Despite Trans-Arterial Chemo Embolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomic model, an imaging-based tool to predict these adverse outcomes.
We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a LASSO feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models’ performance was compared using 5-fold cross-validated Area Under the Receiver Operating Characteristic curves (AUC).
88 patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 months (IQR:3.6-9.3), and the median time for post-LT recurrence was 19.2 months (IQR:6.1-34.0). Twenty-four patients (27.3%) dropped out, and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean AUC of 0.70(±0.07), 0.87(±0.06) and 0.81(±0.06) for the arterial, venous and the combined models, respectively.
A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required.