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Purpose <p>To enhance the clinical management of hepatocellular carcinoma (HCC), we explored how conventional ultrasound and pathological features vary with microvascular invasion (MVI) status. We identified key risk factors for MVI and developed a straightforward preoperative model to predict MVI status.</p> Methods <p>In this study, we enrolled 362 patients with HCC confirmed by surgical pathology from three medical institutions, collecting their clinical pathological and ultrasound features. Patients from institutions 1 and 2 were randomly split into training and internal validation sets at a 7:3 ratio, while those from institution 3 served as the external validation set. We used least absolute shrinkage and selection operator (LASSO) and best subset regression (BSR) to select variables for model building. A nomogram was constructed to predict hepatocellular carcinoma MVI, and its accuracy, clinical utility, and discrimination were assessed using ROC, clinical decision curves, and calibration plots.</p> Results <p>A total of 175 patients were included in the study. Of these, 135 patients from institutions 1 and 2 were randomly divided into a training set (95 patients) and an internal validation set (40 patients). Among them, 57.0% (77/135) were MVI-negative, and 43.0% (58/135) were MVI-positive. Only total cholesterol (TC) levels were significantly higher in the MVI-positive group (<i>p</i>&lt;0.05). No other laboratory or conventional ultrasound features showed significant differences between the two groups. Using BSR and LASSO regression analyses, we identified four independent risk factors for MVI from conventional ultrasound features: margin, shape, echogenicity, and relationship to the capsule. A nomogram was created based on these four features. The area under the receiver operating characteristic curve (AUC) for the MVI prediction model was 0.881 (95% CI: 0.815–0.948) in the training set, 0.790 (95% CI: 0.644–0.936) in the internal validation set, and 0.823 (95% CI: 0.692–0.953) in the external validation set.</p> Conclusions <p>Conventional ultrasound features can accurately predict MVI in HCC. A predictive model based on these features offers a simple and effective tool for assessing the invasiveness of liver cancer in clinical settings.</p>

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Predicting microvascular invasion in hepatocellular carcinoma using US features combined with clinicopathological characteristics: a multicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Li Yang,
  • Tingting Li,
  • Lingjie Wang,
  • Liping Liu,
  • Jianhong Wang,
  • Yanhong Hao

摘要

Purpose

To enhance the clinical management of hepatocellular carcinoma (HCC), we explored how conventional ultrasound and pathological features vary with microvascular invasion (MVI) status. We identified key risk factors for MVI and developed a straightforward preoperative model to predict MVI status.

Methods

In this study, we enrolled 362 patients with HCC confirmed by surgical pathology from three medical institutions, collecting their clinical pathological and ultrasound features. Patients from institutions 1 and 2 were randomly split into training and internal validation sets at a 7:3 ratio, while those from institution 3 served as the external validation set. We used least absolute shrinkage and selection operator (LASSO) and best subset regression (BSR) to select variables for model building. A nomogram was constructed to predict hepatocellular carcinoma MVI, and its accuracy, clinical utility, and discrimination were assessed using ROC, clinical decision curves, and calibration plots.

Results

A total of 175 patients were included in the study. Of these, 135 patients from institutions 1 and 2 were randomly divided into a training set (95 patients) and an internal validation set (40 patients). Among them, 57.0% (77/135) were MVI-negative, and 43.0% (58/135) were MVI-positive. Only total cholesterol (TC) levels were significantly higher in the MVI-positive group (p<0.05). No other laboratory or conventional ultrasound features showed significant differences between the two groups. Using BSR and LASSO regression analyses, we identified four independent risk factors for MVI from conventional ultrasound features: margin, shape, echogenicity, and relationship to the capsule. A nomogram was created based on these four features. The area under the receiver operating characteristic curve (AUC) for the MVI prediction model was 0.881 (95% CI: 0.815–0.948) in the training set, 0.790 (95% CI: 0.644–0.936) in the internal validation set, and 0.823 (95% CI: 0.692–0.953) in the external validation set.

Conclusions

Conventional ultrasound features can accurately predict MVI in HCC. A predictive model based on these features offers a simple and effective tool for assessing the invasiveness of liver cancer in clinical settings.