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Background: There are few data available concerning the use of intravenous contrast materials in children.Objective: To assess patterns of intravenous iodinated/gadolinium-based contrast material (ICM/GBCM) use in children.Materials and Methods: Society of Chairs of Radiology in Children's Hospitals (SCORCH) members were invited to complete a Web-based survey regarding intravenous contrast material use at their institutions.Results: Sixty-six percent (42/64) of SCORCH member institutions responded, 76% of which are academic hospitals. Ioversol and gadopentetate dimeglumine are the most commonly used ICMs and GBCMs (52% and 81%, respectively). Premedication policies to mitigate risk of allergic-like contrast reactions are more common for ICMs than GBCMs (71% vs. 32%). For both ICMs and GBCMs, most institutions premedicate or avoid contrast material in the setting of a prior allergic-like contrast reaction of any severity. Policies regarding ICM and GBCM use in children with renal insufficiency are in place at 74% and 88% of institutions, respectively, but are variable.Conclusion: Policies concerning premedication for prevention of allergic-like contrast agent reactions and administration of contrast materials in children with renal insufficiency are variable and not consistently present at all pediatric institutions. This highlights the need for standardization of practice patterns and pediatric-specific research on these topics. [ABSTRACT FROM AUTHOR]

Objective: To determine whether updated guidance by the ACR in 2017 advocating use of intravenous (IV) premedication in emergency department (ED) patients and inpatients with reported iodinated contrast allergy was associated with a change in clinical practice., Methods: An anonymous survey was distributed via e-mail in October 2020 to practicing radiologist members of the ACR interrogating use of corticosteroid premedication for two clinical vignettes: an indicated routine (perform within 24 hours) inpatient contrast-enhanced CT (CE-CT) and an indicated urgent (perform within 6 hours) ED CE-CT. In both scenarios, the patient had a prior moderate hypersensitivity reaction to iodinated contrast media. Clinical management was evaluated. Data were compared to historical controls from 2009., Results: The response rate was 11% (724 of 6,616). For the inpatient scenario, 72% (518 of 724) would use corticosteroid premedication with CE-CT, and 28% (200 of 724) would perform noncontrast CT. For the ED scenario, 67% (487 of 724) would use corticosteroid premedication with CE-CT, and 30% (217 of 724) would perform noncontrast CT. Oral premedication (85%, 439 of 518) was preferred for routine inpatients, and rapid IV premedication (89%, 433 of 487) was preferred for urgent ED patients. Of those who provided rapid IV dosing data in the ED, two doses of corticosteroids were used by 53% (216 of 410) and one dose was used by 45% (185 of 410), with academic radiologists more likely than private or hybrid practice radiologists to administer two doses (74% [74 of 100] versus 48% [151 of 312], P < .001, odds ratio, 3.03; 95% confidence interval, 1.84-5.00). Rapid IV premedication was more commonly used in 2020 than in 2009 (60% [433 of 724] versus 29% [20 of 69], P < .001, odds ratio, 3.65; 95% confidence interval, 2.12-6.26). Antihistamine use was common in both inpatient (93%, 480 of 518) and ED settings (92%, 447 of 487). Only 32% (229 of 721) of radiologists practiced in accordance with ACR guidelines, suggesting no need for routine premedication before CE-CT in patients with prior severe hypersensitivity reaction to gadolinium-based contrast media. Nonetheless, most (93%, 670 of 724) said the ACR Manual on Contrast Media was a major determinant of their practice., Conclusions: Use of rapid IV premedication in urgent settings has increased since 2009, following updated ACR guidelines, but there is disagreement over whether one or two corticosteroid doses is required. Despite reported high reliance on ACR guidelines, deviations from those guidelines remain common. In general, when ACR guidelines were not followed, it was in a risk-averse direction., (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p = 0.007). For Kaplan-Meier survival analysis, the deceased and alive groups were stratified successfully by C (p < 0.001) and CRD (p < 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.

This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians' diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers' clinical experience, institution affiliation, specialty, and the assessment times on the observers' diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved ( p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers' performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.

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., Competing Interests: Declaration of Competing Interest None, (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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 < .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., Competing Interests: Conflict of Interest: None reported., (© 2020 The Authors. Published by Grapho Publications, LLC.)

