Personalized management of prostate cancer: from molecular and imaging markers to radiogenomics

Abstract

Purpose Prostate cancer (PCa) is the second most common cancer in men. The urge to guide treatment tactics based on personal clinical risk factors has evolved in the era of human genome sequencing. To date, personalized approaches to managing PCa patients have not yet been developed. Radiogenomics is a relatively new term, used to refer to the study of genetic variation associated with imaging features of the tumour in order to improve the prognostication of the disease course. Material and methods The study is a review of recent knowledge regarding potential clinical applications of radiogenomics in personalized treatment of PCa. Results Recent investigations have proven that by combining data on individual genetic tumour features, and radiomic profiling (radiologic-molecular correlation), with traditional staging procedures in order to personalize treatment of PCa, an improved prognostication of PCa course can be performed, and overtreatment of indolent cancer can be avoided. It was found that a combination of multiparametric MRI and gene expression data allowed the detection of radiomic features of PCa, which correlated with a number of gene signatures associated with adverse outcomes. It was revealed that several molecular markers may drive tumour upstaging, allowed the distinction between the PCa stages, and correlated with aggressiveness-related radiomic features. Conclusions The radiogenomics of PCa is not a comprehensively investigated area of oncourology. The combination of genomics and radiomics as integrative parts of precision medicine in the future has the potential to become the foundation for a personalized approach to the management of PCa.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

“…By combining imaging and genomics data, radiogenomics provides a more accurate method for diagnosing and avoiding overtreatment of low-risk tumors [56]. Specifically, radiogenomics may use imaging features to predict (or combine) the status of genes and guide the diagnosis, treatment, and prognostic process of PCa [105]. For example, the combination of mpMRI and gene expression data can detect the radioactive signature of PCa.…”

“…It can also guide clinical treatment strategies based on individual clinical risk factors. For example, one of the personalized methods of PCa risk calculation is to include clinical data of patients, consisting of PSA levels, and PCa Antigen 3 (PCa3) and TMPRSS2-ERG (T2:ERG) expression [105]. Due to the limited medical datasets, the short-term solution is to use transfer learning or data augmentation, and the long-term solution is to use multiinstitutional data by facilitating the development of online databases [123].…”

miRNA Expression Patterns in Early- and Late-Stage Prostate Cancer Patients: High-Throughput Analysis

“…It is known that epigenetic mechanisms are activated in gene regulation at the earliest stages of carcinogenesis. The identification of those microRNAs (miRNAs) that can serve as predictors of PCa development and severity, aid in molecular classification, provide a prognosis, and be useful for personalizing patient management tactics while avoiding unnecessary treatment costs is a crucial task [18].…”