Faculty Bibliography

PURPOSE/OBJECTIVE:Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI prototype with deep-learning-based image reconstruction (DL HASTE-FS) with a standard T2-FS sequence for 3 T liver MRI. METHODS:41 consecutive patients with 3 T abdominal MRI examinations including standard T2-FS and DL HASTE-FS, between 5/6/2020 and 11/23/2020, comprised the study cohort. Three radiologists independently reviewed images using a 5-point Likert scale for artifact and image quality measures, while also assessing for liver lesions. RESULTS:DL HASTE-FS acquisition time was 54.93 ± 16.69, significantly (p < .001) shorter than standard T2-FS (114.00 ± 32.98 s). DL HASTE-FS received significantly higher scores for sharpness of liver margin (4.3 vs 3.3; p < .001), hepatic vessel margin (4.2 vs 3.3; p < .001), pancreatic duct margin (4.0 vs 1.9; p < .001); in-plane (4.0 vs 3.2; p < .001) and through-plane (3.9 vs 3.4; p < .001) motion artifacts; other ghosting artifacts (4.3 vs 2.9; p < .001); and overall image quality (4.0 vs 2.9; p < .001), in addition to receiving a higher score for homogeneity of fat suppression (3.7 vs 3.4; p = .04) and liver-fat contrast (p = .03). For liver lesions, DL HASTE-FS received significantly higher scores for sharpness of lesion margin (4.4 vs 3.7; p = .03). CONCLUSION/CONCLUSIONS:Novel single-shot T2-weighted MRI with deep-learning-based image reconstruction demonstrated superior image quality compared with the standard T2-FS sequence for 3 T liver MRI, while being acquired in less than half the time.

PURPOSE/OBJECTIVE:To implement a more efficient standardized computed tomography (CT) protocoling system for emergency department (ED) patients in order to improve resident work satisfaction and wellness, and decrease lag time between ordering and protocoling a study. METHODS AND MATERIALS/METHODS:Residents recorded lag times between time of order and time of protocol for 176 CT scans between November 2018 and January 2019. Pre- and postintervention resident surveys of 7 questions utilizing a 5-point Likert scale were used to assess the perceived efficiency and overall satisfaction with the protocolling system. CT technologists received a 2-step Standardized ED CT Protocoling Guidance Sheet for common indications and would consult the radiologist for any questions. RESULTS:Lag time between order and protocol averaged 17.8 minutes. Postintervention surveys demonstrated that residents were more satisfied with the new system (100% vs 6.1%), had an overall higher job satisfaction in the ED (91% vs 12.1%), thought the system was more efficient for a single study (100% vs 15.2%) and for an entire shift (100% vs 6.1%), volume of studies was maximized (91% vs 6.1%), and the workflow allowed residents to focus on interpreting studies and communicating findings (91% vs 3%). CONCLUSION/CONCLUSIONS:The implementation of an auto-protocolling system at our institution's ED took a system which was disruptive, inefficient, and unreliable, and eliminated both lag time and variation in time between ordering and protocoling, improving time to final report. It simultaneously decreased interruptions, allowing residents to focus on study interpretation, which increased resident work satisfaction, wellness, and educational benefit.

OBJECTIVE:To assess medical school characteristics associated with school tuition. MATERIALS AND METHODS/METHODS:US medical schools' tuitions, and various medical school characteristics, were extracted from the Association of American Medical Colleges' online MSAR database, using in-state tuition when applicable. US News ranking and National Institutes of Health (NIH) award ranking from the Blue Ridge Institute for Medical Research were obtained, when available. Geographic population density was obtained using Governing magazine's online database. Cost of living estimates were obtained from online American Chamber of Commerce Research Association Cost of Living Index. Spearman correlations were determined, and multivariable linear regression was performed. RESULTS:Among 148 included medical schools, adjusted average ± standard deviation tuition was $47,612 ± $23,765 (range $12,761-$141,464). Tuition demonstrated positive correlations with regional population density (r = +0.577) and years established (r = +0.265). Among ranked schools, tuition showed negative correlations with US News rank (r = -0.469) and NIH rank (r = -0.336). Average tuition varied by geographic region: Northeast: $49,662, Midwest: $43,560, West: $37,701, and South: $34,270. Among states with at least 3 medical schools, average tuition was highest in MA ($53,520), PA ($53,034), $51,547 (DC), and lowest in TX ($21,002), FL ($30,440), LA ($36,066). At multivariable linear regression, the strongest independent predictor of tuition was US News rank (β = -396.0, P= 0.05). CONCLUSIONS:US medical school tuition is highly variable by over a 10:1 ratio. Tuition is greater in higher ranked, longer established schools, in more densely populated regions. Objective data regarding medical education quality may be warranted to assess whether higher tuition in schools with higher US News and NIH rankings is justified.

