Temporal Trends in Imaging Utilization for Suspected Substance Use Disorder in an Academic Emergency Radiology Department

PurposeTo assess temporal trends and utilization patterns of diagnostic imaging performed for substance use disorder (SUD)-related indications in an academic radiology emergency department (ED).MethodsRetrospective analyses of ED imaging examinations acquired from 2005 to 2015 were performed. Imaging examinations performed for suspected SUD-related indications, based on the order history, were compared with those without a SUD-related indication. Unadjusted analyses comparing demographic and imaging characteristics between SUD-related versus non-SUD-related indications used Wilcoxon and Pearson’s χ² tests. Multivariable logistic regression models, within each imaging modality subgroup and combined, were employed to examine the odds of imaging examinations having an SUD-related indication as a function of demographic and imaging characteristics.ResultsAmong 938,245 examinations, 0.17% had an SUD-related indication. Patients with SUD-related indications were younger (mean 37.2 ± 11.1 versus 53.5 ± 22.4, P < .001) and more commonly male (65% versus 52%, P < .001). The proportions of MR (17%), spine (17%), and extremities (33%) studies performed for SUD-related indications were larger among SUD than non-SUD indications (6%, 8%, 26%, respectively, all P < .001). Regression analysis demonstrated the odds of acquiring an ED imaging examination with an SUD-related indication significantly increased over time (P < .001, adjusted odds ratio [aOR] = 1.06), which was most pronounced among MR (P < .001, aOR = 1.23). For all regression models, younger age, male gender, and body part being imaged were identified as independent predictors of an SUD-related indication for ED imaging.ConclusionImaging performed for an SUD-related indication represented a small but increasing subset of overall ED imaging. Utilization of MR for SUD-related indications significantly outpaced growth of MR without SUD-related indications.     

Navigating Uncertainty in the Management of Incidental Findings

The lack of prospective outcomes studies for many types of incidental findings limits our understanding of both their natural history and the potential efficacy of treatment. To support decision making for the management of incidental findings, major sources of uncertainty in management pathways can be mapped and analyzed using mathematical models. This process yields important insights into how uncertainty influences the best treatment decision. Here, we consider a classification scheme, grounded in decision science, which exposes various levels and types of uncertainty in the management of incidental findings and addresses (1) disease-related risks, which are considered in context of a patient’s competing causes of mortality; (2) potential degrees of intervention; (3) strength of evidence; and (4) patients’ treatment-related preferences. Herein we describe how categorizing uncertainty by the sources, issues, and locus can build a framework from which to improve the management of incidental findings. Accurate and comprehensive handling of uncertainty will improve the quality of related decision making and will help guide future research priorities.     

The Current State of Teleradiology Across the United States: A National Survey of Radiologists’ Habits,Attitudes, and Perceptions on Teleradiology Practice

PurposeTo explore the current state of teleradiology practice, defined as the interpretation of imaging examinations at a different facility from where the examination was performed.MethodsA national survey addressing radiologists’ habits, attitudes, and perceptions regarding teleradiology was distributed by e-mail to a random sample of ACR members in early 2019.ResultsAmong 731 of 936 respondents who indicated a non-teleradiologist primary work setting, 85.6% reported performing teleradiology within the past 10 years and 25.4% reported that teleradiology represents a majority of their annual imaging volumes; 84.4% performed teleradiology for internal examinations and 45.7% for external examinations; 46.2% performed teleradiology for rural areas and 37.2% for critical access hospitals; 91.3% performed teleradiology during weekday normal business hours and 44.5% to 79.6% over evening, overnight, and weekend hours. In all, 76.9% to 86.2% perceived value from teleradiology for geographic, after-hours, and multispecialty coverage, as well as reduced interpretation turnaround times. The most common challenges for teleradiology were electronic health record access (62.8%), quality assurance (53.8%), and technologist proximity (48.4%). The strategy most commonly considered useful for improving teleradiology was technical interpretation standards (33.3%). Radiologists in smaller practices were less likely to perform teleradiology or performed teleradiology for lower fractions of work, were less likely to experience coverage advantages of teleradiology, and reported larger implementation challenges, particularly relating to electronic health records and prior examination access.ConclusionDespite historic concerns, teleradiology is widespread throughout modern radiology practice, helping practices achieve geographic, after-hours, and multispecialty coverage; reducing turnaround times; and expanding underserved access. Nonetheless, quality assurance of offsite examinations remains necessary. IT integration solutions could help smaller practices achieve teleradiology’s benefits.     

