Faculty Bibliography

Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.

Background: Multiple commercial and open-source software applications are available for texture analysis. Nonstandard techniques can cause undesirable variability that impedes result reproducibility and limits clinical utility. Objective: The purpose of this study is to measure agreement of texture metrics extracted by 6 software packages. Methods: This retrospective study included 40 renal cell carcinomas with contrast-enhanced CT from The Cancer Genome Atlas and Imaging Archive. Images were analyzed by 7 readers at 6 sites. Each reader used 1 of 6 software packages to extract commonly studied texture features. Inter and intra-reader agreement for segmentation was assessed with intra-class correlation coefficients. First-order (available in 6 packages) and second-order (available in 3 packages) texture features were compared between software pairs using Pearson correlation. Results: Inter- and intra-reader agreement was excellent (ICC 0.93-1). First-order feature correlations were strong (r>0.8, p<0.001) between 75% (21/28) of software pairs for mean and standard deviation, 48% (10/21) for entropy, 29% (8/28) for skewness, and 25% (7/28) for kurtosis. Of 15 second-order features, only co-occurrence matrix correlation, grey-level non-uniformity, and run-length non-uniformity showed strong correlation between software packages (0.90-1, p<0.001). Conclusion: Variability in first and second order texture features was common across software configurations and produced inconsistent results. Standardized algorithms and reporting methods are needed before texture data can be reliably used for clinical applications. Clinical Impact: It is important to be aware of variability related to texture software processing and configuration when reporting and comparing outputs.

RATIONALE AND OBJECTIVES/OBJECTIVE:To determine the impact of COVID-19 workflow changes on patient throughput at the outpatient imaging facilities of a large healthcare system in New York City. MATERIALS AND METHODS/METHODS:COVID-19 workflow changes to permit social distancing and patient and staff safety included screening at the time of scheduling, encouraging patients to use our digital platform to complete registration/safety forms prior to appointments, stationing screeners at all entrances, limiting waiting room capacity, implementing a texting system to notify patients of delays, limiting dressing room use by encouraging patients to wear exam-appropriate clothing, and accelerating MRI protocols without reducing image quality. We assessed patients' pre-exam wait times, MR exam times, overall time spent on site, and registration for and use of the digital portal before (February 2020) and after (June 2020) implementation of these measures. RESULTS:Across 17 outpatient imaging centers, workflow changes resulted in a 23.1% reduction (-6.8 minutes) in all patients' pre-exam wait times (p <0.00001). Pre-exam wait times for MRI, CT, ultrasound, x-ray, and mammography decreased 28.4% (-10.3 minutes), 16.5% (-6.7 minutes), 25.3% (-7.7 minutes), 22.8% (-3.7 minutes), and 23.9% (-5.0 minutes), respectively (p < 0.00001 for all). MR exam times decreased 9.7% (-3.5 minutes) and patients' overall time on site decreased 15.2% (-8.0 minutes). The proportions of patients actively using the digital patient portal (56.1%-70.1%) and completing forms electronically prior to arrival (24.9%-47.1%) increased (p < 0.0001 for both). CONCLUSION/CONCLUSIONS:Workflow changes necessitated by the COVID-19 pandemic to ensure safety of patients and staff have permitted higher outpatient throughput.

PURPOSE/OBJECTIVE:There is substantial variation in the radiologic terms used to characterize renal masses, leading to ambiguity and inconsistency in clinical radiology reports and research studies. The purpose of this study was to develop a standardized lexicon to describe renal masses at CT and MRI. MATERIALS AND METHODS/METHODS:This multi-institutional, prospective, quality improvement project was exempt from IRB oversight. Thirteen radiologists belonging to the Society of Abdominal Radiology (SAR) disease-focused panel on renal cell carcinoma representing nine academic institutions participated in a modified Delphi process to create a lexicon of terms used to describe imaging features of renal masses at CT and MRI. In the first round, members voted on terms to be included and proposed definitions; subsequent voting rounds and a teleconference established consensus. One non-voting member developed the questionnaire and consolidated responses. Consensus was defined as ≥ 80% agreement. RESULTS:Of 37 proposed terms, 6 had consensus to be excluded. Consensus for inclusion was reached for 30 of 31 terms (13/14 basic imaging terms, 8/8 CT terms, 6/6 MRI terms and 3/3 miscellaneous terms). Despite substantial initial disagreement about definitions of 'renal mass,' 'necrosis,' 'fat,' and 'restricted diffusion' in the first round, consensus for all was eventually reached. Disagreement remained for the definition of 'solid mass.' CONCLUSIONS:A modified Delphi method produced a lexicon of preferred terms and definitions to be used in the description of renal masses at CT and MRI. This lexicon should improve clarity and consistency of radiology reports and research related to renal masses.

