Thoracic Applications of Spectral CT Scan.

TOPIC IMPORTANCE: Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology. REVIEW FINDINGS: The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT. SUMMARY: DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology.

A predictive model for lung cancer screening nonadherence in a community setting health-care network.

BACKGROUND: Lung cancer screening (LCS) decreases lung cancer mortality. However, its benefit may be limited by nonadherence to screening. Although factors associated with LCS nonadherence have been identified, to the best of our knowledge, no predictive models have been developed to predict LCS nonadherence. The purpose of this study was to develop a predictive model leveraging a machine learning model to predict LCS nonadherence risk. METHODS: A retrospective cohort of patients who enrolled in our LCS program between 2015 and 2018 was used to develop a model to predict the risk of nonadherence to annual LCS after the baseline examination. Clinical and demographic data were used to fit logistic regression, random forest, and gradient-boosting models that were internally validated on the basis of accuracy and area under the receiver operating curve. RESULTS: A total of 1875 individuals with baseline LCS were included in the analysis, with 1264 (67.4%) as nonadherent. Nonadherence was defined on the basis of baseline chest computed tomography (CT) findings. Clinical and demographic predictors were used on the basis of availability and statistical significance. The gradient-boosting model had the highest area under the receiver operating curve (0.89, 95% confidence interval = 0.87 to 0.90), with a mean accuracy of 0.82. Referral specialty, insurance type, and baseline Lung CT Screening Reporting & Data System (LungRADS) score were the best predictors of nonadherence to LCS. CONCLUSIONS: We developed a machine learning model using readily available clinical and demographic data to predict LCS nonadherence with high accuracy and discrimination. After further prospective validation, this model can be used to identify patients for interventions to improve LCS adherence and decrease lung cancer burden.

Hypertrophic cardiomyopathy - phenotypic variations beyond wall thickness.

Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy (LVH) in the absence of another causal disease. Several morphologic and histologic changes have been described. Given the morbidity and mortality associated with HCM, understanding these anatomic variations is key to interpreting imaging. This is especially important since many patients exhibit these associated findings in the absence of LVH and prompt early detection of these variations may lead to early diagnosis and treatment. This article describes the appearance of morphologic variations seen in HCM beyond myocardial thickening including: papillary muscle and mitral valve variants, myocardial crypts, left ventricular myocardial bands, and dystrophic calcification related to increased wall tension.

Factors Associated With Lung Cancer Screening Adherence Among Patients With Negative Baseline CT Results in a Community Health Care Setting.

PURPOSE: Lung cancer screening (LCS) decreases lung cancer mortality; however, that reduction depends upon screening adherence. The purpose of this study was to determine factors associated with adherence rate for LCS among patients with negative baseline CT results in a multi-integrated health care network. METHODS: A retrospective analysis was conducted among patients with negative baseline CT results in a multi-integrated health care network LCS program between January 2015 and January 2020. The two outcomes were adherence for the first and second subsequent LCS studies. Negative baseline result was defined as a Lung CT Screening Reporting and Data System score 0, 1, or 2. Adherence was defined as undergoing a follow-up study within 11 to 15 months of a prior scan. Multivariable logistic regression was used to determine significant predictors of adherence, adjusting for patient demographics, median household income (on the basis of geocoding ZIP codes from the US Census Bureau), smoking history, screening sites, and provider specialty. RESULTS: A total of 30.7% (512 of 1,668) and 16.3% (270 of 1,660) of patients were adherent for the first two annual subsequent screens, respectively. First-year adherence was higher among former smokers and varied by site and provider specialty. Second-year adherence was higher among former smokers and varied by site, provider specialty, and pack-years smoked. CONCLUSIONS: Adherence to LCS in a multihospital integrated health care network was poor and even lower at year 2. The identified factors associated with adherence may serve as targets to increase LCS adherence and decrease lung cancer mortality.

Recovery of outpatient imaging utilization during the first wave of the COVID-19 pandemic.