OBJECTIVE. The objective of our study was to explore whether clinical factors historically associated with contrast material-causative kidney injury (contrast-induced nephrotoxicity [CIN]) increase risk after use of IV iodinated low-osmolality contrast material (LOCM) in patients with stage IIIb-V chronic kidney disease. MATERIALS AND METHODS. In this retrospective hypothesis-generating study, 1:1 propensity score matching was used to assess post-CT acute kidney injury (AKI) after unenhanced or contrast-enhanced CT in patients with stable estimated glomerular filtration rate (eGFR; 1112 patients with an eGFR = 30-44 mL/min/1.73 m 2 and 86 patients with an eGFR < 30 mL/min/1.73 m 2 and no dialysis). Historical risk factors including diabetes mellitus, age more than 60 years, hypertension, loop diuretic use, hydrochlorothiazide use, and cardiovascular disease were evaluated for modulation of CIN risk. Stepwise multivariable logistic regression was performed. RESULTS. Overall IV LOCM was an independent risk factor for post-CT AKI in patients with an eGFR of less than 30 mL/min/1.73 m 2 (odds ratio, 3.96 [95% CI, 1.29-12.21]; p = 0.016) but not in those with an eGFR of 30-44 mL/min/1.73 m 2 ( p = 0.24). In patients with an eGFR of less than 30 mL/min/1.73 m 2 , the tested covariates did not significantly modify the risk of CIN ( p = 0.096-0.832). In patients with an eGFR of 30-44 mL/min/1.73 m 2 , risk of CIN emerged in those with cardiovascular disease ( p = 0.015; number needed to harm from LOCM = 11 patients); the other tested cofactors had no significant effect ( p = 0.108-0.822). CONCLUSION. CIN was observed when eGFR was less than 30 mL/min/1.73 m 2 . In those with an eGFR of 30-44 mL/min/1.73 m 2 , CIN was not observed with LOCM alone but was observed in the presence of cardiovascular disease. Other cofactors historically thought to increase CIN risk (e.g., diabetes mellitus) did not increase risk of CIN. Further study is needed to determine whether these exploratory results are true associations.

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 < .05)., Conclusion: CDSS-T improves physician performance for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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 < 0.001)., Conclusion: Compared to a previous DCNN-LS method, which depended on a user-input bounding box, the U-DL provided more accurate bladder segmentation and was more automated than the previous approach., (© 2019 American Association of Physicists in Medicine.)

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment based on transfer learning by freezing different DL-CNN layers and varying the DL-CNN structure. Pre- and posttreatment computed tomography scans of 123 patients (cancers, 129; pre- and posttreatment cancer pairs, 158) undergoing chemotherapy were collected. After chemotherapy 33% of patients had T0 stage cancer (complete response). Regions of interest in pre- and posttreatment scans were extracted from the segmented lesions and combined into hybrid pre -post image pairs (h-ROIs). Training (pairs, 94; h-ROIs, 6209), validation (10 pairs) and test sets (54 pairs) were obtained. The DL-CNN consisted of 2 convolution (C1-C2), 2 locally connected (L3-L4), and 1 fully connected layers. The DL-CNN was trained with h-ROIs to classify cancers as fully responding (stage T0) or not fully responding to chemotherapy. Two radiologists provided lesion likelihood of being stage T0 posttreatment. The test area under the ROC curve (AUC) was 0.73 for T0 prediction by the base DL-CNN structure with randomly initialized weights. The base DL-CNN structure with pretrained weights and transfer learning (no frozen layers) achieved test AUC of 0.79. The test AUCs for 3 modified DL-CNN structures (different C1-C2 max pooling filter sizes, strides, and padding, with transfer learning) were 0.72, 0.86, and 0.69. For the base DL-CNN with (C1) frozen, (C1-C2) frozen, and (C1-C2-L3) frozen, the test AUCs were 0.81, 0.78, and 0.71, respectively. The radiologists' AUCs were 0.76 and 0.77. DL-CNN performed better with pretrained than randomly initialized weights.

