Faculty Bibliography

Access to radiology reports and images through a patient portal offers several advantages. The purpose of this study was to characterize patient's interactions with their radiology results. This was a retrospective study that evaluated radiography, ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography, exams performed between July 2020 and June 2021 for patients aged 12 and older. Exam information, access logs of radiology reports and images, and patient demographics were obtained from the electronic health record and image viewing software. Descriptive statistics were computed. The study included 1,685,239 exams. A total of 54.1% of reports were viewed. MRI and PET reports were viewed with the greatest frequency (70.2% and 67.6%, respectively); 25.5% of exam images were viewed, with the greatest frequency for MRI (40.1%). Exams were shared a total of 17,095 times and downloaded 8409 times; 64% of reports were viewed for patients aged 18-39 and 34% for patients aged 80 and greater. The rate of reports viewed was greater for patients with English as their preferred language (57.1%) compared to other languages (33.3%). Among those viewed, 56.5% of reports and 48.2% of images were viewed multiple times; 72.8% of images were viewed on smartphones, 25.8% on desktop computers, and 1.4% on tablets. Patients utilize a portal to view reports and view and share images. Continued efforts are warranted to promote the use of portals and create patient-friendly imaging results to help empower patients.

Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.

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.