This review presents the current state of the art regarding multiparametric magnetic resonance (MR) imaging of prostate cancer. Technical requirements and clinical indications for the use of multiparametric MR imaging in detection, localization, characterization, staging, biopsy guidance, and active surveillance of prostate cancer are discussed. Although reported accuracies of the separate and combined multiparametric MR imaging techniques vary for diverse clinical prostate cancer indications, multiparametric MR imaging of the prostate has shown promising results and may be of additional value in prostate cancer localization and local staging. Consensus on which technical approaches (field strengths, sequences, use of an endorectal coil) and combination of multiparametric MR imaging techniques should be used for specific clinical indications remains a challenge. Because guidelines are currently lacking, suggestions for a general minimal protocol for multiparametric MR imaging of the prostate based on the literature and the authors' experience are presented. Computer programs that allow evaluation of the various components of a multiparametric MR imaging examination in one view should be developed. In this way, an integrated interpretation of anatomic and functional MR imaging techniques in a multiparametric MR imaging examination is possible. Education and experience of specialist radiologists are essential for correct interpretation of multiparametric prostate MR imaging findings. Supportive techniques, such as computer-aided diagnosis are needed to obtain a fast, cost-effective, easy, and more reproducible prostate cancer diagnosis out of more and more complex multiparametric MR imaging data.

Multimodal magnetic resonance imaging is an effective technique to localize prostate cancer. Magnetic resonance imaging guided biopsy of tumor suspicious regions is an accurate method to detect clinically significant prostate cancer in men with repeat negative biopsies and increased prostate specific antigen.

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120281/-/DC1.

Objectives The patients' view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology. Methods Six domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach's alpha and composite reliability. Results The exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5). Conclusions This study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale. Key Points • Although AI systems are increasingly developed, not much is known about patients' views on AI in radiology. • Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients' views on AI in radiology, revealing five factors. • Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.