PURPOSE: The Medicare Access and CHIP Reauthorization Act (MACRA) Quality performance category is the successor to the Physician Quality and Reporting System (PQRS) program and now contributes to physicians' income adjustments based upon performance rates calculated for a minimum of six measures. We assess radiologists' frequency of reporting PQRS measures as a marker of preparedness for MACRA. METHODS: Medicare-participating radiologists were randomly searched through the Physician Compare website until identifying 1,000 radiologists who reported at least one PQRS measure. Associations were explored between the number of reported measures and radiologist characteristics. RESULTS: For PQRS-reporting radiologists, the number of reported PQRS measures was 1 (25.2%), 2 (27.3%), 3 (18.2%), 4 (19.3%), 5 (8.3%), and 6 (1.7%). The most commonly reported measures were "documenting radiation exposure time for procedures using fluoroscopy" (64.3%) and "accurate measurement of carotid artery narrowing" (56.8%). Reporting at least two measures was significantly (P < .001) more likely for nonacademic (77.3%) versus academic (44.9%) radiologists, generalists (82.7%) versus subspecialists (59.1%), and radiologists in smaller (/=100 members) (39.7%) practices. Reporting six measures was significantly (P < .05) more likely for generalists (2.6%) versus subspecialists (0.4%). CONCLUSION: Most PQRS-reporting radiologists reported only one or two measures, well below MACRA's requirement of six. Radiologists continuing such reporting levels will likely be disadvantaged in terms of potential payment adjustments under MACRA. Lower reporting rates for academic and subspecialized radiologists, as well as those in larger practices, may relate to such radiologists' reliance on their hospitals or networks for PQRS reporting. Qualified clinical data registries should be embraced to facilitate more robust measure reporting.

PURPOSE: To evaluate the impact of questionnaires completed by patients at the time of abdominopelvic CT performed for abdominal pain on the completeness of clinical information and the identification of potential causes of pain, compared with order requisitions alone. METHODS: 100 outpatient CT examinations performed for the evaluation of abdominal pain were retrospectively reviewed. The specificity of the location of pain was compared between the order requisition and patient questionnaire. An abdominal imaging fellow (Reader 1) and abdominal radiologist (Reader 2) reviewed the examinations independently in two sessions 6 weeks apart (one with only the order requisition and one also with the questionnaire). Readers recorded identified causes of pain and rated their confidence in interpretation (1-5 scale; least to greatest confidence). RESULTS: In 30% of patients, the questionnaire provided a more specific location for pain. Among these, the pain was localized to a specific quadrant in 40%. With having access to the questionnaire, both readers identified additional causes for pain not identified in session 1 (Reader 1, 8.6% [7/81]; Reader 2 5.3% [4/75]). Additional identified causes of pain included diverticulitis, cystitis, peritoneal implants, epiploic appendagitis, osseous metastatic disease, umbilical hernia, gastritis, and SMA syndrome. Confidence in interpretation was significantly greater using the questionnaire for both readers (Reader 1: 4.8 +/- 0.6 vs. 4.0 +/- 0.5; Reader 2: 4.9 +/- 0.3 vs. 4.7 +/- 0.5, p < 0.001). CONCLUSION: Patient questionnaires provide additional relevant clinical history, increased diagnostic yield, and improve radiologists' confidence. Radiology practices are encouraged to implement questionnaires and make these readily available to radiologists at the time of interpretation.