Purpose: To determine if symptomatic patients referred for radiographic stool quantification have equivalent stool burden to asymptomatic patients., Method: This was an IRB-approved HIPAA-compliant retrospective equivalence cohort study. An a priori equivalence power calculation was performed. Consecutive abdominal radiographs performed in adult outpatients with bloating, constipation, diarrhea, or abdominal pain to assess "fecal loading" [n = 242 (fecal cohort)] were compared to those performed in asymptomatic adult outpatients to assess "renal stones" [n = 242 (renal cohort)]. Radiographs were randomized and reviewed by two blinded independent abdominal radiologists. Exclusion criteria, designed to avoid unblinding, included urinary tract calculi ≥ 0.5 cm, multiple urinary tract calculi, and ureteral stent(s). Readers scored all radiographs (n = 484) for stool burden using validated Leech criteria [scale: 0 (none) to 15 (extreme diffuse)]. Mean Leech scores and 95% confidence intervals were calculated. Multivariable generalized linear modeling was performed to adjust for baseline medication use, age, and gender. The adjusted parameter estimate was used to test for equivalence in the mean difference between cohorts using Schuirmann's method of two one-sided t-tests. Inter-reader agreement was assessed with intraclass correlation coefficients., Results: Overall mean Leech scores for fecal [6.9 (95% CI 6.7, 7.2)] and renal [7.3 (95% CI 7.1, 7.5)] cohorts were equivalent within a margin of 0.75 (adjusted mean difference: - 0.4 [90% CI - 0.7, - 0.04]; p value = 0.02). Inter-reader agreement was good [ICC: 0.62 (95% CI 0.56, 0.68)]., Conclusion: Radiographic stool quantification produces equivalent results in symptomatic and asymptomatic adults and is of uncertain value.

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., (© 2018 American Association of Physicists in Medicine.)

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., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Objective: 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., Conclusion: 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.

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., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Objective: 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., Materials and Methods: 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., Results: 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 p > 0.05)., Conclusion: 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 < .0001, β = 0.361-0.516) and less faculty-to-resident feedback (P < 0.0001, β = [-0.628]-[-0.733])., Conclusions: Residents with lower conference attendance are indistinguishable from those who attend more frequently in a wide range of clinical and knowledge-based performance assessments, suggesting that required attendance may not be necessary to gain certain measurable core competencies., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: 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., Methods: 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., Results: 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)., Conclusion: 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.

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., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose To identify computed tomographic (CT) findings that are predictive of recurrence of colonic diverticulitis. Materials and Methods Institutional review board approval was obtained for this HIPAA-compliant, retrospective cohort study. Six abdominal fellowship-trained radiologists reviewed the CT studies of 440 consecutive subjects diagnosed with acute colonic diverticulitis between January 2004 and May 2008 to determine the involved segments, maximum wall thickness in the inflamed segment, severity of diverticulosis, presence of complications (abscess, fistula, stricture, or perforation), and severity of the inflammation. Electronic medical records were reviewed for a 5-year period after the patients' first CT study to determine clinical outcomes. Predictors of diverticulitis recurrence were assessed with univariate and multiple Cox proportional hazard regression models. Results Colonic diverticulitis most commonly involved the rectosigmoid (70%, 309 of 440) and descending (30%, 133 of 440) colon segments. Complicated diverticulitis was present in 22% (98 of 440) of patients. On the basis of the results of univariate analysis, significant predictors of diverticulitis recurrence were determined to be maximum colonic wall thickness in the inflamed segment (hazard ratio [HR], 1.07 per every millimeter of increase in wall thickness; P < .001), presence of a complication (HR, 1.75; P = .002), and subjective severity of inflammation (HR, 1.36 for every increase in severity category; P value for linear trend = .003). The difference in maximum wall thickness in the inflamed segment (HR, 1.05 per millimeter; P = .016) and subjective inflammation severity (HR, 1.29 per category; P = .018)remained statistically significant in a Cox multiple regression model. Conclusion Maximum colonic wall thickness and subjective severity of acute diverticulitis allow prediction of recurrent diverticulitis and may be useful for stratifying patients according to the need for elective partial colectomy. © RSNA, 2017 Online supplemental material is available for this article.

