Using computed tomography-based radiomics to predict outcomes for hepatocellular carcinoma patients receiving stereotactic body radiotherapy

Background: Stereotactic body radiotherapy (SBRT) is an effective and non-invasive alternative for treatment of hepatocellular carcinoma (HCC). To date, a personalized model for predicting therapeutic response is lacking. Here we propose a radiomics-based machine learning (ML) strategy for local response (LR) prediction. Methods: One hundred seventy-two HCC patients in our hospital were retrospectively analyzed between January 2007 and December 2016. For radiomic analysis, patients who underwent locoregional ablative treatments in the past were excluded. Enrolled patients had undergone dynamic CT before radiotherapy and follow-up CT to evaluate responses. Results: The 1-year local control was 85.4% in our patient cohort. After excluding unsuitable tumors for imaging analysis, 41 tumors remained. The Support Vector Machine (SVM) classifier, based on computed tomography (CT) scans in the A phase processed by equal probability (Ep) quantization with 8 gray levels, showed the highest mean F1 score (0.7995) for favorable LR within 1 year (W1R), at the end of follow-up (EndR), and condition of in-field failure-free (IFFF). The area under the curve (AUC) for this model was 92.1%, 96.3%, and 99.2% for W1R, EndR, and IFFF, respectively. Discussion: SBRT has high 1-year local control and our study sets the basis for constructing predictive models for HCC patients receiving SBRT.