• metastatic prostate cancer
• PET/CT
• PSMA
• segmentation
• software
• tumor volume

Metastatic prostate cancer patients present an extensive disease affecting lymph nodes, bone, liver, and lungs. Positron emission tomography (PET) combined with computed tomography (CT) has proven to be one of the best imaging techniques for evaluating disease extent and treatment response. However, molecular imaging targeting prostate specific membrane antigen (PSMA) has become of great interest in prostate cancer (PC) diagnostics. 68Ga-PSMA-11 PET/CT is a novel imaging technique that showed enhanced accuracy compared with conventional imaging modalities in detecting prostate cancer lesions. Guidelines related to treatment response evaluation using 68Ga-PSMA-11 PET/CT, identify PSMA ligand-positive tumor volume (PSMA-VOL) as the parameter that can predict the assessment of response to therapy. In reality, given the high complexity of metastatic prostate cancer defined by its heterogeneity and variable extent of total disease burden, there is not a single segmentation method that can identify PSMA-VOL.
In this study, a comparative analysis was performed between different segmentation methods to assess the volumetric burden of disease in metastatic prostate cancer patients who underwent 68Ga-PSMA-11 PET/CT. In particular, the primary objective was to compare different segmentation methods and software with qPSMA, a semiautomatic software for whole-body tumor burden assessment. The secondary objectives were: to identify the most reproducible methods to assess the PSMA-VOL using a database with seventy-eight biopsy proven PC patients; to evaluate the difference in terms of lesions detected among semi-automatic qPSMA and operator-based manual segmentation; and to evaluate if qPSMA is more user-friendly and less operator-independent compared with alternative available software.
The identification of an appropriate method is fundamental not only for testing the predictivity of the PSMA-VOL parameter but also for the development of predictive models in the frame of personalized medicine.