Abstract:
Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging
and directing prostate carcinoma treatment. Here, we summarize the available literature data on
radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores
derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand
avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and
could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for
the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning
based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving
the way for a fully automated image reading in nuclear medicine. As for prediction to treatment
outcome following 177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand
PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration
in larger datasets of patients.
