LAUSR

The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer. The study population consisted of 31 prostate cancer patients. Radiomics features were extracted from weekly CBCT scans performed for verifying treatment position. From the data, logistic-regression models were learned for establishing tumor stage, Gleason score, level of prostate-specific antigen, and risk stratification, and for predicting biochemical recurrence. Performance of the learned models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) or the area under the precision-recall curve (AUC-PRC). Results suggest that the histogram-based Energy and Kurtosis features and the shape-based feature representing the standard deviation of the maximum diameter of the prostate gland during treatment are predictive of biochemical relapse and indicative of patients at high risk. Our results suggest the usefulness of CBCT-based radiomics for treatment definition in prostate cancer.