At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully harness the potential of imaging. To tackle these challenges, radiomics has emerged as a promising approach for image analysis. It enables the extraction of objective quantitative features that are overlooked by human visual observation.
In this narrative review, we delve into the contemporary landscape of radiomics and artificial intelligence, shedding light on their potential contributions to the realm of PCa treatment planning, spanning curative approaches, active surveillance, and post-treatment disease recurrence management. Encouragingly, current evidence underscores the promising role of radiomics in bolstering decision-support tools to aid physicians in achieving these goals. Nonetheless, significant endeavors, particularly the adoption of external independent datasets for validation and the conduction of prospectively designed studies, are imperative to confirm these promising prospects and generate the compelling evidence required to translate these exciting possibilities into tangible medical practices.
Key points
- Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.
- Radiomics models could improve risk assessment for radical prostatectomy patient selection.
- Delta-radiomics appears promising for the management of patients under active surveillance.
- Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.
- Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
Article: Beyond diagnosis: is there a role for radiomics in prostate cancer management?
Authors: Arnaldo Stanzione, Andrea Ponsiglione, Francesco Alessandrino, Giorgio Brembilla & Massimo Imbriaco
