18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Background: Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of F-18-fluorodeoxyglucose positron emission tomography/computed tomography (F-18-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI. Methods: In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models. Results: For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve ( AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively). Conclusions: F-18-FDG PET/ CT radiomic features integrating clinical factors have potentials in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.

Automated summary

This study explores the contribution of F-18-FDG PET/CT radiomic features for the preoperative prediction of HCC and ICC classification and MVI. The results show that the prediction model has high accuracy and sensitivity in predicting HCC and ICC classification, and PET features play a dominant role in the predictive power of the model. This prediction model provides a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.