Objective To explore the application value of preoperative 45 keV single energy spectral CT imaging com-bined with machine learning in predicting short-term efficacy of liver cancer after TACE.Methods Retrospective analysis of 104 patients with pathologically confirmed hepatocellular carcinoma,randomly divided into a training set(71 cases)and a validation set(33 cases).Collect pre treatment energy spectrum CT single energy and conventional CECT images of pa-tients,and extract imaging omics features of lesions.Using LASSO algorithm combined with ten fold cross validation to screen spectral omics features,constructing spectral imaging omics models using five machine learning methods including LR,RF,SVM,XGBoost,KNN,etc.Based on the machine learning method of the best spectral omics model and correspond-ing features,constructing a CECT model,and using multi factor LR analysis to screen clinical data variables to construct a clinical model.The ROC curve is used to evaluate the effectiveness of the model.The DC A curve evaluates the clinical deci-sion-making ability of the best imaging omics model,CECT model,and clinical model.Results A total of 19 feature con-struction models were selected and included.The AUC of the 5 imaging omics models were the training set(0.72,0.68,0.77,0.70,0.71)and the validation set(0.55,0.58,0.74,0.74,0.65),respectively.Based on multiple factor LR screening,two clinical variables were identified as AFP＞400 μg/L and Child grading.The AUC of the clinical model train-ing set and validation set were 0.63 and 0.67,respectively.The AUC of the CECT SVM model was 0.69 in the training set and 0.61 in the validation set.The calibration curve indicates a good fit between the energy spectrum,conventional SVM model,and clinical model.The DCA curve shows that the energy spectrum SVM model has higher clinical application value.Conclusion The combination of preoperative 45 keV single energy spectral CT imaging omics and machine learning can effectively predict the short-term efficacy of liver cancer after TACE.Among them,the energy spectral SVM model has the best predictive performance and is more decisive than other models.
Hepatocellular carcinomaRadiomics45 keV single energy spectrum CTMachine learning
维普
万方数据