Strengths,Weaknesses, Opportunities,and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. There is growing evidence of the advantages of AI in radiology creating seamless imaging workflows for radiologists or even replacing radiologists. Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.     

A Geospatial Analysis of Factors Affecting Access to CT Facilities: Implications for Lung Cancer Screening

ObjectiveThe association between access to CT facilities for lung cancer screening and population characteristics is understudied. We aimed to determine the relationship between census tract–level socioeconomic characteristics (SEC) and driving distance to an ACR-accredited CT facility.MethodsCensus tract–level SEC were determined from the US Census Bureau. Distance to nearest ACR-accredited CT facility was derived at the census tract level. Census tract–level multivariable regression modeling was used to determine the relationship between driving distance to a CT facility and census tract SEC, including population density (a marker of rural versus urban), gender, race, insurance status or type, and education level.ResultsIn an adjusted multivariable model, census tract–level population density was the greatest relative determinant of distance to a CT facility. Namely, rural census tracts had relatively longer distances to CT facilities than urban census tracts (P < .001). Census tracts with higher uninsured, Medicaid, undereducated (less <high school degree) populations had relatively greater distances to CT facilities (p<0.001), whereas those with higher non-White, female, and Medicare populations had shorter distances (p<0.001).DiscussionRural populations have relatively less geographic access to CT facilities. Furthermore, other vulnerable populations, such as the uninsured, those on Medicaid, and the undereducated, may also have relatively less access to CT imaging facilities. These variations in access to CT may affect the uptake and utilization of lung cancer screening.     

Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging for Differentiation Between Parotid Neoplasms

PurposeThis study was designed to evaluate the role of multiparametric magnetic resonance imaging (MRI) for differentiation of parotid gland neoplasms.MethodsProspective study was conducted upon 52 consecutive patients (30 men, 22 women; aged 24–78 years; mean, 51 years) with parotid tumours that underwent multiparametric MRI using combined static MRI, dynamic contrast enhanced (DCE) MRI, and diffusion-weighted imaging (DWI). The static MRI parameter, time signal intensity curves (TIC) derived from DCE-MRI, and apparent diffusion coefficient (ADC) values of parotid tumours were correlated with histopathological findings.ResultsStatic MRI revealed a significant difference between both benign and malignant lesions in regards to margin definition (P < .001) and T2 hypointensity (P < .013), with a diagnostic accuracy 95% and 78.33% respectively. Study of the TIC type on DCE-MRI revealed statistically significant difference between benign and malignant lesions (P < .001) and diagnostic accuracy 96.55%. There was no statistically significant difference (P = .181) between the ADC values of benign and malignant lesions. ROC curve analysis revealed that by using ADC cut-off value of 1 × 10^?3 mm²/s had accuracy of 84.62% respectively for differentiating Warthin from malignant tumours that could be modified to higher value (94.28%) by excluding lymphoma from malignant lesions. By using cutoff value of 1.3 × 10^?3 mm²/s to differentiate pleomorphic adenoma from malignancy, ROC curve analysis had high accuracy of 97.06%.ConclusionMultiparametric MRI can be used for differentiation of malignant from benign parotid tumours and characterization of some benign parotid tumours.     

