MRI phenotype of the prostate: Transition zone radiomics analysis improves explanation of prostate-specific antigen (PSA) serum level compared to volume measurement alone

Overview of radiomics in prostate imaging and future directions

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Prostate heterogeneity correlates with clinical features on multiparametric MRI

BACKGROUND

Prostate heterogeneity on multi-parametric MRI (mpMRI) may confound image interpretation by obscuring lesions; systematic biopsy may have a role in this context.

PURPOSE

To determine if prostate heterogeneity (1) correlates with clinical risk factors for prostate cancer and (2) associates with higher-grade tumor in systematic biopsy (SB), compared with MRI-directed target biopsy (MDTB), i.e., SB > MDTB, thus providing a rationale for combined biopsy.

METHODS

IRB-approved retrospective study included men who underwent mpMRI, SB, and MDTB between 2015 and 2017. Regions of interest were applied to the entire transition zone (TZ) and peripheral zone (PZ) on T2-weighted imaging (T2WI), apparent diffusion coefficient maps (ADC), and early dynamic contrast-enhanced (DCE) images on the midgland slice. Mean signal intensities and standard deviation (SD) of each zone were calculated. SD served as a measure of heterogeneity. Spearman's rank correlation analysis of clinical and imaging variables was performed. Univariate logistic regression was used to determine if any imaging variable associated with SB > MDTB.

RESULTS

93 patients were included. Significant correlations included age and TZ ADC heterogeneity (rho = 0.34, p = 0.013), PSA density, and mean TZ ADC (rho = - 0.29, p = 0.049). PZ T2WI heterogeneity correlated with PZ ADC heterogeneity (rho = 0.48, p < 0.001). PZ DCE heterogeneity correlated with TZ DCE heterogeneity (rho = 0.46, p < 0.001). TZ ADC heterogeneity was associated with SB > MDTB prior to multiple comparison correction (p = 0.032). p value after correction was 0.24.

CONCLUSION

TZ ADC heterogeneity correlated with age and may reflect prostatic hyperplasia and/or prostate cancer. PZ heterogeneity, possibly a measure of prostatitis, correlated with TZ hyperplasia and/or inflammation. TZ ADC heterogeneity was associated with SB > MDTB with p value of < 0.05 prior to multiple correction; future investigation is needed to further elucidate significance of ADC heterogeneity in prostate imaging.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

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.