Faculty Bibliography

Purpose To compare standard diffusion-weighted (DW) imaging and diffusion kurtosis (DK) imaging for prostate cancer (PC) detection and characterization in a large patient cohort, with attention to the potential added value of DK imaging. Materials and Methods This retrospective institutional review board-approved study received a waiver of informed consent. Two hundred eighty-five patients with PC underwent 3.0-T phased-array coil prostate magnetic resonance (MR) imaging, including a DK imaging sequence (b values 0, 500, 1000, 1500, and 2000 sec/mm2) before prostatectomy. Maps of apparent diffusion coefficient (ADC) and diffusional kurtosis (K) were derived by using maximal b values of 1000 and 2000 sec/mm2, respectively. Mean ADC and K were obtained from volumes of interest (VOIs) placed on each patient's dominant tumor and benign prostate tissue. Metrics were compared between benign and malignant tissue, between Gleason score (GS) /= 3 + 4 tumors, and between GS /= 4 + 3 tumors by using paired t tests, analysis of variance, receiver operating characteristic (ROC) analysis, and exact tests. Results ADC and K showed significant differences for benign versus tumor tissues, GS /= 3 + 4 tumors, and GS /= 4 + 3 tumors (P < .001 for all). ADC and K were highly correlated (r = -0.82; P < .001). Area under the ROC curve was significantly higher (P = .002) for ADC (0.921) than for K (0.902) for benign versus malignant tissue but was similar for GS /= 3 + 4 tumors (0.715-0.744) and GS /= 4 + 3 tumors (0.694-0.720) (P > .15). ADC and K were concordant for these various outcomes in 80.0%-88.6% of patients; among patients with discordant results, ADC showed better performance than K for GS /= 4 + 3 tumors (P = .016) and was similar to K for other outcomes (P > .136). Conclusion ADC and K were highly correlated, had similar diagnostic performance, and were concordant for the various outcomes in the large majority of cases. These observations did not show a clear added value of DK imaging compared with standard DW imaging for clinical PC evaluation. (c) RSNA, 2017 Online supplemental material is available for this article.

The annual cost of healthcare delivery in the USA now exceeds US$3 trillion. Fee for service methodology is often implicated as a cause of this exceedingly high figure. The Affordable Care Act created the Center for Medicare and Medicaid Innovation (CMMI) to pilot test value based alternative payments for reimbursing physician services. In 2015, the Medicare Access and CHIP Reauthorization Act (MACRA) was passed into law. MACRA has dramatic implications for all US based healthcare providers. MACRA permanently repealed the Medicare Sustainable Growth Rate so as to stabilize physician part B Medicare payments, consolidated pre-existing federal performance programs into the Merit based Incentive Payments System (MIPS), and legislatively mandated new approaches to paying clinicians. Neurointerventionalists will predominantly participate in MIPS. MIPS unifies, updates, and streamlines previously existing federal performance programs, thereby reducing onerous redundancies and overall administrative burden, while consolidating performance based payment adjustments. While MIPS may be perceived as a straightforward continuation of fee for service methodology with performance modifiers, MIPS is better viewed as a stepping stone toward eventually adopting alternative payment models in later years. In October 2016, the Centers for Medicare and Medicaid Services (CMS) released a final rule for MACRA implementation, providing greater clarity regarding 2017 requirements. The final rule provides a range of options for easing MIPS reporting requirements in the first performance year. Nonetheless, taking the newly offered 'minimum possible' approach toward meeting the requirements will still have negative consequences for providers.

