Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

The most sensitive biomarker for prostate cancer recurrence after radical prostatectomy or radiotherapy is serum prostate specific antigen (PSA).¹ However, the evidence on the association of biochemical recurrence (BCR) (ie, PSA rise after treatment with curative intent) with prostate cancer survival is limited. Patients with rising PSA after radical prostatectomy or primary radiotherapy have different risks of subsequent symptomatic metastatic disease based on various parameters, including the PSA levels. Moreover, the high variability in reported effect sizes of BCR suggests that only certain patient subgroups with BCR might be at an increased risk of mortality.² In a recent systematic review of the literature, Van den Broeck et al³ found that higher International Society of Urological Pathology (ISUP) Gleason Grade Group (GG), shorter time from treatment to recurrence, and shorter PSA doubling time (PSA-DT) are associated with prostate cancer–specific mortality (PCSM) in patients with BCR. Based on these findings, the EAU guidelines suggest to stratify patients with BCR into EAU low-risk BCR (for radical prostatectomy: PSA-DT of more than 1 year and pathological GG of less than 4; for radiotherapy: time to BCR of more than 18 months and biopsy GG of less than 4) or EAU high-risk BCR (for radical prostatectomy: PSA-DT of less than 1 year or pathological GG 4 to 5; for radiotherapy: interval to biochemical failure of less than 18 months or biopsy GG 4 to 5).² Subsequently, Tilki et al⁴ and Pak et al⁵ externally validated the proposed risk stratification for patients undergoing radical prostatectomy, suggesting that patients who are low risk may not require an immediate intervention at time of BCR.

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.