Purpose: To evaluate the feasibility of using an objective computer-aided system to assess bladder cancer stage in CT Urography (CTU)., Materials and Methods: A dataset consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥ T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically. There were 43 cancers below stage T2 and 41 cancers at stage T2 or above. All 84 lesions were automatically segmented using our previously developed auto-initialized cascaded level sets (AI-CALS) method. Morphological and texture features were extracted. The features were divided into subspaces of morphological features only, texture features only, and a combined set of both morphological and texture features. The dataset was split into Set 1 and Set 2 for two-fold cross-validation. Stepwise feature selection was used to select the most effective features. A linear discriminant analysis (LDA), a neural network (NN), a support vector machine (SVM), and a random forest (RAF) classifier were used to combine the features into a single score. The classification accuracy of the four classifiers was compared using the area under the receiver operating characteristic (ROC) curve (A z )., Results: Based on the texture features only, the LDA classifier achieved a test A z of 0.91 on Set 1 and a test A z of 0.88 on Set 2. The test A z of the NN classifier for Set 1 and Set 2 were 0.89 and 0.92, respectively. The SVM classifier achieved test A z of 0.91 on Set 1 and test A z of 0.89 on Set 2. The test A z of the RAF classifier for Set 1 and Set 2 was 0.89 and 0.97, respectively. The morphological features alone, the texture features alone, and the combined feature set achieved comparable classification performance., Conclusion: The predictive model developed in this study shows promise as a classification tool for stratifying bladder cancer into two staging categories: greater than or equal to stage T2 and below stage T2., (© 2017 American Association of Physicists in Medicine.)

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.

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.

Corticosteroid prophylaxis is commonly used for the prevention of allergiclike reactions to iodinated and gadolinium-based contrast material in patients at highest risk of an allergiclike reaction. However, it has only a weak mitigating effect on allergiclike reactions, probably does not affect the severity of subsequent reactions, and does not prevent all reactions. Breakthrough reactions occur, are usually the same severity as the index reaction, and can occasionally be life threatening. Premedication of inpatients is likely associated with substantial cost and harm because of hospital length-of-stay prolongation; these indirect effects may exceed the benefits of premedication in this population., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to determine the frequency in which the pelvis component of an abdominopelvic CT provides information that would influence clinical management in two separate groups of patients: those with previously resected pancreatic ductal adenocarcinoma (PDA) and those with locally advanced unresectable PDA., Methods: This institutional review-board approved HIPAA compliant retrospective study with waived informed consent included 247 subjects with histologically proven PDA, including 153 subjects post-pancreaticoduodenectomy and 94 subjects with locally advanced unresectable disease. Imaging reports interpreted between January 2005 and December 2013 were obtained from our institution's Radiology Information System by searching a Cancer Registry database of PDA patients separately for the words "whipple" and "unresectable." CT findings were separated by location in the abdomen or pelvis, and subsequently reviewed and graded for their likelihood of representing metastatic disease. The probability of pelvic CT influencing clinical management-i.e., of finding isolated pelvic metastatic disease-was determined using 95% binomial proportion confidence intervals for both the post-pancreaticoduodenectomy and locally advanced unresectable groups., Results: No subjects who had undergone pancreaticoduodenectomy had an isolated pelvic metastasis on follow-up imaging (0%; 95% CI 0-2.38, p = 0.0004); 33 had metastatic disease in the abdomen, and 120 had no or equivocal evidence of abdominopelvic metastatic disease. One subject with locally advanced unresectable PDA had a possible isolated pelvic metastasis on follow-up imaging (1.1%; 95% CI 0.03-5.79, p = 0.048); 20 had metastatic disease in the abdomen, and 73 had no or equivocal evidence of abdominopelvic metastatic disease., Conclusion: Isolated pelvic metastatic disease rarely occurs in patients with PDA who have had prior pancreaticoduodenectomy or have a locally advanced unresectable primary tumor, suggesting routine pelvic CT in follow-up imaging of these patients may not be necessary., Competing Interests: The authors declare that they have no conflict of interest.

Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response.