RATIONALE AND OBJECTIVES: Emerging alternative metrics leverage social media and other online platforms to provide immediate measures of biomedical articles' reach among diverse public audiences. We aimed to compare traditional citation and alternative impact metrics for articles in popular general radiology journals. MATERIALS AND METHODS: All 892 original investigations published in 2013 issues of Academic Radiology, American Journal of Roentgenology, Journal of the American College of Radiology, and Radiology were included. Each article's content was classified as imaging vs nonimaging. Traditional journal citations to articles were obtained from Web of Science. Each article's Altmetric Attention Score (Altmetric), representing weighted mentions across a variety of online platforms, was obtained from Altmetric.com. Statistical assessment included the McNemar test, the Mann-Whitney test, and the Pearson correlation. RESULTS: Mean and median traditional citation counts were 10.7 +/- 15.4 and 5 vs 3.3 +/- 13.3 and 0 for Altmetric. Among all articles, 96.4% had >/=1 traditional citation vs 41.8% for Altmetric (P < 0.001). Online platforms for which at least 5% of the articles were represented included Mendeley (42.8%), Twitter (34.2%), Facebook (10.7%), and news outlets (8.4%). Citations and Altmetric were weakly correlated (r = 0.20), with only a 25.0% overlap in terms of articles within their top 10th percentiles. Traditional citations were higher for articles with imaging vs nonimaging content (11.5 +/- 16.2 vs 6.9 +/- 9.8, P < 0.001), but Altmetric scores were higher in articles with nonimaging content (5.1 +/- 11.1 vs 2.8 +/- 13.7, P = 0.006). CONCLUSIONS: Although overall online attention to radiology journal content was low, alternative metrics exhibited unique trends, particularly for nonclinical articles, and may provide a complementary measure of radiology research impact compared to traditional citation counts.

In early 2015, Sylvia Burwell, Secretary of the Department of Health and Human Services, described the federal administration's goals for delivery of health care in the United States. Prominently featured was a conversion from volume to value through the incorporation of Alternative Payment Models. The Department of Health and Human Services laid the framework, but recognized significant knowledge gaps in how providers and institutions would develop Alternative Payment Models. To that end, the Health Care Payment Learning and Action Network was conceived. On March 25, 2015, the Health Care Payment Learning and Action Network held its first meeting, which included a broad swath of industry participants. This collaboration was considered mission critical to achieving success in the goals of advancing Alternative Payment Models. This article highlights the Health Care Payment Learning and Action Network and the framework it is proposing for Alternative Payment Models that would have meaningful implications for radiologists.

OBJECTIVE: Prior studies in prostate diffusion-weighted magnetic resonance imaging (MRI) have largely explored the impact of b-value and diffusion directions on estimated diffusion coefficient D. Here we suggest varying diffusion time, t, to study time-dependent D(t) in prostate cancer, thereby adding an extra dimension in the development of prostate cancer biomarkers. METHODS: Thirty-eight patients with peripheral zone prostate cancer underwent 3-T MRI using an external-array coil and a diffusion-weighted image sequence acquired for b = 0, as well as along 12 noncollinear gradient directions for b = 500 s/mm using stimulated echo acquisition mode (STEAM) diffusion tensor imaging (DTI). For this sequence, 6 diffusion times ranging from 20.8 to 350 milliseconds were acquired. Tumors were classified as low-grade (Gleason score [GS] 3 + 3; n = 11), intermediate-grade (GS 3 + 4; n = 16), and high-grade (GS >/=4 + 3; n = 11). Benign peripheral zone and transition zone were also studied. RESULTS: Apparent diffusion coefficient (ADC) D(t) decreased with increasing t in all zones of the prostate, though the rate of decay in D(t) was different between sampled zones. Analysis of variance and area under the curve analyses suggested better differentiation of tumor grades at shorter t. Fractional anisotropy (FA) increased with t for all regions of interest. On average, highest FA was observed within GS 3 + 3 tumors. CONCLUSIONS: There is a measurable time dependence of ADC in prostate cancer, which is dependent on the underlying tissue and Gleason score. Therefore, there may be an optimal selection of t for prediction of tumor grade using ADC. Controlling t should allow ADC to achieve greater reproducibility between different sites and vendors. Intentionally varying t enables targeted exploration of D(t), a previously overlooked biophysical phenomenon in the prostate. Its further microstructural understanding and modeling may lead to novel diffusion-derived biomarkers.

