Background: Altered systemic and cellular lipid metabolism plays a pivotal role in the pathogenesis of prostate cancer (PCa). In this study, we aimed to characterize T1-magnetic resonance imaging (MRI)-derived radiomic parameters of periprostatic adipose tissue (PPAT) associated with clinically significant PCa (Gleason score ≥7 [3 + 4]) in a cohort of men who underwent robot-assisted prostatectomy. Methods: Preoperative MRI scans of 98 patients were identified. The volume of interest was defined by identifying an annular shell-like region on each MRI slice to include all surgically resectable visceral adipose tissue. An optimal biomarker method was used to identify features from 7631 intensity- and texture-based properties that maximized the classification of patients into clinically significant PCa and indolent tumors at the final pathology analysis. Results: Six highest ranked optimal features were derived, which demonstrated a sensitivity, specificity, and accuracy of association with the presence of clinically significant PCa, and area under a receiver operating characteristic curve of 0.95, 0.39 0.82, and 0.82, respectively. Conclusion: A highly independent set of PPAT features derived from MRI scans that predict patients with clinically significant PCa was developed and tested. With future external validation, these features may provide a more precise scientific basis for deciding to omit biopsies in patients with borderline prostate-specific antigen kinetics and multiparametric MRI readings and help in the decision of enrolling patients into active surveillance.
Keywords: MRI; prostate cancer; radiomics.