OBJECTIVE. The purposes of this study were to evaluate the outcome of new arterial phase enhancing nodules at MRI of cirrhotic livers, including clinical and imaging factors that affect progression to hepatocellular carcinoma (HCC), and to assess the diagnostic performance of Liver Imaging Reporting and Data System version 2018 (LI-RADSv2018) versus version 2017 (LI-RADSv2017) in categorizing these nodules. MATERIALS AND METHODS. A database search identified 129 new arterial phase enhancing, round, solid, space-occupying nodules in 79 patients with cirrhosis who underwent surveillance MRI. Three readers assessed the nodules for LI-RADS findings and made assessments based on the 2017 and 2018 criteria. Clinical information and laboratory values were collected. Outcome data were assessed on the basis of follow-up imaging and pathology results. Interreader agreement was assessed. Logistic regression and ROC curve analyses were used to assess the utility of the features for prediction of progression to HCC. RESULTS. Of the 129 nodules, 71 (55%) progressed to HCC. LI-RADSv2017 score, LIRADSv2018 score, and mild-to-moderate T2 hyperintensity were significant independent predictors of progression to HCC in univariate analyses. Serum α-fetoprotein level, hepatitis B or C virus infection as the cause of liver disease, and presence of other HCCs were significant predictors of progression to HCC in multivariate analyses. The rates of progression of LI-RADS category 3 and 4 observations were 38.1% and 57.6%, respectively, for LI-RADSv2017 and 44.4% and 69.9%, respectively, for LI-RADSv2018. CONCLUSION. New arterial phase enhancing nodules in patients with cirrhosis frequently progress to HCC. Factors such as serum α-fetoprotein level and presence of other HCCs are strong predictors of progression to HCC.

OBJECTIVE:Guideline-concordant follow-up of incidental lung nodules (ILNs) is suboptimal. We aimed to improve communication and tracking for follow-up of these common incidental findings detected on imaging examinations. METHODS:We implemented a process improvement program for reporting and tracking ILNs at a large urban academic health care system. A multidisciplinary committee designed, tested, and implemented a multipart tracking system in the electronic health record (EHR) that included Fleischner Society management recommendations for each patient. Plan-do-study-act cycles addressed gaps in the follow-up of ILNs, broken into phases of developing and testing components of the conceived EHR toolkit. RESULTS:The program resulted in standardized text macros with discrete categories and recommendations for ILNs, with ability to track each case in a work list within the EHR. The macros incorporated evidence-based guidelines and also input of collaborating clinical referrers in the respective specialty. The ILN macro was used 3,964 times over the first 2 years, increasing from 104 to over 300 uses per month. Usage spread across all subspecialty divisions, with nonthoracic radiologists currently accounting for 80% (56 of 70) of the radiologists using the system and 31% (1,230 of 3,964) of all captured ILNs. When radiologists indicated ILNs as warranting telephone communication to provider offices, completion was documented in 100% of the cases captured in the EHR-embedded tracking report. CONCLUSION/CONCLUSIONS:An EHR-based system for managing incidental nodules enables case tracking with exact recommendations, provider communication, and completion of follow-up testing. Future efforts will target consistent radiologist use of the system and follow-up completion.

INTRODUCTION/BACKGROUND:Communication and physician burn out are major issues within Radiology. This study is designed to determine the utilization and cost benefit of a hybrid computer/human communication tool to aid in relay of clinically important imaging findings. MATERIAL AND METHODS/METHODS:Analysis of the total number of tickets, (requests for assistance) placed, the type of ticket and the turn-around time was performed. Cost analysis of a hybrid computer/human communication tool over a one-year period was based on human costs as a multiple of the time to close the ticket. Additionally, we surveyed a cohort of radiologists to determine their use of and satisfaction with this system. RESULTS:14,911 tickets were placed in the 6-month period, of which 11,401 (76.4%) were requests to "Get the Referring clinician on the phone." The mean time to resolution (TTR) of these tickets was 35.3 (±17.4) minutes. Ninety percent (72/80) of radiologists reported being able to interpret a new imaging study instead of waiting to communicate results for the earlier study, compared to 50% previously. 87.5% of radiologists reported being able to read more cases after this system was introduced. The cost analysis showed a cost savings of up to $101.12 per ticket based on the length of time that the ticket took to close and the total number of placed tickets. CONCLUSIONS:A computer/human communication tool can be translated to significant time savings and potentially increasing productivity of radiologists. Additionally, the system may have a cost savings by freeing the radiologist from tracking down referring clinicians prior to communicating findings.

PURPOSE/OBJECTIVE:To determine the utility of an automated radiology-pathology feedback tool. METHODS:We previously developed a tool that automatically provides radiologists with pathology results related to imaging examinations they interpreted. The tool also allows radiologists to mark the results as concordant or discordant. Five abdominal radiologists prospectively scored their own discordant results related to their previously interpreted abdominal ultrasound, CT, and MR interpretations between August 2017 and June 2018. Radiologists recorded whether they would have followed up on the case if there was no automated alert, reason for the discordance, whether the result required further action, prompted imaging rereview, influenced future interpretations, enhanced teaching files, or inspired a research idea. RESULTS:There were 234 total discordances (range 30-66 per radiologist), and 70.5% (165 of 234) of discordances would not have been manually followed up in the absence of the automated tool. Reasons for discordances included missed findings (10.7%; 25 of 234), misinterpreted findings (29.1%; 68 of 234), possible biopsy sampling error (13.3%; 31 of 234), and limitations of imaging techniques (32.1%; 75/234). In addition, 4.7% (11 of 234) required further radiologist action, including report addenda or discussion with referrer or pathologist, and 93.2% (218 of 234) prompted radiologists to rereview the images. Radiologists reported that they learned from 88% (206 of 234) of discordances, 38.6% (90 of 233) of discordances probably or definitely influenced future interpretations, 55.6% (130 of 234) of discordances prompted the radiologist to add the case to his or her teaching files, and 13.7% (32 of 233) inspired a research idea. CONCLUSION/CONCLUSIONS:Automated pathology feedback provides a valuable opportunity for radiologists across experience levels to learn, increase their skill, and improve patient care.

OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.