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.

The Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 describes alternative payment models (APMs) as new approaches to health care payment that incentivize higher quality and value. MACRA incentivizes increasing APM participation by all physician specialties over the coming years. Some APMs will be deemed Advanced APMs; clinicians who are a Qualifying Participant in an Advanced APM will receive substantial benefits under MACRA including an automatic 5% payment bonus, regardless of their performance and savings within the APM, and a larger payment rate increase beginning in 2026. Existing APMs are most relevant to primary care physicians, and opportunities for radiologists to participate in Advanced APMs fulfilling Qualified Participant requirements are limited. Physician-Focused Payment Models (PFPMs), as described in MACRA, are APMs that target physicians' Medicare payments based on quality and cost of physician services. PFPMs must address a new issue or specialty compared with existing APMs and will thus foster a more diverse range of APMs encompassing a wider range of specialties. The PFPM Technical Advisory Committee is a new independent agency that will review proposals for new PFPMs and provide recommendations to CMS regarding their approval. The PFPM Technical Advisory Committee comprises largely primary care physicians and health policy experts and is not required to consult clinical experts when reviewing new specialist-proposed PFPMs. As PFPMs provide a compelling opportunity for radiologists to demonstrate and be rewarded for their unique contributions toward patient care, radiologists should embrace this new model and actively partner with other stakeholders in developing radiology-relevant PFPMs.

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.

PURPOSE: The aim of this study was to assess relationships between county-level variation in Medicare beneficiary imaging resource consumption and measures of population economic status. METHODS: The 2013 CMS Geographic Variation Public Use File was used to identify county-level per capita Medicare fee-for-service imaging utilization and nationally standardized costs to the Medicare program. The County Health Rankings public data set was used to identify county-level measures of population economic status. Regional variation was assessed, and multivariate regressions were performed. RESULTS: Imaging events per 1,000 Medicare beneficiaries varied 1.8-fold (range, 2,723-4,843) at the state level and 5.3-fold (range, 1,228-6,455) at the county level. Per capita nationally standardized imaging costs to Medicare varied 4.2-fold (range, $84-$353) at the state level and 14.1-fold (range, $33-$471) at the county level. Within individual states, county-level utilization varied on average 2.0-fold (range, 1.1- to 3.1-fold), and costs varied 2.8-fold (range, 1.1- to 6.4-fold). For both large urban populations and small rural states, Medicare imaging resource consumption was heterogeneously variable at the county level. Adjusting for county-level gender, ethnicity, rural status, and population density, countywide unemployment rates showed strong independent positive associations with Medicare imaging events (beta = 26.96) and costs (beta = 4.37), whereas uninsured rates showed strong independent positive associations with Medicare imaging costs (beta = 2.68). CONCLUSIONS: Medicare imaging utilization and costs both vary far more at the county than at the state level. Unfavorable measures of county-level population economic status in the non-Medicare population are independently associated with greater Medicare imaging resource consumption. Future efforts to optimize Medicare imaging use should consider the influence of local indigenous socioeconomic factors outside the scope of traditional beneficiary-focused policy initiatives.

On 8 November 2016 the American electorate voted Donald Trump into the Presidency and a majority of Republicans into both houses of Congress. Since many Republicans ran for elected office on the promise to 'repeal and replace' Obamacare, this election result came with an expectation that campaign rhetoric would result in legislative action on healthcare. The American Health Care Act (AHCA) represented the Republican effort to repeal and replace the Affordable Care Act (ACA). Key elements of the AHCA included modifications of Medicaid expansion, repeal of the individual mandate, replacement of ACA subsidies with tax credits, and a broadening of the opportunity to use healthcare savings accounts. Details of the bill and the political issues which ultimately impeded its passage are discussed here.