(68)Ga-PSMA-11 PET/CT Features Extracted from Different Radiomic Zones Predict Response to Androgen Deprivation Therapy in Patients with Advanced Prostate Cancer

SIMPLE SUMMARY: Androgen deprivation therapy plays a key role in the therapeutic management of patients with advanced prostate cancer. However, prediction of response before treatment initiation remains difficult. This study was undertaken to investigate whether (68)Ga-PSMA-11 PET/CT imaging features extracted from different prostatic zones (zone-1, zone-2, and zone-3) might predict response to androgen deprivation therapy in patients with advanced prostate cancer. Seven radiomic features extracted from zone-1 were identified as significantly associated with treatment response. In addition, two radiomic features from zone-2 and two from zone-3 were able to distinguish between different treatment response groups. Our findings demonstrate the potential usefulness of radiomic features extracted from different prostatic zones in predicting treatment response prior to androgen deprivation therapy. ABSTRACT: Purpose: Prediction of treatment response to androgen deprivation therapy (ADT) prior to treatment initiation remains difficult. This study was undertaken to investigate whether (68)Ga-PSMA-11 PET/CT features extracted from different radiomic zones within the prostate gland might predict response to ADT in patients with advanced prostate cancer (PCa). Methods: A total of 35 patients with prostate adenocarcinoma underwent two (68)Ga-PSMA-11 PET/CT scans—termed PET-1 and PET-2—before and after 3 months of ADT, respectively. The prostate was divided into three radiomic zones, with zone-1 being the metabolic tumor zone, zone-2 the proximal peripheral tumor zone, and zone-3 the extended peripheral tumor zone. Patients in the response group were those who showed a reduction ratio > 30% for PET-derived parameters measured at PET-1 and PET-2. The remaining patients were classified as non-responders. Results: Seven features (glcm_idmn, glcm_idn, glcm_imc1, ngtdm_Contrast, glrlm_rln, gldm_dn, and shape_MeshVolume) from zone-1, two features (gldm_sdlgle and shape_MinorAxisLength) from zone-2, and two features (diagnostics_Mask-interpolated_Minimum and shape_Sphericity) from zone-3 successfully distinguished responders from non-responders to ADT. One predictive feature (shape_SurfaceVolumeRatio) was consistently identified in all of the three zones. Conclusions: this study demonstrates the potential usefulness of radiomic features extracted from different prostatic zones in distinguishing responders from non-responders prior to ADT initiation.