Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.