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The article reports that in the era of precision medicine, integrated diagnostics is employed for selecting appropriate and optimal therapies tailored for patients' individual characteristics. Topics include considered a highly debated and heterogeneous clinical scenario is to manage the patients who underwent whole-gland therapy for prostate cancer.

To assess the impact of scanning direction on the image quality of coronary artery bypass grafts (CABGs), native coronary arteries (NCAs) were examined by electrocardiographically (ECG) gated 16-row multidetector computed tomography (16-MDCT). Eighty-two patients with 209 grafts were studied by 16-MDCT. Forty-one patients with 111 grafts were scanned craniocaudally. Forty-one patients with 98 grafts were scanned caudocranially. CABG, native coronary arteries were examined in four (proximal, middle, distal, distal anastomoses), three (proximal, middle, distal) segments, respectively. Subjective image quality on a four-point scale was calculated for segments. Scores of groups were compared. Results Image quality scores of proximal, distal segments of the right coronary artery (RCA) were better in caudocranially scanned group (P<0.05). When we subgrouped patients according to initial heart rates (IHR) (group 1, <65 beats/min; group 2, > or =65 beats/min), there was no statistical significance between image quality scores of coronary arteries, CABG when IHR was <65 beats/min in groups regardless of scanning direction. Scores of anastomotic segment of CABG to RCA, middle segments of circumflex coronary artery, proximal and distal segments of RCA in caudocranially scanned group were better when the IHR is > or =65 beats/min compared with the craniocaudally scanned group. When the IHR of the patient is > or =65 beats/min, performing ECG-gated 16-MDCT angiography in the caudocranial direction provides better image quality for evaluation of coronary arteries and CABGs. [ABSTRACT FROM AUTHOR]

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined. [ABSTRACT FROM AUTHOR]

Key Points: • Identify, assure, and measure major sources of variability affecting the MRI-directed biopsy pathway for prostate cancer diagnosis. • Develop strategies to control and minimize variations that impair pathway effectiveness including the performance of main players and team working. • Assure end-to-end quality of the diagnostic chain with robust multidisciplinary team working. [ABSTRACT FROM AUTHOR]

An editorial is presented on multiple researchers developing computer-aided detection (CAD) algorithms enabling the detection of clinically significant prostate cancer (csPCa) on MRI. Topics include developments being welcomed by radiologists, urologists, and healthcare administrators facing the increased demand for MRI-influenced prostate biopsies; and number of acceptable false detections depending on the urologist or patient showing biopsy versus cancer averse.

Objectives: To evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists.Methods: Nine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone-peripheral (PZ) and transition (TZ).Results: Index lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001).Conclusions: CAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience.Key Points: • Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI. • CAD assistance improves agreement between radiologists in detecting prostate cancer lesions. • However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone. • CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence. [ABSTRACT FROM AUTHOR]