Artificial Intelligence Clinical Application in Prostate Cancer Molecular Imaging

Abstract

In medical imaging, artificial intelligence (AI) can be described as the ability of a system to accurately interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. This new technology holds great promise for more personalized approaches in the future. The aim of this review is to describe the current literature on AI applied to molecular imaging for prostate cancer (PC). A comprehensive search strategy was used based on SCOPUS and PubMed databases up to October 2020. From all studies published in English, we selected the most relevant that evaluated insights of AI in the assessment of PC through positron emission tomography (PET). AI may improve PC's patient care in many different fields, from the semi-automatization of tumor segmentation, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to prognostic prediction and risk-group selection. The implementation of AI algorithms in PC molecular imaging can improve and ease the diagnostic, predictive process, and global patient care. Construction of standardized and large databases with complex information (clinical, imaging, laboratory) will be the essential step to creating and training automated diagnostic/prognostic models that can help clinicians make unbiased and faster decisions aimed at personalized healthcare.