Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

Abstract

To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.