OBJECTIVE: To report a case of refractory ulcerated necrobiosis lipoidica (NL) with significant response to treatment with topical tacrolimus. SUBJECT: A 55-year-old woman without diabetes and with a previous history of NL presented to the Helen L. and Martin S. Kimmel Hyperbaric and Advanced Wound Healing Center of NYU Langone Medical Center, New York, with bilateral lower-leg ulcerations resistant to wound healing techniques at other institutions. MATERIALS AND METHODS: Repeat biopsy performed at the author's institution confirmed the diagnosis of NL. Initial therapy was based on reports of other successful treatment methods, which included collagen wound grafts and collagen-based dressings coupled with compression. These methods initially showed promising results; however, the wounds reulcerated, and any gains in wound healing were lost. Alternative options were initiated, including topical clobetasol and narrowband ultraviolet B; however, no significant improvement was observed. The patient's lower-extremity wounds began to deteriorate. The patient also refused systemic therapy. Treatment was changed to topical 0.1% tacrolimus ointment and was applied daily for 10 months with multilayer compression wraps. RESULTS: Both lower-extremity ulcerations began to show significant improvement, with the ulcers progressing toward closure except for 1 very small area on the left lower extremity. CONCLUSIONS: Topical tacrolimus seems to be an effective treatment option for patients with refractory chronic ulcerated NL who do not want systemic oral therapy. The authors found that successful wound closure may require a multimodal approach, which promotes wound healing, but also concurrently addresses the underlying disease process.

OBJECTIVE: The objective of the present study is to assess the feasibility of a novel claims-based classification system for payer identification of academic radiologist subspecialties. MATERIALS AND METHODS: Using a categorization scheme based on the Neiman Imaging Types of Service (NITOS) system, we mapped the Medicare Part B services billed by all radiologists from 2012 to 2014, assigning them to the following subspecialty categories: abdominal imaging, breast imaging, cardiothoracic imaging, musculoskeletal imaging, nuclear medicine, interventional radiology, and neuroradiology. The percentage of subspecialty work relative value units (RVUs) to total billed work RVUs was calculated for each radiologist nationwide. For radiologists at the top 20 academic departments funded by the National Institutes of Health, those percentages were compared with subspecialties designated on faculty websites. NITOS-based subspecialty assignments were also compared with the only radiologist subspecialty classifications currently recognized by Medicare (i.e., nuclear medicine and interventional radiology). RESULTS: Of 1012 academic radiologists studied, the median percentage of Medicare-billed NITOS-based subspecialty work RVUs matching the subspecialty designated on radiologists' own websites ranged from 71.3% (for nuclear medicine) to 98.9% (for neuroradiology). A NITOS-based work RVU threshold of 50% correctly classified 89.8% of radiologists (5.9% were not mapped to any subspecialty; subspecialty error rate, 4.2%). In contrast, existing Medicare provider codes identified only 46.7% of nuclear medicine physicians and 39.4% of interventional radiologists. CONCLUSION: Using a framework based on a recently established imaging health services research tool that maps service codes based on imaging modality and body region, Medicare claims data can be used to consistently identify academic radiologists by subspecialty in a manner not possible with the use of existing Medicare physician specialty identifiers. This method may facilitate more appropriate performance metrics for subspecialty academic physicians under emerging value-based payment models.

OBJECTIVE: The purpose of this study is to assess features of patient satisfaction scores for U.S. radiologists using a popular physician rating website. MATERIALS AND METHODS: Patient reviews were retrieved from the website RateMDs for all listed radiologists in all 297 U.S. cities with population 100,000 or greater. Reviews included rating scores of 1-5 (5 = highest) in four categories (staff, punctuality, knowledge, and helpfulness). Additional physician information was obtained from Medicare files. Common words in patient free-text comments were assessed. Statistical analyses were performed. RESULTS: We identified 1891 patient reviews for 1259 radiologists. In all four categories, the most common score was 5 for excellent (62.7-74.3%), and the second most common score was 1 for terrible (13.5-20.4%); scores of 2-4 were far less frequent (1.9-11.6%). Scores for all four categories highly correlated with one another (r = 0.781-0.951). Radiologists in the Northeast scored significantly lower (p < 0.001) than those elsewhere for both staff and punctuality. Radiologists attending a designated top 50 medical school showed nonsignificant trends toward lower scores for helpfulness (p = 0.073) and knowledge (p = 0.062). The most common words in free-text comments for positive reviews were "caring," "knowledgeable," and "professional." For negative reviews, "rude," "pain," and "unprofessional" were most common. CONCLUSION: Overall, most radiologists rated online by their patients score well, but reviews tended to be either strongly positive or negative. Scores across various categories are highly correlated, suggesting that there is a halo effect. Radiologists should recognize the effect of both facility- and radiologist-related factors in influencing patients' overall perceptions.

RATIONALE AND OBJECTIVES: This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. MATERIALS AND METHODS: We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. RESULTS: The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). CONCLUSIONS: Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature.