Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors.

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.

A Survey of Practicing Radiologists on the Use of Premedication Before Intravenous Iodinated Contrast Medium Administration.

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.

Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study.

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.

Cocaine nephropathy: A rare cause of abnormal nephrograms.

Cocaine use is associated with a variety of renal injuries. Although rhabdomyolysis is the most common cause of cocaine-induced nephropathy, cocaine can also cause renal vasculitis, acute interstitial nephritis, acute tubular necrosis, thrombotic microangiopathy, and renal infarction. We present a rare case of cocaine-induced nephropathy in a 30-year-old male who presented with acute kidney injury and abnormal nephrograms at contrast-enhanced computed tomography. Mechanisms of cocaine-induced renal injury and differential causes of abnormal nephrograms encountered at imaging are discussed. Cocaine-induced nephropathy is a rare but important cause of abnormal nephrograms and should be considered in the differential diagnosis when clinically appropriate.

Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support.

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.

Video Interviewing: A Review and Recommendations for Implementation in the Era of COVID-19 and Beyond.

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.

Influence of Clinical Factors on Risk of Contrast-Induced Nephrotoxicity From IV Iodinated Low-Osmolality Contrast Material in Patients With a Low Estimated Glomerular Filtration Rate.

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 m2 and 86 patients with an eGFR < 30 mL/min/1.73 m2 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 m2 (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 m2 (p = 0.24). In patients with an eGFR of less than 30 mL/min/1.73 m2, 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 m2, 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 m2. In those with an eGFR of 30-44 mL/min/1.73 m2, 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.

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

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.

U-Net based deep learning bladder segmentation in CT urography.

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.

Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

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.

Needs Assessment Using a Structured Prioritization Schema: An Open Letter to PACS Vendors.

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.

Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support.

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.

Radiographic stool quantification: an equivalence study of 484 symptomatic and asymptomatic subjects.

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.

A pictorial review of bladder cancer nodal metastases.

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.

Hemostatic Agents and Tissue Sealants: Potential Mimics of Abdominal Abnormalities.

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.

(Lack of) Measurable Clinical or Knowledge Gains From Resident Participation in Noon Conference.

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, ß = 0.361-0.516) and less faculty-to-resident feedback (P < 0.0001, ß = [-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.

Novel Quality Indicators for Radiologists Interpreting Abdominopelvic CT Images: Risk-Adjusted Outcomes Among Emergency Department Patients With Right Lower Quadrant Pain.

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.

Imaging appearance of fibrosing diseases of the retroperitoneum: can a definitive diagnosis be made?

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.