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Due to the COVID-19 pandemic, it is very likely that many radiology residency and fellowship programs will adopt interactive videoconference interviewing for the 2020-2021 residency match cycle. Although video interviewing has become a common part of the hiring process for business, experience with video interviewing for resident and fellow selection has been limited. Advantages of video interviews over traditional on-site interviews include cost-savings to both applicants and residency programs, less disruption to an applicant's educational activities, and potential for training programs to access a wider pool of candidates. The loss of the casual interactions that occur during an on-site interview and the inability of candidates to evaluate training facilities and their surrounding environments in-person are among the obstacles posed by video interviews, but training programs can mitigate these challenges with enhanced website content and creative media solutions. Through a review of the existing literature and internet resources, this article recommends specific measures medical schools, applicants, and radiology residency and fellowship programs can take to optimize the virtual interview experience for all involved parties.

Rationale and Objectives To evaluate whether a computed tomography (CT)-based computerized decision-support system for muscle-invasive bladder cancer treatment response assessment (CDSS-T) can improve identification of patients who have responded completely to neoadjuvant chemotherapy. Materials and Methods Following Institutional Review Board approval, pre-chemotherapy and post-chemotherapy CT scans of 123 subjects with 157 muscle-invasive bladder cancer foci were collected retrospectively. CT data were analyzed with a CDSS-T that uses a combination of deep-learning convolutional neural network and radiomic features to distinguish muscle-invasive bladder cancers that have fully responded to neoadjuvant treatment from those that have not. Leave-one-case-out cross-validation was used to minimize overfitting. Five attending abdominal radiologists, four diagnostic radiology residents, two attending oncologists, and one attending urologist estimated the likelihood of pathologic T0 disease (complete response) by viewing paired pre/post-treatment CT scans placed side-by-side on an internally-developed graphical user interface. The observers provided an estimate without use of CDSS-T and then were permitted to revise their estimate after a CDSS-T-derived likelihood score was displayed. Observer estimates were analyzed with multi-reader, multi-case receiver operating characteristic methodology. The area under the curve (AUC) and the statistical significance of the difference were estimated. Results The mean AUCs for assessment of pathologic T0 disease were 0.80 for CDSS-T alone, 0.74 for physicians not using CDSS-T, and 0.77 for physicians using CDSS-T. The increase in the physicians' performance was statistically significant (P Conclusion CDSS-T improves physician performance for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

We evaluated the intraobserver variability of physicians aided by a computerized decision-support system for treatment response assessment (CDSS-T) to identify patients who show complete response to neoadjuvant chemotherapy for bladder cancer, and the effects of the intraobserver variability on physicians' assessment accuracy. A CDSS-T tool was developed that uses a combination of deep learning neural network and radiomic features from computed tomography (CT) scans to detect bladder cancers that have fully responded to neoadjuvant treatment. Pre- and postchemotherapy CT scans of 157 bladder cancers from 123 patients were collected. In a multireader, multicase observer study, physician-observers estimated the likelihood of pathologic T0 disease by viewing paired pre/posttreatment CT scans placed side by side on an in-house-developed graphical user interface. Five abdominal radiologists, 4 diagnostic radiology residents, 2 oncologists, and 1 urologist participated as observers. They first provided an estimate without CDSS-T and then with CDSS-T. A subset of cases was evaluated twice to study the intraobserver variability and its effects on observer consistency. The mean areas under the curves for assessment of pathologic T0 disease were 0.85 for CDSS-T alone, 0.76 for physicians without CDSS-T and improved to 0.80 for physicians with CDSS-T (P = .001) in the original evaluation, and 0.78 for physicians without CDSS-T and improved to 0.81 for physicians with CDSS-T (P = .010) in the repeated evaluation. The intraobserver variability was significantly reduced with CDSS-T (P &lt, 0001). The CDSS-T can significantly reduce physicians' variability and improve their accuracy for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

OBJECTIVES To develop a U-Net-based deep learning approach (U-DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline. MATERIALS AND METHODS A dataset of 173 cases including 81 cases in the training/validation set (42 masses, 21 with wall thickening, 18 normal bladders), and 92 cases in the test set (43 masses, 36 with wall thickening, 13 normal bladders) were used with Institutional Review Board approval. An experienced radiologist provided three-dimensional (3D) hand outlines for all cases as the reference standard. We previously developed a bladder segmentation method that used a deep learning convolution neural network and level sets (DCNN-LS) within a user-input bounding box. However, some cases with poor image quality or with advanced bladder cancer spreading into the neighboring organs caused inaccurate segmentation. We have newly developed an automated U-DL method to estimate a likelihood map of the bladder in CTU. The U-DL did not require a user-input box and the level sets for postprocessing. To identify the best model for this task, we compared the following models: (a) two-dimensional (2D) U-DL and 3D U-DL using 2D CT slices and 3D CT volumes, respectively, as input, (b) U-DLs using CT images of different resolutions as input, and (c) U-DLs with and without automated cropping of the bladder as an image preprocessing step. The segmentation accuracy relative to the reference standard was quantified by six measures: average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), average Hausdorff distance (AHD), and the average Jaccard index (AJI). As a baseline, the results from our previous DCNN-LS method were used. RESULTS In the test set, the best 2D U-DL model achieved AVI, AVE, AAVE, AMD, AHD, and AJI values of 93.4 ± 9.5%, -4.2 ± 14.2%, 9.2 ± 11.5%, 2.7 ± 2.5 mm, 9.7 ± 7.6 mm, 85.0 ± 11.3%, respectively, while the corresponding measures by the best 3D U-DL were 90.6 ± 11.9%, -2.3 ± 21.7%, 11.5 ± 18.5%, 3.1 ± 3.2 mm, 11.4 ± 10.0 mm, and 82.6 ± 14.2%, respectively. For comparison, the corresponding values obtained with the baseline method were 81.9 ± 12.1%, 10.2 ± 16.2%, 14.0 ± 13.0%, 3.6 ± 2.0 mm, 12.8 ± 6.1 mm, and 76.2 ± 11.8%, respectively, for the same test set. The improvement for all measures between the best U-DL and the DCNN-LS were statistically significant (P

Purpose The aim of this work was to prioritize in a quaternary academic environment necessary elements of a replacement PACS. Methods This quality improvement work was conducted at one academic medical center and was “not regulated” by the institutional review board. Three workgroups (10-15 members each) with unique resident, fellow, and attending radiologists; IT specialists; and departmental leaders convened in 2018 to prioritize elements for a PACS replacement project, including integrated IT tools. Each workgroup met two or three times and represented one of three missions (clinical, research, and education). Six elements assigned the highest priority were distilled from each workgroup. The resulting 18 elements were condensed into survey format and distributed to all department residents, fellows, and faculty members for 5-point Likert-type prioritization stratified by mission. Data were collected over 2 weeks. Results The survey response rate was 37% (71 of 192; 17 of 44 residents, 3 of 27 fellows, and 51 of 121 faculty members). Self-reported work effort was 63 ± 26% clinical, 14 ± 11% education, 15 ± 21% research, and 8 ± 14% administration. Aggregate priority ratings across all domains were highest for “stable system with predictable behavior” (mean, 4.51), “minimizes repetitive non-value-added work” (mean, 4.40), “interoperability” (mean, 4.12), and “near-instantaneous load times” (mean, 4.07). Clinical-specific ratings for these elements were even higher (means, 4.85-4.90). The lowest aggregate scores were mobile device compatibility (mean, 3.03), connectivity to nonaffiliated sites (mean, 3.01), and integrated instant messaging (mean, 2.87). Conclusions The department prioritized a stable and interoperable system that minimized non-value-added work. In other words, participants wanted a functioning PACS. PACS vendors should prioritize a reliable experience over niche add-ons.

Purpose We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU). Materials and methods A data set of 172 CTU cases was collected retrospectively with Institutional Review Board (IRB) approval. The data set was randomly split into two independent sets of training (81 cases) and testing (92 cases) which were manually outlined for both the inner and outer wall. We trained a deep-learning convolutional neural network (DL-CNN) to distinguish the bladder wall from the inside and outside of the bladder using neighborhood information. Approximately, 240 000 regions of interest (ROIs) of 16 × 16 pixels in size were extracted from regions in the training cases identified by the manually outlined inner and outer bladder walls to form a training set for the DL-CNN; half of the ROIs were selected to include the bladder wall and the other half were selected to exclude the bladder wall with some of these ROIs being inside the bladder and the rest outside the bladder entirely. The DL-CNN trained on these ROIs was applied to the cases in the test set slice-by-slice to generate a bladder wall likelihood map where the gray level of a given pixel represents the likelihood that a given pixel would belong to the bladder wall. We then used the DL-CNN likelihood map as an energy term in the energy equation of a cascaded level sets method to segment the inner and outer bladder wall. The DL-CNN segmentation with level sets was compared to the three-dimensional (3D) hand-segmented contours as a reference standard. Results For the inner wall contour, the training set achieved the average volume intersection, average volume error, average absolute volume error, and average distance of 90.0 ± 8.7%, -4.2 ± 18.4%, 12.9 ± 13.9%, and 3.0 ± 1.6 mm, respectively. The corresponding values for the test set were 86.9 ± 9.6%, -8.3 ± 37.7%, 18.4 ± 33.8%, and 3.4 ± 1.8 mm, respectively. For the outer wall contour, the training set achieved the values of 93.7 ± 3.9%, -7.8 ± 11.4%, 10.3 ± 9.3%, and 3.0 ± 1.2 mm, respectively. The corresponding values for the test set were 87.5 ± 9.9%, -1.2 ± 20.8%, 11.9 ± 17.0%, and 3.5 ± 2.3 mm, respectively. Conclusions Our study demonstrates that DL-CNN-assisted level sets can effectively segment bladder walls from the inner bladder and outer structures despite a lack of consistent distinctions along the inner wall. However, even with the addition of level sets, the inner and outer walls may still be over-segmented and the DL-CNN-assisted level sets may incorrectly segment parts of the prostate that overlap with the outer bladder wall. The outer wall segmentation was improved compared to our previous method and the DL-CNN-assisted level sets were also able to segment the inner bladder wall with similar performance. This study shows the DL-CNN-assisted level set segmentation tool can effectively segment the inner and outer wall of the bladder.

Lymph node involvement in bladder cancer is common and has prognostic implications. Early and accurate identification of metastatic lymph nodes is, therefore, important in ensuring appropriate patient triage and management. The purpose of this review is to provide a pictorial and educational overview of the staging and imaging appearance of metastatic lymph nodes in bladder cancer. Additionally, a secondary aim of this manuscript is to provide a review of the diagnostic accuracy of common imaging modalities available for detecting metastatic lymph nodes in affected patients.

Topical tissue sealants and hemostatic agents, seen on postoperative imaging in a variety of intraabdominal and pelvic locations, have the potential to be mistaken for abdominal abnormalities, especially if the radiologist is not aware of the patient's surgical history. The normal appearance of these agents may mimic abscesses, tumors, enlarged lymph nodes, or retained foreign bodies. Therefore, it is important to be familiar with their typical imaging appearances and to review the surgical records when needed to avoid misdiagnoses. The purpose of this article is to increase the radiologist's familiarity with various types of topical tissue sealants and hemostatic agents used during surgical and percutaneous procedures in the abdomen and pelvis along with their radiologic appearances.Various types of hemostatic agents are now commonly used during surgery and percutaneous procedures in the abdomen and pelvis, and it is important to recognize the various appearances of these agents. Although there are suggestive features outlined in this article, the most important factor for the radiologist is to be aware of the patient's history and the possibility that a hemostatic agent may be present. On postoperative imaging, hemostatic agents may mimic abscesses, tumors, enlarged lymph nodes, or retained foreign bodies, and accurate diagnosis can save a patient unnecessary treatment. It is therefore crucial to incorporate knowledge of the patient's surgical history with recognition of the typical imaging appearances of hemostatic agents and other pseudolesions to avoid misdiagnoses.

The purpose of this study was to determine whether individual radiologists are predictive of important relevant health outcomes among emergency department (ED) patients undergoing abdominopelvic CT for right lower quadrant pain.This single-institution retrospective cohort study included 2169 patients undergoing abdominopelvic CT for right lower quadrant pain in the ED from February 1, 2012, through August 31, 2016. CT examinations were interpreted by 15 radiologists (four emergency, 11 abdominal) who each reported on more than 70 CT examinations in the cohort. After risk adjustment for covariates thought to influence outcome, including baseline risk (demographics, 30 Elixhauser comorbidities, number of previous ED visits), clinical factors (vital signs, triage and pain scores, laboratory data), and system factors (time of CT, resident involvement, attending physician experience), multivariable models were built to analyze the effect of individual radiologists on four important health outcomes: hospital admission (primary outcome), readmission within 30 days, abdominal surgery, and image-guided percutaneous aspiration or drainage.Radiologists had a mean experience of 14 years (range, 2-36 years) and read a mean of 145 CT examinations in the study cohort (range, 73-253 examinations). Unadjusted event rates across the 15 radiologists were 38-55% (admission), 11-21% (readmission), 10-26% (surgery), and 0-3% (aspiration or drainage). After risk adjustment, individual radiologists were not a significant multivariable predictor of hospital admission, readmission within 30 days, abdominal surgery, or image-guided abdominal percutaneous aspiration or drainage (all p0.05).Individual radiologists were indistinguishable both within group and between group by emergency and abdominal specialization for the prediction of major patient outcomes after abdominopelvic CT performed for right lower quadrant pain in the ED.

RATIONALE AND OBJECTIVES The objective of this study was to determine whether noon conference attendance by diagnostic radiology residents is predictive of measurable performance. METHODS This single-center retrospective Health Insurance and Portability and Accountability Act (HIPAA)-compliant cross-sectional study was considered "not regulated" by the institutional review board. All diagnostic radiology residents who began residency training from 2008 to 2012 were included (N = 54). Metrics of clinical performance and knowledge were collected, including junior and senior precall test results, American Board of Radiology scores (z-score transformed), American College of Radiology in-training scores (years 1-3), on-call "great call" and minor and major discrepancy rates, on-call and daytime case volumes, and training rotation scores. Multivariate regression models were constructed to determine if conference attendance, match rank order, or starting year could predict these outcomes. Pearson bivariate correlations were calculated. RESULTS Senior precall test results were moderately correlated with American Board of Radiology (r = 0.41) and American College of Radiology (r = 0.38-0.48) test results and mean rotation scores (r = 0.41), indicating moderate internal validity. However, conference attendance, match rank order, and year of training did not correlate with (r = -0.16-0.16) or predict (P > .05) measurable resident knowledge. On multivariate analysis, neither match rank order (P = .14-.96) nor conference attendance (P = .10-.88) predicted measurable clinical efficiency or accuracy. Year started training predicted greater cross-sectional case volume (P

Rationale and Objective We aimed to determine the frequency and clinical significance of homogeneous renal masses measuring 21–39 Hounsfield units on contrast-enhanced computed tomography (CT). Methods Subjects 40–69 years old undergoing portal-venous-phase contrast-enhanced abdominal CT from January 1, 2006 to December 31, 2010 with slice thickness ≤5 mm and no prior CT or magnetic resonance imaging were identified (n = 1387) for this institutional review board-approved retrospective cohort study. Images were manually reviewed by three radiologists in consensus to identify all circumscribed homogeneous renal masses (maximum of three per subject) ≥10 mm with a measured attenuation of 21–39 Hounsfield units. Exclusion criteria were known renal cancer or imaging performed for a renal indication. The primary outcome was retrospective characterization as a clinically significant mass, defined as a solid mass, a Bosniak IIF/III/IV mass, or extirpative therapy or metastatic renal cancer within 5 years' follow-up. Results Eligible masses (n = 74) were found in 5% (63/1387) of subjects. Of those with a reference standard (n = 42), none (0% [95% CI: 0.0%–8.4%]) were determined to be clinically significant. Conclusion Incidental renal masses on contrast-enhanced CT that are homogeneous and display an attenuation of 21–39 Hounsfield units are uncommon in patients 40–69 years of age, unlikely to be clinically significant, and may not need further imaging evaluation. If these results can be replicated in an independent and larger population, the practical definition of a benign cyst on imaging may be able to be expanded.

Purpose To determine if the allergic-like breakthrough reaction rate of intravenous corticosteroid prophylaxis administered 5 hours before contrast material-enhanced computed tomography (CT) is noninferior to that of a traditional 13-hour oral regimen. Materials and Methods Institutional review board approval was obtained and informed consent waived for this retrospective noninferiority cohort study. Subjects (n = 202) who completed an accelerated 5-hour intravenous corticosteroid premedication regimen before low-osmolality contrast-enhanced CT for a prior allergic-like or unknown-type reaction to iodine-based contrast material from June 1, 2008, to June 30, 2016, were identified. The breakthrough reaction rate was compared by using the Farrington and Manning noninferiority likelihood score to test subjects premedicated with a traditional 13-hour oral regimen (2.1% [13 of 626]). All subjects were premedicated for a prior allergic-like or unknown-type reaction to iodine-based contrast material. A noninferiority margin of 4.0% was selected to allow for no more than a clinically negligible 6.0% breakthrough reaction rate in the cohort that received 5-hour intravenous corticosteroid prophylaxis. Results The breakthrough reaction rate for 5-hour intravenous prophylaxis was 2.5% (five of 202 patients; 95% confidence interval: 0.8%, 5.7%), which was noninferior to the 2.1% (13 of 626 patients; 95% confidence interval: 1.1%, 3.5%) rate for the 13-hour regimen (P = .0181). The upper limits of the confidence interval for the difference between the two rates was 3.7% (0.4%; 95% confidence interval: -1.6%, 3.7%), which was within the 4.0% noninferiority margin. All breakthrough reactions were of equal or lesser severity to those of the index reactions (two severe, one moderate, and one mild reaction). Conclusion Accelerated intravenous premedication with corticosteroids beginning 5 hours before contrast-enhanced CT has a breakthrough reaction rate noninferior to that of a 13-hour oral premedication regimen. © RSNA, 2017.

To assess the frequency with which previously reported characteristic findings of retroperitoneal fibrosis (RPF) (a circumferential or almost circumferential peri-aortic mass centered at L4, which does not displace the abdominal aorta or proximal common iliac arteries) are present in patients with RPF, in patients with other fibrosing diseases, and in cancer patients referred to a subspecialty clinic with a suspected diagnosis of RPF, in order to determine whether diagnostic percutaneous biopsy can be avoided in some patients. This HIPAA-compliant Institutional Review Board-approved retrospective study assessed clinical and CT and MR imaging abnormalities on imaging studies in 92 patients referred to a subspecialty clinic with suspected RPF over a 14-year period. Two reviewers, in consensus, determined the frequency of different CT and MRI findings in three groups of patients (Group 1: those with an eventual diagnosis of RPF, Group 2: those with a fibrosing disease associated with vascular or urologic abnormalities, and Group 3: those with cancer). Assessed imaging features included the presence of retroperitoneal masses, whether masses were single or multiple, whether such masses were circumferential or nearly circumferential, whether they displaced the aorta away from the spine (with the degree of such displacement measured), and whether there were abnormalities outside of the peri-aortic region of the retroperitoneum. The frequency with which findings previously reported as characteristic of RPF were present was determined for each of the three groups. Imaging results were correlated with the final diagnoses. Of 68 subjects eventually diagnosed with retroperitoneal fibrosis (RPF) (Group 1), 47 had peri-aortic retroperitoneal masses, 18 of which displaced the aorta anteriorly away from the spine. Of 12 subjects with fibrosing abnormalities related to vascular or urologic disease (Group 2), six had retroperitoneal masses, none of which displaced the aorta away from the spine. Of 12 subjects with malignancies (Group 3), six had peri-aortic retroperitoneal masses only two of whom had aortic displacement. Only 34 of 68 Group 1 subjects had peri-aortic masses characteristic of RPF, compared with six Group 2 subjects and one Group 3 subject. Subjects with characteristic retroperitoneal masses were significantly more likely to have benign disease than cancer (p = 0.009). Many patients with RPF do not have characteristic imaging findings. Contrary to prior publications, absence of aortic displacement is not seen in all patients with RPF and is seen in some cancer patients. Nonetheless, when infiltrative peri-aortic retroperitoneal soft tissue that does not displace the aorta is encountered on CT or MRI, RPF can be diagnosed with a high degree of confidence, obviating the need for biopsy.

Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.