CT radiomics model for predicting the three-year survival time of primary hepatocellular carcinoma

Abstract

Objective To explore the value of CT radiomics model in predicting three-year survival time in patients with primary hepatocellular carcinoma (HCC). Methods Eighty one patients pathologically or clinically confirmed HCC and B stageof Barcelona clinical liver cancer before transcatheter arterial chemoembolization (TACE) in Zhejiang Cancer Hospitalwere retrospectively enrolled from January 2010 to June 2014.A primary cohort consisted of 64 patients and an independent validation cohort consisted of 17 patients. The patients were divided into survival group of 39 cases and death groupof 42 cases duringthree-year follow-up. All the patients underwentnon-enhanced and contrast-enhanced CTimages scan before TACE. Three hundered and seventy six quantization radiomics features were extracted from the arterial phase and portal phase CTimages of target lesion. LASSO regression model was used for data dimension reduction. Logistic regression was used to develop the prediction model. The predictive ability of the model was validated using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The radiomics features selected from the arterial and portal phase were 8 and 5, respectively. The arterial prediction model showed AUC=0.833,sensitivity=83.9%(26/31),specificity=81.8%(27/33), accuracy=82.8%(53/64)in primary datasetand AUC=0.861, sensitivity=75.0%(6/8), specificity=100.0% (9/9), accuracy=88.2%(15/17)in independent validation dataset.The portal prediction model showed AUC=0.858,sensitivity=83.3%(25/30),specificity=85.3%(29/34), accuracy=84.4%(54/64)in primary dataset and AUC=0.750, sensitivity=75.0%(6/8), specificity=100.0%(9/9), accuracy=88.2(15/17)in independent validation dataset. Conclusion This study shows CT radiomics model can be conveniently used to facilitate the preoperative individualized prediction of three-year survival time in patients with HCC. Key words: Carcinoma, hepatocellular; Radiomics; Artificial intelligence