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Background: Few eligible patients in the United States participate in lung cancer screening (LCS) with low-dose computed tomography (LDCT)., Objective: What is the efficacy of interventions to increase LCS participation?, Design: We performed a systematic review following a prespecified protocol registered in PROSPERO (CRD42021283984). In June/July of 2021, we searched Ovid MEDLINE, Embase, Cochrane, CENTRAL, ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, and Epistemonikos from 1946 to October 2021 to identify studies evaluating interventions to increase LCS participation., Participants: Thirteen of 2761 studies met inclusion criteria for data extraction. Of these, six had results available (five RCTs and one prospective observational study). The studies had predominantly White and non-Hispanic participants., Main Measures: An intention-to-treat analysis was used to calculate each study's relative risk (RR) to increase LCS. Effect sizes were pooled using a random-effects model with a subgroup analysis for multi- versus single-step interventions. Risk of bias was evaluated with the revised Cochrane risk-of-bias tool (RoB 2) and risk of bias in non-randomized studies of interventions (ROBINS-I)., Key Results: Overall, the proportion of screening LDCTs performed did not improve in the intervention group relative to the comparator group (RR [95% CI] of 1.30 [0.74, 2.29]), and meta-analysis indicated high heterogeneity of studies (I 2  = 91%). Subgroup analysis suggests that interventions targeting multiple barriers may increase LCS participation (RR [95% CI] for multistep vs single-step; 2.68 [1.77, 4.05] vs 0.99 [0.89, 1.10], P < 0.01). Quality assessment revealed that three of five RCTs showed some concerns or high risk of bias., Conclusion: Evidence on efficacy of interventions to increase LCS participation is limited due to a small number of prospective studies performed in non-diverse populations with critical risk of bias. Further, overall, studied interventions did not improve lung cancer screening participation, though interventions targeting multiple barriers may have some benefit., Competing Interests: Declarations: Conflict of Interest: The authors declare that they do not have a conflict of interest., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Introduction: Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. LungCT ScreeningReporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement. Unfortunately, due to variability in radiology reports, extraction of Lung-RADS scores is non-trivial, and best practices do not exist. The purpose of this project is to compare mechanisms to extract Lung-RADS scores from free-text radiology reports., Methods: We retrospectively analyzed reports of LCS low-dose computed tomography (LDCT) examinations performed at a multihospital integrated healthcare network in New York State between January 2016 and July 2023. We compared three methods of Lung-RADS score extraction: manual physician entry at time of report creation, manual LCS specialist entry after report creation, and an internally developed, rule-based natural language processing (NLP) algorithm. Accuracy, recall, precision, and completeness (i.e., the proportion of LCS exams to which a Lung-RADS score has been assigned) were compared between the three methods., Results: The dataset includes 24,060 LCS examinations on 14,243 unique patients. The mean patient age was 65 years, and most patients were male (54 %) and white (75 %). Completeness rate was 65 %, 68 %, and 99 % for radiologists' manual entry, LCS specialists' entry, and NLP algorithm, respectively. Accuracy, recall, and precision were high across all extraction methods (>94 %), though the NLP-based approach was consistently higher than both manual entries in all metrics., Discussion: An NLP-based method of LCS score determination is an efficient and more accurate means of extracting Lung-RADS scores than manual review and data entry. NLP-based methods should be considered best practice for extracting structured Lung-RADS scores from free-text radiology reports., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors have no conflicts of interest related to the submission of this manuscript. Dr. Bade is a site PI on industry-sponsored trials for Nucleix, Delfi Diagnostics, and Biodesix., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Novel strategies are needed to improve low rates of lung cancer screening (LCS) in the US. Seeking to determine hospitalists' perspectives on leveraging hospitalizations to identify patients eligible for LCS, we performed qualitative interviews with eight hospitalists from two hospitals within a large integrated healthcare system. The interviews used semi-structured questions to assess (1) knowledge and practice of general screening and LCS guidelines from the United States Preventive Services Task Force (USPSTF), (2) identification of smoking history, and (3) hospitalists' views on how data obtained during hospitalization may be utilized to improve general screening and LCS post hospitalization. We ultimately reached the conclusion that hospitalists would support a dedicated program to identify hospitalized patients eligible for LCS and facilitate testing after discharge. Efforts to identify patients and arrange subsequent screening should be performed by team members outside the inpatient team., Competing Interests: Declaration of interests The authors have nothing to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: J. R. is a consultant for Siemens Healthineers. M. R.-J. receives clinical research support from Siemens Healthineers. T. F. is an employee for Siemens employee (Siemens Healthineers, Firchheim, Germany). None declared (J. M., E. A., S. R., J. C.)., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2023. Published by Oxford University Press.)

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., (Copyright © 2023. Published by Elsevier Inc.)

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., (Copyright © 2021 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

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 χ 2 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., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)