The Society of Cardiovascular Computed Tomography (SCCT) and the Society of Thoracic Radiology (STR) have jointly produced this document. Experts in this subject have been selected from both organizations to examine subject-specific data and write this guideline in partnership. A formal literature review, weighing the strength of evidence has been performed. When available, information from studies on cost was considered. Computed tomography (CT) acquisition, CAC scoring methodologies and clinical outcomes are the primary basis for the recommendations in this guideline. This guideline is intended to assist healthcare providers in clinical decision making. The recommendations reflect a consensus after a thorough review of the best available current scientific evidence and practice patterns of experts in the field and are intended to improve patient care while acknowledging that situations arise where additional information may be needed to better inform patient care.

The Society of Cardiovascular Computed Tomography (SCCT) and the Society of Thoracic Radiology (STR) have jointly produced this document. Experts in this subject have been selected from both organizations to examine subject-specific data and write this guideline in partnership. A formal literature review, weighing the strength of evidence has been performed. When available, information from studies on cost was considered. Computed tomography (CT) acquisition, CAC scoring methodologies and clinical outcomes are the primary basis for the recommendations in this guideline. This guideline is intended to assist healthcare providers in clinical decision making. The recommendations reflect a consensus after a thorough review of the best available current scientific evidence and practice patterns of experts in the field and are intended to improve patient care while acknowledging that situations arise where additional information may be needed to better inform patient care.

In the January/February 2017 issue of Journal of Cardiovascular Computed Tomography, in the Guidelines article titled ‘‘2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans a report of the society of Cardiovascular Computed Tomography and Society of Thoracic Radiology’’ (2017; 11:74–84; http://dx.doi.org/10.1016/j.jcct.2016.11.003), Appendix 1 containing the disclosures was not included. The appendix has been added to the online version of the article.

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.

PURPOSE: Lung cancer causes the largest number of cancer-related deaths in the United States. Lung cancer incidence rates, mortality rates, and rates of advanced stage disease are higher among those who live in rural areas. Known disparities in lung cancer outcomes between rural and nonrural populations may be in part because of barriers faced by rural populations. The authors tested the hypothesis that among Veterans who receive initial lung cancer screening, rural Veterans would be less likely to complete annual repeat screening than nonrural Veterans. METHODS: A retrospective cohort study was conducted of 10 Veterans Affairs medical centers from 2015 to 2019. Rural and nonrural Veterans undergoing lung cancer screening were identified. Rural status was defined using the rural-urban commuting area codes. The primary outcome was annual repeat lung cancer screening in the 9- to 15-month window (primary analysis) and 31-day to 18-month window (sensitivity analysis) after the first documented lung cancer screening. To examine rurality as a predictor of annual repeat lung cancer screening, multivariable logistic regression models were used. RESULTS: In the final analytic sample of 11,402 Veterans, annual repeat lung cancer screening occurred in 27.7% of rural Veterans (641 of 2,316) and 31.8% of nonrural Veterans (2,891 of 9,086) (adjusted odds ratio: 0.86; 95% confidence interval: 0.73-1.03). Similar results were seen in the sensitivity analysis, with 41.6% of rural Veterans (963 of 2,316) versus 45.2% of nonrural Veterans (4,110 of 9,086) (adjusted odds ratio: 0.88; 95% confidence interval: 0.73-1.04) having annual repeat screening in the expanded 31-day to 18-month window. CONCLUSIONS: Among a national cohort of Veterans, rural residence was associated with numerically lower odds of annual repeat lung cancer screening than nonrural residence. Continued, intentional outreach efforts to increase annual repeat lung cancer screening among rural Veterans may offer an opportunity to decrease deaths from lung cancer.

INTRODUCTION: To assess healthcare professionals perceptions of rural barriers and facilitators of lung cancer screening program implementation in a Veterans Health Administration (VHA) setting through a series of one-on-one interviews with healthcare team members. METHODS: Based on measures developed using Reach Effectiveness Adoption Implementation Maintenance (RE-AIM), we conducted a cross-sectional qualitative study consisting of one-on-one semi-structured telephone interviews with VHA healthcare team members at 10 Veterans Affairs medical centers (VAMCs) between December 2020 and September 2021. An iterative inductive and deductive approach was used for qualitative analysis of interview data, resulting in the development of a conceptual model to depict rural barriers and facilitators of lung cancer screening program implementation. RESULTS: A total of 30 interviews were completed among staff, providers, and lung cancer screening program directors and a conceptual model of rural barriers and facilitators of lung cancer screening program implementation was developed. Major themes were categorized within institutional and patient environments. Within the institutional environment, participants identified systems-level (patient communication, resource availability, workload), provider-level (attitudes and beliefs, knowledge, skills and capabilities), and external (regional and national networks, incentives) barriers to and facilitators of lung cancer screening program implementation. Within the patient environment, participants revealed patient-level (modifiable vulnerabilities) barriers and facilitators as well as ecological modifiers (community) that influence screening behavior. DISCUSSION: Understanding rural barriers to and facilitators of lung cancer screening program implementation as perceived by healthcare team members points to opportunities and approaches for improving lung cancer screening reach, implementation and effectiveness in VHA rural settings.

