This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.

These results suggest that a 3D MRSI examination added to a clinical MR imaging examination may help define the presence and spatial extent of prostate cancer.

The major goal for prostate cancer imaging in the next decade is more accurate disease characterization through the synthesis of anatomic, functional, and molecular imaging information. No consensus exists regarding the use of imaging for evaluating primary prostate cancers. Ultrasonography is mainly used for biopsy guidance and brachytherapy seed placement. Endorectal magnetic resonance (MR) imaging is helpful for evaluating local tumor extent, and MR spectroscopic imaging can improve this evaluation while providing information about tumor aggressiveness. MR imaging with superparamagnetic nanoparticles has high sensitivity and specificity in depicting lymph node metastases, but guidelines have not yet been developed for its use, which remains restricted to the research setting. Computed tomography (CT) is reserved for the evaluation of advanced disease. The use of combined positron emission tomography/CT is limited in the assessment of primary disease but is gaining acceptance in prostate cancer treatment follow-up. Evidence-based guidelines for the use of imaging in assessing the risk of distant spread of prostate cancer are available. Radionuclide bone scanning and CT supplement clinical and biochemical evaluation (prostate-specific antigen [PSA], prostatic acid phosphate) for suspected metastasis to bones and lymph nodes. Guidelines for the use of bone scanning (in patients with PSA level > 10 ng/mL) and CT (in patients with PSA level > 20 ng/mL) have been published and are in clinical use. Nevertheless, changes in practice patterns have been slow. This review presents a multidisciplinary perspective on the optimal role of modern imaging in prostate cancer detection, staging, treatment planning, and follow-up.

The addition of 3D MR spectroscopic imaging to MR imaging provides better detection and localization of prostate cancer in a sextant of the prostate than does use of MR imaging alone.

Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.