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Purpose <p>This study aims to predict microvascular invasion (MVI) in bifocal hepatocellular carcinoma (bHCC) by analyzing the habitat and radiomic features of the two tumor lesions, and further to establish a prediction model to estimate the prognosis of bHCC patients.</p> Methods <p>183 bHCC patients were enrolled, randomly dividing into a training cohort (<i>n</i> = 146) and a test cohort (<i>n</i> = 37) in an 8:2 ratio. Habitat analysis was performed in two tumors as a whole one to construct the habitat model. The radiomic features were further extracted from the habitat-derived subregions. The logistic regression was used to identify the clinicoradiological variables associated with MVI. The Clinical-Radiological-Habitat (CRH) model combined clinicoradiological and habitat features, while the comprehensive model further integrated habitat-derived radiomics for MVI prediction. Diagnostic performance was assessed using receiver operating characteristic curve analysis, and prognostic analysis was performed using the Kaplan-Meier curves.</p> Results <p>The dual tumors in bHCC patients were analyzed as a whole tumor and divided into two habitats (habitat 1 and 2). The habitat model demonstrated AUCs of 0.817 and 0.700 for assessing MVI in the training and testing cohorts, respectively. The CRH model outperformed the habitat model with the AUCs of 0.837 and 0.766 in the training and testing cohorts. The comprehensive model combined habitat-derived radiomics features on arterial phase and T2-weighted imaging with CRH model and achieved excellent diagnostic performance with AUCs of 0.907 and 0.884 in the training and test cohorts, respectively. The model’s clinical relevance was further supported by calibration and decision curve analysis. Additionally, the model showed high predictive accuracy for survival outcomes in risk stratification.</p> Conclusion <p>The H<sub>1 − AP+T2WI</sub>+CRH model hold promise as a non-invasive tool for predicting MVI and overall survival in bHCC patients, offering a valuable approach for preoperative risk assessment.</p> Clinical trial number <p>Not applicable.</p>

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Habitat-derived radiomics analysis based on dual lesion for the prediction of microvascular invasion in bifocal hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Xi Jia,
  • Fei Wu,
  • Jing Liu,
  • Fang Wang,
  • Yuyao Xiao,
  • Dijia Wu,
  • Chun Yang,
  • Mengsu Zeng

摘要

Purpose

This study aims to predict microvascular invasion (MVI) in bifocal hepatocellular carcinoma (bHCC) by analyzing the habitat and radiomic features of the two tumor lesions, and further to establish a prediction model to estimate the prognosis of bHCC patients.

Methods

183 bHCC patients were enrolled, randomly dividing into a training cohort (n = 146) and a test cohort (n = 37) in an 8:2 ratio. Habitat analysis was performed in two tumors as a whole one to construct the habitat model. The radiomic features were further extracted from the habitat-derived subregions. The logistic regression was used to identify the clinicoradiological variables associated with MVI. The Clinical-Radiological-Habitat (CRH) model combined clinicoradiological and habitat features, while the comprehensive model further integrated habitat-derived radiomics for MVI prediction. Diagnostic performance was assessed using receiver operating characteristic curve analysis, and prognostic analysis was performed using the Kaplan-Meier curves.

Results

The dual tumors in bHCC patients were analyzed as a whole tumor and divided into two habitats (habitat 1 and 2). The habitat model demonstrated AUCs of 0.817 and 0.700 for assessing MVI in the training and testing cohorts, respectively. The CRH model outperformed the habitat model with the AUCs of 0.837 and 0.766 in the training and testing cohorts. The comprehensive model combined habitat-derived radiomics features on arterial phase and T2-weighted imaging with CRH model and achieved excellent diagnostic performance with AUCs of 0.907 and 0.884 in the training and test cohorts, respectively. The model’s clinical relevance was further supported by calibration and decision curve analysis. Additionally, the model showed high predictive accuracy for survival outcomes in risk stratification.

Conclusion

The H1 − AP+T2WI+CRH model hold promise as a non-invasive tool for predicting MVI and overall survival in bHCC patients, offering a valuable approach for preoperative risk assessment.

Clinical trial number

Not applicable.