calibration curves, and decision curves. Kaplan-Meier survival analysis assessed early recurrence in VETC positive and VETC negative HCC patients with tumors ≤ 3 cm.

Results

Both the clinical model utilizing logistic regression with GGT, AFP, non-smooth margin, and arterial peritumoral enhancement as independent risk factors, and the radiomics model incorporating 15 radiomics features with logistic regression, exhibited superior AUC values across the three groups (training, test, and external validation) when compared to other algorithms. Notably, the clinical radiomics model exhibited higher AUC values in the training group (0.885), validation group (0.867), and external validation group (0.849) compared to the standalone clinical and radiomics models. There was a significant difference (p < 0.05) in predicting early recurrence between VETC positive and VETC negative HCC patients (≤ 3 cm) when using the clinical radiomics models.

Conclusions

The clinical radiomics model demonstrates robust performance in predicting VETC positivity in HCC patients (≤ 3 cm), thereby holding the potential to forecast early recurrence in these patients.