Faculty Bibliography

PURPOSE: Incidental ovarian cysts are frequently detected on imaging. Despite published follow-up consensus statements, there remains variability in radiologist follow-up recommendations and clinician practice patterns. The aim of this study was to evaluate if collaborative ovarian cyst management recommendations and a radiologist decision support tool can improve adherence to follow-up recommendations. METHODS: Gynecologic oncologists and abdominal radiologists convened to develop collaborative institutional recommendations for the management of incidental, asymptomatic simple ovarian cysts detected on ultrasound, CT, and MRI. The recommendations were developed by modifying the published consensus recommendations developed by the Society of Radiologists in Ultrasound on the basis of local practice patterns and the experience of the group members. A less formal process involved the circulation of the published consensus recommendations, followed by suggestions for revisions and subsequent consensus, in similar fashion to the ACR Incidental Findings Committee II. The recommendations were developed by building on the published work of experienced groups to provide the authors' medical community with a set of recommendations that could be endorsed by both the Department of Gynecology and the Department of Radiology to provide supportive guidance to the clinicians who manage incidental ovarian cysts. The recommendations were integrated into a radiologist decision support tool accessible from the dictation software. Nine months after tool launch, institutional review board approval was obtained, and radiology reports mentioning ovarian cysts in the prior 34 months were retrospectively reviewed. For cysts detected on ultrasound, adherence rates to Society of Radiologists in Ultrasound recommendations were calculated for examinations before tool launch and compared with adherence rates to the collaborative institutional recommendations after tool launch. Additionally, electronic medical records were reviewed to determine the follow-up chosen by the clinician. RESULTS: For cysts detected on ultrasound, radiologist adherence to recommendations improved from 50% (98 of 197) to 80% (111 of 139) (P < .05). Overmanagement decreased from 34% (67 of 197) to 10% (14 of 139) (P < .05). A recommendation was considered "overmanaged" if the radiologist recommended follow-up when it was not indicated or if the recommended follow-up time was at a shorter interval than indicated. Clinician adherence to radiologist recommendations showed statistically nonsignificant improvement from 49% (36 of 73) to 57% (27 of 47) (P = .5034). CONCLUSIONS: Management recommendations developed through collaboration with clinicians may help standardize follow-up of ovarian cysts and reduce overutilization.

RATIONALE AND OBJECTIVES: This study aimed to assess the most common publication types in radiology journals, as well as temporal trends and association with citation frequency. MATERIALS AND METHODS: PubMed was searched to extract all published articles having the following "Publication Type" indices: "validation studies," "meta-analysis," "clinical trial," "comparative study," "evaluation study," "guideline," "multicenter study," "randomized study," "review," "editorial," "case report," and "technical report." The percentage of articles within each category published within clinical radiology journals was computed. Normalized percentages for each category were also computed on an annual basis. Citation counts within a 2-year window following publication were obtained using Web of Science. Overall trends were assessed. RESULTS: Publication types with the highest fraction in radiology journals were technical reports, evaluation studies, and case reports (4.8% to 5.8%). Publication types with the lowest fraction in radiology journals were randomized trials, multicenter studies, and meta-analyses (0.8% to 1.5%). Case reports showed a significant decrease since 1999, with accelerating decline since 2007 (P = 0.002). Publication types with highest citation counts were meta-analyses, guidelines, and multicenter studies (8.1 +/- 10.7 to 12.9 +/- 5.1). Publication types with lowest citation counts were case reports, editorials, and technical reports (1.4 +/- 2.4 to 2.9 +/- 4.3). The representation in radiology journals and citation frequency of the publication types showed weak inverse correlation (r = -0.372). CONCLUSIONS: Radiology journals have historically had relatively greater representation of less frequently cited publication types. Various strategies, including methodological training, multidisciplinary collaboration, national support networks, as well as encouragement of higher level of evidence by funding agencies and radiology journals themselves, are warranted to improve the impact of radiological research.

PURPOSE: To assess information regarding radiology practices on public transparency Web sites. METHODS: Eight Web sites comparing radiology centers' price and quality were identified. Web site content was assessed. RESULTS: Six of eight Web sites reported examination prices. Other reported information included hours of operation (4/8), patient satisfaction (2/8), American College of Radiology (ACR) accreditation (3/8), on-site radiologists (2/8), as well as parking, accessibility, waiting area amenities, same/next-day reports, mammography follow-up rates, examination appropriateness, radiation dose, fellowship-trained radiologists, and advanced technologies (1/8 each). CONCLUSION: Transparency Web sites had a preponderance of price (and to a lesser extent service quality) information, risking fostering price-based competition at the expense of clinical quality.

OBJECTIVE: The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). MATERIALS AND METHODS: This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. RESULTS: For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. CONCLUSION: Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

PURPOSE: The goal of this study was to use patient reviews posted on Yelp.com, an online ratings website, to identify factors most commonly associated with positive versus negative patient perceptions of radiology imaging centers across the United States. METHODS: A total of 126 outpatient radiology centers from the 46 largest US cities were identified using Yelp.com; 1,009 patient reviews comprising 2,582 individual comments were evaluated. Comments were coded as pertaining to either the radiologist or other service items, and as expressing either a positive or negative opinion. Distribution of comments was compared with center ratings using Fisher's exact test. RESULTS: Overall, 14% of comments were radiologist related; 86% pertained to other aspects of service quality. Radiologist-related negative comments more frequent in low-performing centers (mean rating /=4) pertained to imaging equipment (25% versus 7%), report content (25% versus 2%), and radiologist professionalism (25% versus 2%) (P < .010). Other service-related negative comments more frequent in low-performing centers pertained to receptionist professionalism (70% versus 21%), billing (65% versus 10%), wait times (60% versus 26%), technologist professionalism (55% versus 12%), scheduling (50% versus 17%), and physical office conditions (50% versus 5%) (P < .020). Positive comments more frequent in high-performing centers included technologist professionalism (98% versus 55%), receptionist professionalism (79% versus 50%), wait times (72% versus 40%), and physical office conditions (64% versus 25%) (P < .020). CONCLUSIONS: Patients' perception of radiology imaging centers is largely shaped by aspects of service quality. Schedulers, receptionists, technologists, and billers heavily influence patient satisfaction in radiology. Thus, radiologists must promote a service-oriented culture throughout their practice.

Rapid growth in the amount of data that is electronically recorded as part of routine clinical operations has generated great interest in the use of Big Data methodologies to address clinical and research questions. These methods can efficiently analyze and deliver insights from high-volume, high-variety, and high-growth rate datasets generated across the continuum of care, thereby forgoing the time, cost, and effort of more focused and controlled hypothesis-driven research. By virtue of an existing robust information technology infrastructure and years of archived digital data, radiology departments are particularly well positioned to take advantage of emerging Big Data techniques. In this review, we describe four areas in which Big Data is poised to have an immediate impact on radiology practice, research, and operations. In addition, we provide an overview of the Big Data adoption cycle and describe how academic radiology departments can promote Big Data development.