Purpose: To determine whether resident abdominopelvic CT reports considered prospectively concordant with the final interpretation are also considered concordant by other blinded specialists and abdominal radiologists., Methods: In this institutional review board-approved retrospective cohort study, 119 randomly selected urgent abdominopelvic CT examinations with a resident preliminary report deemed prospectively "concordant" by the signing faculty were identified. Nine blinded specialists from Emergency Medicine, Internal Medicine, and Abdominal Radiology reviewed the preliminary and final reports and scored the preliminary report with respect to urgent findings as follows: 1.) concordant; 2.) discordant with minor differences; 3.) discordant with major differences that do not alter patient management; or 4.) discordant with major differences that do alter patient management. Predicted management resulting from scores of 4 was recorded. Consensus was defined as majority agreement within a specialty. Consensus major discrepancy rates (ie, scores 3 or 4) were compared to the original major discrepancy rate of 0% (0/119) using the McNemar test., Results: Consensus scores of 4 were assigned in 18% (21/119, P < .001, Emergency Medicine), 5% (6/119, P = .03, Internal Medicine), and 13% (16/119, P < .001, Abdominal Radiology) of examinations. Consensus scores of 3 or 4 were assigned in 31% (37/119, P < .001, Emergency Medicine), 14% (17/119, P < .001, Internal Medicine), and 18% (22/119, P < .001, Abdominal Radiology). Predicted management alterations included hospital status (0-4%), medical therapy (1%-4%), imaging (1%-10%), subspecialty consultation (3%-13%), nonsurgical procedure (3%), operation (1%-3%), and other (0-3%)., Conclusions: The historical low major discrepancy rate for urgent findings between resident and faculty radiologists is likely underreported., (Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate the effect of available intravenous (IV) access on the accuracy and timeliness of epinephrine administration during a surprise mock severe contrast reaction., Methods: Informed consent was waived for this prospective randomized IRB-approved study. Radiology trainees with previous annual hands-on contrast reaction training (n = 46) were randomized to one of two surprise mock contrast reactions over a 23-month period: Group 1-severe laryngeal edema with IV access present (n = 27) or Group 2-severe laryngeal edema without IV access present (n = 19). Both intramuscular (IM, Epi-Pen(®)) and IV epinephrine were available in both scenarios. Time-to-treat and epinephrine administration error rates were compared by study group and by route of administration using two-tailed Student's t test or χ (2) test. Epinephrine administration errors were correlated with training experience using Pearson's correlation., Results: Mean time to epinephrine administration was significantly faster for scenarios without IV access (Group 2: 35 ± 16 s vs. Group 1: 62 ± 49 s, p = 0.03), and for intramuscular administrations overall (IM: 42 ± 34 s vs. IV: 98 ± 46 s, p < 0.001). Epinephrine administration errors were common: (63% [17/27, Group 1] vs. 61% [11/18, Group 2], p = 1.00), had no relationship with time to most recent hands-on training (r = 0.24, p = 0.11), and were not predicted by year of post-graduate training (r = 0.04, p = 0.79)., Conclusions: Lack of IV access is associated with a faster epinephrine administration time but no improvement in epinephrine administration error rate among radiology trainees responding to a surprise mock severe contrast reaction. Annual hands-on training appears to have little effect on epinephrine administration accuracy.

Purpose: To estimate the effect of an oral 13-hour inpatient corticosteroid premedication regimen on length of stay, hospital cost, and hospital-acquired infections (HAIs) by using a combination of real and hypothetical study populations., Materials and Methods: Institutional review board approval was obtained and informed consent waived for this HIPAA-compliant retrospective study. Inpatients who received an oral 13-hour corticosteroid premedication regimen before contrast material-enhanced CT (n = 1424) from 2008 to 2013 were matched by age, sex, and year when CT was performed to a control cohort (n = 1425) of patients who underwent contrast-enhanced CT without premedication and who had similar rates of 13 comorbid diseases. Length of stay in the hospital and time from admission to CT were compared by using the Mann-Whitney U test. Rates of prospectively reported HAIs were compared by using χ(2) tests. The indirect cost and risk of HAI with premedication were estimated by using published data., Results: Premedicated inpatients had a significantly longer median length of stay (+25 hours; 158 vs 133 hours, P < .001), a significantly longer median time to CT (+25 hours, 42 vs 17 hours, respectively; P < .001), and a significantly greater risk of HAI (5.1% [72 of 1424] vs 3.1% [44 of 1424], respectively; P = .008) compared with nonpremedicated control subjects. On the basis of these data and existing references, the prolonged length of stay was estimated to result in 0.04 HAI-related deaths and a cost of $159 131 (in U.S. dollars) for each prevented reaction of any severity and 32 HAI-related deaths and a cost of $131 211 400 for each prevented reaction-related death., Conclusion: Oral 13-hour inpatient corticosteroid prophylaxis is associated with substantial cost relative to its modest benefit, and may cause more indirect harm than the direct harm that it prevents., ((©) RSNA, 2015.)

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer., Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours., Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively., Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder.