Application Value of Radiomics Features Based on PSMA PET/CT in Diagnosis of Clinically Significant Prostate Cancer: A Comparative Analysis of the PRIMARY and PI-RADS Scores

Abstract

Objectives: The aim of our study was to explore the role of radiomic features derived from positron emission tomography (PSMA-PET)/computed tomography (CT) images in diagnosis of clinically significant prostate cancer (csPCa). Additionally, we aimed to investigate correlations between these features and other PET/CT parameters. Finally, we compared these radiomic features with the PRIMARY and PI-RADS scores to assess their complementarity and enhance the diagnostic capabilities for prostate cancer. Methods: A total of 110 patients with a certain pathological diagnosis were included, and a total of 1155 sets of radiomic features were extracted from these images for each patient. We employed the LASSO regression algorithm (Lasso) to select these features and collect MRI interpretation results (PI-RADS v2.1) via consensus reading for each patient. Two clinical physicians with more than three years of experience in nuclear medicine scored the medical images (PRIMARY) for all patients, and a consensus was reached. Finally, we compared diagnostic capabilities between radiomic features and indices/scores based on medical imaging (magnetic resonance (MRI) and positron emission tomography (PET/CT)). Results:After the Lasso algorithm was applied, three sets of radiomic features, log-sigma-1-mm-3D_glcm_Correlation, log-sigma-3-mm-3D_firstorder_Minimum, and wavelet-LLH_glcm_Imc2, marked as RF1, RF2, and RF3, respectively, were included in the analysis. The area under the curve (AUC) for diagnosing csPCa was 0.8744 (95% CI=0.806-0.943), 0.8413 (95% CI=0.762-0.920), and 0.8602 (95% CI=0.625-0.841), with accuracies of 0.8364, 0.8273, and 0.8273, respectively. The kappa values with the maximum standard uptake value (SUVmax) were 0.757, 0.779, and 0.737; Gleason scores were 0.696, 0.688, and 0.668 of three radiomics features, respectively. The combined analysis of radiomic features(RF1) and MRI results yielded an accuracy of 0.8727, a recall of 0.8364, and a specificity of 0.9091. Conclusion: Radiomic features based on PSMA PET/CT images correlate strongly with the SUVmax and pathological diagnostic results (Gleason score) of prostate cancer patients. These methods can compensate for the insufficient specificity of MRI for prostate cancer diagnosis but not for PET/CT.