Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study

The aim of this study was to evaluate the effectiveness of [18F]-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging in discriminating high pathological grade (Gleason score > 7), and low pathological grade (Gleason score < 7) using machine learning techniques. The study involved 81 patients with diagnosed prostate cancer who underwent positive [18F]-SPMA PET/CT scans. The PET images were used to identify the primary lesions, and then radiomics analyses were performed using an Imaging Biomarker Standardization Initiative (IBSI) compliant software, namely matRadiomics. Machine learning approaches were employed to identify relevant radiomics features for predicting high-risk malignant disease. The performance of the models was validated using 10 times repeated 5-fold cross validation scheme. The results showed a value of 0.75 for the area under the curve and an accuracy of 72% using the support vector machine (SVM). In conclusion, the study showcased the clinical potential of [18F]-SPMA PET/CT radiomics in differentiating high-risk and low-risk tumors, without the need for biopsy sampling. In-vivo PET/CT imaging could therefore be considered a noninvasive tool for virtual biopsy, facilitating personalized treatment management.