Diagnostic efficacy of a clinical-ultrasound radiomics-based machine learning model in preoperative differentiation of combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma
ZHANG Xiumei
MA Huihui
HAN Sen
ZONG Ruilong
Abstract：Objective To explore the diagnostic efficacy of a machine learning model based on clinically-ultrasound radiomics in preoperatively differentiating combined hepatocellular-cholangiocarcinoma(cHCC-CC)from hepatocellular carcinoma(HCC).Methods A retrospective analysis was conducted on 42 patients with pathologically confirmed cHCC-CC in Xuzhou Central Hospital from January 2010 to October 2024.The control group consisted of 84 patients with pathologically confirmed HCC during the same period,selected using propensity score matching at a 1:2 ratio.Radiomic features were extracted from both the tumor and peritumoral regions,and the Rad-score was calculated.Independent risk factors associated with cHCC-CC were identified through univariate and multivariate logistic regression analyses.Three machine learning algorithms,including support vector machine(SVM),random forest(RF),and logistic regression(LR),were employed to develop predictive models.The model with the highest AUC value was selected as the optimal model.Patients were randomly divided into a training set(n=89)and a testing set(n=37)in a 7:3 ratio,and the performance of the best model was validated using the 10-fold cross-validation method.Results Tumor shape,cirrhosis,CA19-9 levels,Rad-scoretumor,and Rad-score10 mm were identified as independent factors for differentiating the two tumor types.Among the three machine learning models,the LR model demonstrated the best performance,achieving an AUC of 0.883(95%CI:0.826-0.951).The LR model achieved AUCs of 0.888(95%CI:0.805-0.971),0.841(95%CI:0.633-0.994),and 0.893(95%CI:0.793-0.992)on the training,validation,and test sets,respectively.The calibration curve indicated good consistency,and the decision curve analysis revealed a high net benefit.Conclusion The LR model based on clinically-ultrasound radiomics demonstrates significant preoperative diagnostic value in differentiating cHCC-CC from HCC,contributing to precise clinical diagnosis and treatment.
Keywords：machine learningcombined hepatocellular-cholangiocarcinomahepatocellular carcinomamodel
Publication Date：2025-04-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 509-515 )