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Purpose: To identify predictors of when systematic biopsy leads to a higher overall prostate cancer grade compared to targeted biopsy., Methods and Materials: 918 consecutive patients who underwent prostate MRI followed by MRI/US fusion biopsy and systematic biopsies from January 2015 to November 2019 at a single academic medical center were retrospectively identified. The outcome was upgrade of PCa by systematic biopsy, defined as cases when systematic biopsy led to a Gleason Grade (GG) ≥ 2 and greater than the maximum GG detected by targeted biopsy. Generalized linear regression and conditional logistic regression were used to analyze predictors of upgrade., Results: At the gland level, the presence of an US-visible lesion was associated with decreased upgrade (OR 0.64, 95% CI 0.44-0.93, p = 0.02). At the sextant level, upgrade was more likely to occur through the biopsy of sextants with MRI-visible lesions (OR 2.58, 95% CI 1.87-3.63, p < 0.001), US-visible lesions (OR 1.83, 95% CI 1.14-2.93, p = 0.01), and ipsilateral lesions (OR 3.89, 95% CI 2.36-6.42, p < 0.001)., Conclusion: Systematic biopsy is less valuable in patients with an US-visible lesion, and more likely to detect upgrades in sextants with imaging abnormalities. An approach that takes additional samples from regions with imaging abnormalities may provide analogous information to systematic biopsy., (© 2022. The Author(s).)

Many national radiology societies are recognizing the need for early career and trainee engagement as crucial to keeping their societies relevant, active, and invigorated with new ideas. In this descriptive paper, we review the benefits of establishing the Society of Abdominal Radiology's Resident and Fellow Section and Early Career Committee-including our activities and experience, advice for committee structure, and opportunities for growth., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lead to significant time delays. This HIPAA-compliant, institutional review board-approved, retrospective clinical study required identification and retrieval of all outpatient and emergency patients undergoing abdominal and pelvic computed tomography (CT) at three affiliated hospitals in the year 2012. If a patient had multiple abdominal CT exams, the first exam was selected for retrieval (n=23,186). Our experience in attempting to retrieve 23,186 abdominal CT exams yielded 22,852 valid CT abdomen/pelvis exams and identified four major categories of challenges when retrieving large datasets: cohort selection and processing, retrieving DICOM exam files from PACS, data storage, and non-recoverable failures. The retrieval took 3 months of project time and at minimum 300 person-hours of time between the primary investigator (a radiologist), a data scientist, and a software engineer. Exam selection and retrieval may take significantly longer than planned. We share our experience so that other investigators can anticipate and plan for these challenges. We also hope to help institutions better understand the demands that may be placed on their infrastructure by large-scale medical imaging machine learning projects., (© 2021. The Author(s).)

The authors provide a commentary on the current status of the Abdominal Radiology Fellowship recruitment process, which is not presently governed by a formal Match. Abdominal Radiology is the largest radiology subspecialty fellowship that remains outside of the Match. The Society of Abdominal Radiology convened a task force in 2019 to assess stakeholder viewpoints on a Match and found that the community was divided. Radiology departments and Abdominal Radiology fellowship program directors have voluntarily complied with a series of guidelines laid out by the Society of Chairs in Academic Radiology Departments during the two most recent recruiting cycles, but challenges in the process persist. Stakeholders report improved organization and fairness as a result of these procedural changes, and the authors suggest that Abdominal Radiology may continue to consider a formal fellowship Match in coming years., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences., (© 2021. Society for Imaging Informatics in Medicine.)

Purpose: To subjectively and quantitatively compare the quality of 3 Tesla magnetic resonance imaging of the prostate acquired with a novel flexible surface coil (FSC) and with a conventional endorectal coil (ERC)., Methods: Six radiologists independently reviewed 200 pairs of axial, high-resolution T2-weighted and diffusion-weighted image data sets, each containing one examination acquired with the FSC and one with the ERC, respectively. Readers selected their preferred examination from each pair and assessed every single examination using six quality criteria on 4-point scales. Signal-to-noise ratios were measured and compared., Results: Two readers preferred FSC acquisition (36.5-45%) over ERC acquisition (13.5-15%) for both sequences combined, and four readers preferred ERC acquisition (41-46%). Analysis of pooled responses for both sequences from all readers shows no significant preference for FSC or ERC. Analysis of the individual sequences revealed a pooled preference for the FSC in T2WI (38.7% vs 17.8%) and for the ERC in DWI (50.9% vs 19.6%). Patients' weight was the only weak predictor of a preference for the ERC acquisition (p = 0.04). SNR and CNR were significantly higher in the ERC acquisitions (p<0.001) except CNR differentiating tumor lesions from benign prostate (p=0.1)., Conclusion: Although readers have strong individual preferences, comparable subjective image quality can be obtained for prostate MRI with an ERC and the novel FSC. ERC imaging might be particularly valuable for sequences with inherently lower SNR as DWI and larger patients whereas the FSC is generally preferred in T2WI. FSC imaging generates a lower SNR than with an ERC.