Stereotactic Body Radiotherapy for Hepatocellular Carcinoma: Current Evidence and the Feasibility of Radiomics-based Predictive Models

Abstract Background : Stereotactic body radiotherapy (SBRT) is an effective but less focused alternative for treatment of hepatocellular carcinoma (HCC). To date, a personalized model for predicting therapeutic response is lacking. This study aimed to review current knowledge and to propose a radiomics-based machine-learning (ML) strategy for local response (LR) prediction. Methods : We searched the literature for studies conducted between January 1993 and August 2019 that used > 100 patients. Additionally, 172 HCC patients in our hospital were retrospectively analyzed between January 2007 and December 2016. In the radiomic analysis, 41 treated tumors were contoured and 46 radiomic features were extracted. Results : The 1-year local control was 85.4% in our patient cohort, comparable with current results (87-99%). 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. Conclusions : SBRT has high 1-year local control and our study sets the basis for constructing predictive models for HCC patients receiving SBRT.