Aortic Bulge: A Possible Predictive Sign of Impending Aortoenteric Fistula

PurposeThe purpose of this study is to introduce the aortic bulge sign, a finding observed retrospectively on computed tomography prior to the acute presentation of aortoenteric fistula, and to determine its interobserver reliability.MethodsFollowing research ethics board approval, all cases of aortoenteric fistula at our institution occurring from 2011–2015 were identified retrospectively. All previous computed tomography images of patients who eventually developed aortoenteric fistula were reviewed by a single observer for the presence of a potentially predictive finding of fistulization, the aortic bulge sign. These previous images were then combined with age and sex matched controls into a case bank. Eight radiology residents and staff were instructed in observing the aortic bulge sign. These observers then reviewed the case bank in a blinded analysis to determine the interobserver reliability of this finding.ResultsFourteen cases of aortoenteric were identified. The average patient age was 70.71 years with a male-to-female ratio of 11:3. Eleven patients had previous computed tomography images available for review. With blinded analysis by multiple observers, the aortic bulge sign was identified with greater than 80% agreement in six of 11 cases (66.67%). Fleiss' kappa was calculated at k = 0.60 (95% confidence interval 0.50–0.69), corresponding to moderate-to-substantial interobserver agreement.ConclusionsThe aortic bulge sign has been retrospectively identified as a promising computed tomography finding of eventual aortoenteric fistula prior to acute presentation. Further study is required to determine the diagnostic value of this sign.     

Simple Analysis of the Computed Tomography Features of Gastric Schwannoma

ObjectiveThe objective of this study was to assess the computed tomography (CT) findings of gastric schwannoma (GS) and identify the difference between large (> 5 cm) and small (≤ 5 cm) GS.Materials and MethodsCT findings of 38 pathologically proven cases of GSs were retrospectively reviewed. The CT evaluation of GS included categorical variables (location, contour, growth pattern, enhancement pattern, necrosis, ulceration, calcification, and lymph nodes) and continuous variables (size, CT value of 3 phases, and enhancement degree). The lesion was divided into 2 groups (large [> 5 cm] and small [≤ 5 cm] GS) according to the tumor size. The Fisher exact test was used for categorical variables and the Student t or Mann-Whitney U test for continuous variables.ResultsOf the 38 patients, there were 32 women and 6 men. The median age was 54.5 years (range 39–79). Most of patients (65.8%, [25 of 38]) had nonspecific gastrointestinal symptoms such as abdominal or gastric pain, fullness and discomfort, bleeding, and melena. The tumors were mainly located in the stomach body (71.1% [27 of 38]), and the mean diameter was 3.7 cm (range 1.5 cm-10.3 cm), of which included large (> 5 cm) (n = 8) and small (≤ 5 cm) (n = 30). All of the GSs were benign, 9 of whom had palpable perigastric lymph nodes, which confirmed by pathology for the reactive inflammatory hyperplasia. Growth pattern, pattern of enhancement, necrosis, calcification, surface ulceration, and lymph node in the CT images were found to be significant variables for differentiating large (> 5 cm) and small (≤ 5 cm) GS (P < .05).ConclusionGSs were predominantly located at the gastric body and occurred most frequently in women between the ages of 40–70 years, and showed gradual enhancement after contrast enhancement. Palpable perigastric lymph nodes could not be considered as malignant factor of GS. There 7 computed CT criteria are significant difference between large (> 5 cm) and small (≤ 5 cm) GS.     

Imaging-Based 3-Dimensional Printing for Improved Maxillofacial Presurgical Planning: A Single Center Case Series

Purpose3-D printing is an increasingly widespread technology that allows physical models to be constructed based on cross-sectional medical imaging data. We sought to develop a pipeline for production of 3-dimensional (3-D) models for presurgical planning and assess the value of these models for surgeons and patients.MethodsIn this institutional review board–approved, single-center case series, participating surgeons identified cases for 3-D model printing, and after obtaining patient consent, a 3-D model was produced for each of the 7 participating patients based on preoperative cross-sectional imaging. Each model was given to the surgeon to use during the surgical consent discussion and preoperative planning. Patients and surgeons completed questionnaires evaluating the quality and usefulness of the models.ResultsThe 3-D models improved surgeon confidence in their operative approach, influencing the choice of operative approach in the majority of cases. Patients and surgeons reported that the model improved patient comprehension of the surgery during the consent discussion, including risks and benefits of the surgery. Model production time was as little as 4 days, and the average per-model cost was $350.Conclusions3-D printed models are useful presurgical tools from both surgeon and patient perspectives. Development of local hospital-based 3-D printing capabilities enables model production with rapid turnaround and modest cost, representing a value-added service for radiologists to offer their surgical colleagues.