Introduction

Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).

Methods

We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.

Results

Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001).

Conclusion

AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

INTRODUCTION: Lung cancer screening is widely underutilized. Organizational factors, such as readiness for change and belief in the value of change (change valence), may contribute to underutilization. The aim of this study was to evaluate the association between healthcare organizations preparedness and lung cancer screening utilization. METHODS: Investigators cross-sectionally surveyed clinicians, staff, and leaders at10 Veterans Affairs from November 2018 to February 2021 to assess organizational readiness to implement change. In 2022, investigators used simple and multivariable linear regression to evaluate the associations between facility-level organizational readiness to implement change and change valence with lung cancer screening utilization. Organizational readiness to implement change and change valence were calculated from individual surveys. The primary outcome was the proportion of eligible Veterans screened using low-dose computed tomography. Secondary analyses assessed scores by healthcare role. RESULTS: The overall response rate was 27.4% (n=1,049), with 956 complete surveys analyzed: median age of 49 years, 70.3% female, 67.6% White, 34.6% clinicians, 61.1% staff, and 4.3% leaders. For each 1-point increase in median organizational readiness to implement change and change valence, there was an associated 8.4-percentage point (95% CI=0.2, 16.6) and a 6.3-percentage point increase in utilization (95% CI= -3.9, 16.5), respectively. Higher clinician and staff median scores were associated with increased utilization, whereas leader scores were associated with decreased utilization after adjusting for other roles. CONCLUSIONS: Healthcare organizations with higher readiness and change valence utilized more lung cancer screening. These results are hypothesis generating. Future interventions to increase organizations preparedness, especially among clinicians and staff, may increase lung cancer screening utilization.

Rationale and objectives

We previously reported a novel manual method for measuring bone mineral density (BMD) in coronary artery calcium (CAC) scans and validated our method against Dual X-Ray Absorptiometry (DEXA). Furthermore, we have developed and validated an artificial intelligence (AI) based automated BMD (AutoBMD) measurement as an opportunistic add-on to CAC scans that recently received FDA approval. In this report, we present evidence of equivalency between AutoBMD measurements in cardiac vs lung CT scans.

Materials and methods

AI models were trained using 132 cases with 7649 (3 mm) slices for CAC, and 37 cases with 21918 (0.5 mm) slices for lung scans. To validate AutoBMD against manual measurements, we used 6776 cases of BMD measured manually on CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA). We then used 165 additional cases from Harbor UCLA Lundquist Institute to compare AutoBMD in patients who underwent both cardiac and lung scans on the same day.

Results

Mean±SD for age was 69 ± 9.4 years with 52.4% male. AutoBMD in lung and cardiac scans, and manual BMD in cardiac scans were 153.7 ± 43.9, 155.1 ± 44.4, and 163.6 ± 45.3 g/cm³, respectively (p = 0.09). Bland-Altman agreement analysis between AutoBMD lung and cardiac scans resulted in 1.37 g/cm³ mean differences. Pearson correlation coefficient between lung and cardiac AutoBMD was R² = 0.95 (p < 0.0001).

Conclusion

Opportunistic BMD measurement using AutoBMD in CAC and lung cancer screening scans is promising and yields similar results. No extra radiation plus the high prevalence of asymptomatic osteoporosis makes AutoBMD an ideal screening tool for osteopenia and osteoporosis in CT scans done for other reasons.

Mutations in the epidermal growth factor receptor gene (EGFR) represent one of the most frequent "actionable" alterations in non-small cell lung cancer (NSCLC). Typified by high response rates to targeted therapies, EGFR tyrosine kinase inhibitors (TKIs) are now established first-line treatment options and have transformed the treatment paradigm for NSCLC. With the recent breakthrough designation and approval of the third-generation EGFR TKI osimertinib, available systemic and local treatment options have expanded, requiring new clinical algorithms that take into account individual patient molecular and clinical profiles. In this International Association for the Study of Lung Cancer commissioned consensus statement, key pathologic, diagnostic, and therapeutic considerations, such as optimal choice of EGFR TKI and management of brain metastasis, are discussed. In addition, recommendations are made for clinical guidelines and research priorities, such as the role of repeat biopsies and use of circulating free DNA for molecular studies. With the rapid pace of progress in treating EGFR-mutant NSCLC, this statement provides a state-of-the-art review of the contemporary issues in managing this unique subgroup of patients.