Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma.

Abstract

Most patients recur after resection of intrahepatic cholangiocarcinoma (IHC). We studied whether machine-learning incorporating radiomics and tumor size could predict intrahepatic recurrence within 1-year. This was a retrospective analysis of patients with IHC resected between 2000 and 2017 who had evaluable computed tomography imaging. Texture features (TFs) were extracted from the liver, tumor, and future liver remnant (FLR). Random forest classification using training (70.3%) and validation cohorts (29.7%) was used to design a predictive model. 138 patients were included for analysis. Patients with early recurrence had a larger tumor size (7.25cm [IQR 5.2-8.9] vs. 5.3cm [IQR 4.0-7.2], P=0.011) and a higher rate of lymph node metastasis (28.6% vs. 11.6%, P=0.041), but were not more likely to have multifocal disease (21.4% vs. 17.4%, P=0.643). Three TFs from the tumor, FD1, FD30, and IH4 and one from the FLR, ACM15, were identified by feature selection. Incorporation of TFs and tumor size achieved the highest AUC of 0.84 (95% CI 0.73-0.95) in predicting recurrence in the validation cohort. This study demonstrates that radiomics and machine-learning can reliably predict patients at risk for early intrahepatic recurrence with good discrimination accuracy.