OBJECTIVE: During the COVID-19 pandemic, Radiology practices experienced marked reductions in outpatient imaging volumes. Our purpose was to evaluate the timing, rate, and degree of recovery of outpatient imaging during the first wave of the pandemic. We also sought to ascertain the relationship of outpatient imaging recovery to the incidence of COVID-19 cases. METHODS: Retrospective study of outpatient imaging volumes in a large healthcare system was performed from January 1, 2019-August 25, 2020. Dataset was split to compare Pre-COVID (weeks 1-9), Peak-COVID (weeks 10-15) and Recovery-COVID (weeks 16-34) periods. Chi-square and Independent-samples t-tests compared weekly outpatient imaging volumes in 2020 and 2019. Regression analyses assessed the rate of decline and recovery in Peak-COVID and Recovery-COVID periods, respectively. RESULTS: Total outpatient imaging volume in 2020 (weeks 1-34) was 327,738 exams, compared to 440,314 in 2019. The 2020 mean weekly imaging volumes were significantly decreased in Peak-COVID (p = 0.0148) and Recovery-COVID (p = 0.0003) periods. Mean weekly decline rate was -2580 exams/week and recovery rate was +617 exams/week. The 2020 Post-COVID (weeks 10-34) period had an average decrease of 36.5% (4813.4/13,178.6) imaging exams/week and total estimated decrease of 120,335 exams. Significant inverse correlation (-0.8338, p < 0.0001) was seen between positive-tested COVID-19 cases and imaging utilization with 1-week lag during Post-COVID (weeks 10-34) period. CONCLUSION: Recovery of outpatient imaging volume during the first wave of COVID-19 pandemic showed a gradual return to pre-pandemic levels over the course of 3-4 months. The rate of imaging utilization was inversely associated with new positive-tested COVID-19 cases with a 1-week lag.

Imaging Utilization During the COVID-19 Pandemic Highlights Socioeconomic Health Disparities.

OBJECTIVE: The devastating impact from the coronavirus disease 2019 (COVID-19) pandemic highlights long-standing socioeconomic health disparities in the United States. The purpose of this study was to evaluate socioeconomic factors related to imaging utilization during the pandemic. METHODS: Retrospective review of consecutive imaging examinations was performed from January 1, 2019, to May 31, 2020, across all service locations (inpatient, emergency, outpatient). Patient level data were provided for socioeconomic factors (age, sex, race, insurance status, residential zip code). Residential zip code was used to assign median income level. The weekly total imaging volumes in 2020 and 2019 were plotted from January 1 to May 31 stratified by socioeconomic factors to demonstrate the trends during the pre-COVID-19 (January 1 to February 28) and post-COVID-19 (March 1 to May 31) periods. Independent-samples t tests were used to statistically compare the 2020 and 2019 socioeconomic groups. RESULTS: Compared with 2019, the 2020 total imaging volume in the post-COVID-19 period revealed statistically significant increased imaging utilization in patients who are aged 60 to 79 years (P = .0025), are male (P < .0001), are non-White (Black, Asian, other, unknown; P < .05), are covered by Medicaid or uninsured (P < .05), and have income below $80,000 (P < .05). However, there was a significant decrease in imaging utilization among patients who are younger (<18 years old; P < .0001), are female (P < .0001), are White (P = .0003), are commercially insured (P < .0001), and have income ≥$80,000 (P < .05). DISCUSSION: During the pandemic, there was a significant change in imaging utilization varying by socioeconomic factors, consistent with the known health disparities observed in the prevalence of COVID-19. These findings could have significant implications in directing utilization of resources during the pandemic and subsequent recovery.

Coronavirus Disease 2019 (COVID-19) Pandemic Shifts Inpatient Imaging Utilization.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on imaging utilization across practice settings. The purpose of this study was to quantify the change in the composition of inpatient imaging volumes for modality types and Current Procedural Terminology-coded groups during the COVID-19 pandemic. METHODS: A retrospective study of inpatient imaging volumes in a large health care system was performed, analyzing weekly imaging volumes by modality types (radiography, CT, MRI, ultrasound, interventional radiology, nuclear medicine) in years 2020 and 2019. The data set was split to compare pre-COVID-19 (weeks 1-9) and post-COVID-19 (weeks 10-16) periods. Further subanalyses compared early post-COVID-19 (weeks 10-13) and late post-COVID-19 (weeks 14-16) periods. Statistical comparisons were performed using χ2 and independent-samples t tests. RESULTS: Compared with 2019, total inpatient imaging volume in 2020 post-COVID-19, early and late post-COVID-19 periods, declined by 13.6% (from 78,902 to 68,168), 16.6% (from 45,221 to 37,732), and 9.6% (from 33,681 to 30,436), respectively. By week 16, inpatient imaging volume rebounded and was only down 4.2% (from 11,003 to 10,546). However, a statistically significant shift (P < .0001) in the 2020 composition mix was observed largely comprised of radiography (74.3%), followed by CT (12.7%), ultrasound (8%), MRI (2.4%), interventional radiology (2.3%), and nuclear medicine (0.4%). Although the vast majority of imaging studies declined, few Current Procedural Terminology-coded groups showed increased trends in imaging volumes in the late post-COVID-19 period, including CT angiography chest, radiography chest, and ultrasound venous duplex. DISCUSSION: During the COVID-19 pandemic, we observed a decrease in inpatient imaging volumes accompanied by a shift away from cross-sectional imaging toward radiography. These findings could have significant implications in planning for a potential resurgence.

Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Imaging Case Volumes.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has had significant economic impact on radiology with markedly decreased imaging case volumes. The purpose of this study was to quantify the imaging volumes during the COVID-19 pandemic across patient service locations and imaging modality types. METHODS: Imaging case volumes in a large health care system were retrospectively studied, analyzing weekly imaging volumes by patient service locations (emergency department, inpatient, outpatient) and modality types (x-ray, mammography, CT, MRI, ultrasound, interventional radiology, nuclear medicine) in years 2020 and 2019. The data set was split to compare pre-COVID-19 (weeks 1-9) and post-COVID-19 (weeks 10-16) periods. Independent-samples t tests compared the mean weekly volumes in 2020 and 2019. RESULTS: Total imaging volume in 2020 (weeks 1-16) declined by 12.29% (from 522,645 to 458,438) compared with 2019. Post-COVID-19 (weeks 10-16) revealed a greater decrease (28.10%) in imaging volumes across all patient service locations (range 13.60%-56.59%) and modality types (range 14.22%-58.42%). Total mean weekly volume in 2020 post-COVID-19 (24,383 [95% confidence interval 19,478-29,288]) was statistically reduced (P = .003) compared with 33,913 [95% confidence interval 33,429-34,396] in 2019 across all patient service locations and modality types. The greatest decline in 2020 was seen at week 16 specifically for outpatient imaging (88%) affecting all modality types: mammography (94%), nuclear medicine (85%), MRI (74%), ultrasound (64%), interventional (56%), CT (46%), and x-ray (22%). DISCUSSION: Because the duration of the COVID-19 pandemic remains uncertain, these results may assist in guiding short- and long-term practice decisions based on the magnitude of imaging volume decline across different patient service locations and specific imaging modality types.

Lack of ventral striatal response to positive stimuli in depressed versus normal subjects.

OBJECTIVE: Most of the functional neuroimaging studies of depression have focused primarily on the resting state or responses to negatively valenced stimuli. However, depression consists not only of an accentuation of negative affective processing but of an inability to experience pleasure or positive motivation. The authors tested the hypothesis that depressed subjects would show less activation than healthy comparison subjects, in response to positive stimuli, in ventral striatal regions associated with processing of reward and positive stimuli. METHOD: Positive, negative, and neutral words were presented to 10 unmedicated depressed patients and 12 healthy comparison subjects in the context of a 3T functional magnetic resonance imaging (MRI) paradigm. Image processing and analysis were performed using statistical parametric mapping with a mixed-effects model. Significant differences in neural responses were assessed, examining group, condition, and interaction effects of interest within the context of a general linear model. RESULTS: Relative to comparison subjects, depressed patients demonstrated significantly less bilateral ventral striatal activation to positive stimuli, correlating with decreased interest/pleasure in and performance of activities. They also displayed decreased activation to positive stimuli in a dorsomedial frontal region associated with processing of self-related stimuli. Responses of depressed subjects to negative stimuli were consistent with the growing literature on frontolimbic dysfunction in depression. CONCLUSIONS: This finding 1) supports a pathophysiological model of depression that includes reward/motivational pathway dysfunction, 2) suggests a contributing neural substrate of the inability to experience pleasure or engage in rewarding activities, 3) provides greater specification of abnormalities of basal ganglia function in depression, and 4) may help guide treatment approaches.