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Background <p>As a rare subtype of primary liver cancer that contains both hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) components, combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) is characterized by strong invasiveness and poor prognosis. The proportion of HCC and CCA components is closely related to the prognosis of patients. This study aimed to develop a combined model integrating preoperative ultrasound radiomic features and clinical data to non-invasively predict the predominant component in cHCC-CCA.</p> Methods <p>The study included 60 histopathologically confirmed cHCC-CCA patients, divided into high hepatocellular carcinoma percentage (HCC%) group (<i>n</i> = 33, HCC% &gt; 50) and low HCC% group (<i>n</i> = 27, HCC% ≤ 50). A predominant component prediction model for cHCC-CCA was developed using radiomic features extracted from four distinct region of interest (ROI) to identify the optimal radiomic model: tumor alone (ROI<sub>tumor</sub>), tumor plus 5&#xa0;mm peritumoral margin (defined as ROI<sub>tumor</sub>+peritumor 1), tumor plus 10&#xa0;mm peritumoral margin (defined as ROI<sub>tumor</sub>+peritumor 2), and tumor plus 20&#xa0;mm peritumoral margin (defined as ROI<sub>tumor</sub>+peritumor 3). Univariate and multivariate logistic regression analyses were performed to evaluate the predictive value of preoperative clinical parameters as potential indicators of the predominant component predictors. Ultimately, a combined model integrating the optimal radiomic features and clinical data was constructed, with the final predictive model visualized using nomogram.</p> Results <p>The model developed based on ROI<sub>tumor</sub>+peritumor 2 demonstrated optimal predictive performance and was defined as the optimal radiomic model [training set area under the curve (AUC) = 0.853, test set AUC = 0.806, overall set AUC = 0.832]. Through multivariate logistic regression analysis, we identified two preoperative indicators: tumor maximum diameter and total bilirubin. Compared with both the optimal radiomic model and the clinical model, the combined model demonstrated superior predictive capability for the predominant component in preoperative cHCC-CCA patients (training set AUC = 0.877, 95% CI: 0.775–0.980; test set AUC = 0.847, 95% CI: 0.657–1.00; overall set AUC = 0.851, 95% CI: 0.756–0.945). Decision curve analysis demonstrated that the combined model exhibited favorable clinical utility.</p> Conclusions <p>Combination of ultrasound-based radiomics with tumor maximum diameter and total bilirubin levels holds significant value for the preoperative identification of HCC% in cHCC-CCA.</p>

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Combining preoperative clinical and ultrasound radiomic features to predict the predominant component of combined hepatocellular carcinoma-cholangiocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • Yuanping Yang,
  • Weiming Liang,
  • Jinshu Pang,
  • Xiumei Bai,
  • Rong Wen,
  • Yuquan Wu,
  • Jinbo Peng,
  • Hong Yang,
  • Yun He,
  • Ruizhi Gao

摘要

Background

As a rare subtype of primary liver cancer that contains both hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) components, combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) is characterized by strong invasiveness and poor prognosis. The proportion of HCC and CCA components is closely related to the prognosis of patients. This study aimed to develop a combined model integrating preoperative ultrasound radiomic features and clinical data to non-invasively predict the predominant component in cHCC-CCA.

Methods

The study included 60 histopathologically confirmed cHCC-CCA patients, divided into high hepatocellular carcinoma percentage (HCC%) group (n = 33, HCC% > 50) and low HCC% group (n = 27, HCC% ≤ 50). A predominant component prediction model for cHCC-CCA was developed using radiomic features extracted from four distinct region of interest (ROI) to identify the optimal radiomic model: tumor alone (ROItumor), tumor plus 5 mm peritumoral margin (defined as ROItumor+peritumor 1), tumor plus 10 mm peritumoral margin (defined as ROItumor+peritumor 2), and tumor plus 20 mm peritumoral margin (defined as ROItumor+peritumor 3). Univariate and multivariate logistic regression analyses were performed to evaluate the predictive value of preoperative clinical parameters as potential indicators of the predominant component predictors. Ultimately, a combined model integrating the optimal radiomic features and clinical data was constructed, with the final predictive model visualized using nomogram.

Results

The model developed based on ROItumor+peritumor 2 demonstrated optimal predictive performance and was defined as the optimal radiomic model [training set area under the curve (AUC) = 0.853, test set AUC = 0.806, overall set AUC = 0.832]. Through multivariate logistic regression analysis, we identified two preoperative indicators: tumor maximum diameter and total bilirubin. Compared with both the optimal radiomic model and the clinical model, the combined model demonstrated superior predictive capability for the predominant component in preoperative cHCC-CCA patients (training set AUC = 0.877, 95% CI: 0.775–0.980; test set AUC = 0.847, 95% CI: 0.657–1.00; overall set AUC = 0.851, 95% CI: 0.756–0.945). Decision curve analysis demonstrated that the combined model exhibited favorable clinical utility.

Conclusions

Combination of ultrasound-based radiomics with tumor maximum diameter and total bilirubin levels holds significant value for the preoperative identification of HCC% in cHCC-CCA.