Faculty Bibliography

To use Twitter to assess the immediate public response to the United States Preventive Services Task Force (USPSTF) 2013 draft guidelines on lung cancer screening with low-dose chest CT (LDCT). The number of tweets including the phrases "lung cancer screening," "lung CT," "chest CT," "low dose computed tomography," "low dose CT," or "LDCT" was recorded for 6 days before and after guidelines release. A systematic sample of 172 tweets from the week following release was coded for user type, tweet opinion, linked article source, and article opinion. Following guidelines' release, the number of daily tweets increased from 13 +/- 8 to 311 +/- 395. The 172 tweets in the week following release were tweeted by 166 unique users including: news organizations/online news gathering accounts (34.9%), general public (21.7%), physicians (12.0%, 6 radiologists), and businesses (11.4%). 23.3% of tweets provided opinion on the guidelines (50.0% favorable, 27.5% concerned toward screening). Most (91.3%) tweets contained links to a total of 46 unique articles, which were authored by lay press (41.3%), non-peer-reviewed medical press (32.6%), and hospital/medical practice websites (10.9%). Among these, 50.0% were favorable, citing mortality reduction (87.0%), published data supporting screening (50.0%), and early detection (43.5%), while 28.3% expressed concern, including false positives (58.9%) and radiation risk (39.1%). Twitter activity rose rapidly after the USPSTF draft guidelines on LDCT. Most users were non-physicians and frequently cited non-peer-reviewed articles. Users maintained an overall favorable view of screening, while expressing various concerns. Considerable opportunity exists for greater radiologist engagement in this online public dialog.

PURPOSE: To evaluate whole-pancreas 3D-histogram ADC metrics in acute pancreatitis. METHODS: In 41 patients with acute pancreatitis undergoing MRI/MRCP with DWI, 3D-volumes-of-interest encompassing the entire pancreas were placed to derive whole-pancreas histogram ADC metrics. RESULTS: There were trends toward higher 0-10th percentile ADC, higher 10-25th percentile ADC, lower skewness, and higher kurtosis in patients with new complications (p=0.065-0.095). Conventional mean ADC showed no association with new complications (p=0.203). Kurtosis had highest area-under-the-curve (0.784) for predicting new complications (sensitivity=75.0%; specificity=91.9%). CONCLUSION: Findings suggest whole-pancreas histogram ADC metrics assist early management of acute pancreatitis, (e.g., patient selection for more intensive monitoring/intervention).

OBJECTIVE: The purpose of this study is to evaluate the roles of self-directed learning and continual feedback in the learning curve for tumor detection by novice readers of prostate MRI. MATERIALS AND METHODS: A total of 124 prostate MRI examinations classified as positive (n = 52; single Prostate Imaging Reporting and Data System [PI-RADS] category 3 or higher lesion showing Gleason score >/= 7 tumor at MRI-targeted biopsy) or negative (n = 72; PI-RADS category 2 or lower and negative biopsy) for detectable tumor were included. These were divided into four equal-sized batches, each with matching numbers of positive and negative examinations. Six second-year radiology residents reviewed examinations to localize tumors. Three of the six readers received feedback after each examination showing the preceding case's solution. The learning curve, plotting accuracy over time, was assessed by the Akaike information criterion (AIC). Logistic regression and mixed-model ANOVA were performed. RESULTS: For readers with and without feedback, the learning curve exhibited an initial rapid improvement that slowed after 40 examinations (change in AIC > 0.2%). Accuracy improved from 58.1% (batch 1) to 71.0-75.3% (batches 2-4) without feedback and from 58.1% to 72.0-77.4% with feedback (p = 0.027-0.046), without a difference in the extent of improvement (p = 0.800). Specificity improved from 53.7% to 68.5-81.5% without feedback and from 55.6% to 74.1-81.5% with feedback (p = 0.006-0.010), without a difference in the extent of improvement (p = 0.891). Sensitivity improved from 59.0-61.5% (batches 1-2) to 71.8-76.9% (batches 3-4) with feedback (p = 0.052), though did not improve without feedback (p = 0.602). Sensitivity for transition zone tumors exhibited larger changes (p = 0.024) with feedback than without feedback. Sensitivity for peripheral zone tumors did not improve in either group (p > 0.3). Reader confidence increased only with feedback (p < 0.001). CONCLUSION: The learning curve in prostate tumor detection largely reflected self-directed learning. Continual feedback had a lesser effect. Clinical prostate MRI interpretation by novice radiologists warrants caution.

PURPOSE: The aim of this study was to evaluate patient satisfaction scores for interventional radiologists (IRs) across the United States using a leading physician ratings website. METHODS: The physician ratings website Healthgrades was manually queried for all 2,774 Medicare-participating self-designated IRs. All patient-reviewed IRs for whom the primary "likelihood of recommending to family and friends" field was scored were included, resulting in 781 included IRs. Physician characteristics were extracted from Medicare data sets. All available patient satisfaction scores (1 [poor] to 5 [excellent]: likelihood to recommend, ease of scheduling, office environment, staff friendliness, trust in physician's decisions, how well physician explains condition, how well physician listens and answers questions, whether physician spends appropriate time with patients) and wait times were extracted from Healthgrades. Associations among measures were explored. RESULTS: IRs' mean likelihood-to-recommend score was 4.3 +/- 1.2 (median, 5.0; 64.5% received a score of 5; 10.5% received scores < 3). Mean scores ranged from 4.4 to 4.5 for office-related factors and from 4.3 to 4.5 for physician-related factors. Likelihood-to-recommend scores showed substantial correlations with office-related factors (r = 0.738 to 0.780) and physician-related factors (r = 0.918 to 0.946). Likelihood to recommend was significantly higher for IRs with shorter wait times (P < .001) but was not associated with physician gender or geographic region (P = 0.370-0.791), nor was there any correlation with physician age, years since graduation, or group practice size (r = -0.089 to 0.096). CONCLUSIONS: Satisfaction scores on a leading physician ratings website generally range from very good to excellent for US IRs. Most patients leaving reviews are likely to recommend their own IRs to friends or family members. The likelihood to recommend is strongly associated with differences in wait times.

PURPOSE: In implementing the Merit-Based Incentive Payment System (MIPS), CMS will provide special considerations to physicians with infrequent face-to-face patient encounters by reweighting MIPS performance categories to account for the unique circumstances facing these providers. The aim of this study was to determine the impact of varying criteria on the fraction of radiologists who are likely to receive special considerations for performance assessment under MIPS. METHODS: Data from the 2014 Medicare Physician and Other Supplier file for 28,710 diagnostic radiologists were used to determine the fraction of radiologists meeting various proposed criteria for receiving special considerations. For each definition, the fraction of patient-facing encounters among all billed codes was determined for those radiologists not receiving special considerations. RESULTS: When using the criterion proposed by CMS that physicians will receive special considerations if billing