Characteristics of Persons Screened for Lung Cancer in the United States : A Cohort Study.

BACKGROUND: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was recommended by the U.S. Preventive Services Task Force (USPSTF) in 2013, making approximately 8 million Americans eligible for screening. The demographic characteristics and adherence of persons screened in the United States have not been reported at the population level. OBJECTIVE: To define sociodemographic characteristics and adherence among persons screened and entered into the American College of Radiology's Lung Cancer Screening Registry (LCSR). DESIGN: Cohort study. SETTING: United States, 2015 to 2019. PARTICIPANTS: Persons receiving a baseline LDCT for LCS from 3625 facilities reporting to the LCSR. MEASUREMENTS: Age, sex, and smoking status distributions (percentages) were computed among persons who were screened and among respondents in the 2015 National Health Interview Survey (NHIS) who were eligible for screening. The prevalence between the LCSR and the NHIS was compared with prevalence ratios (PRs) and 95% CIs. Adherence to annual screening was defined as having a follow-up test within 11 to 15 months of an initial LDCT. RESULTS: Among 1 159 092 persons who were screened, 90.8% (n = 1 052 591) met the USPSTF eligibility criteria. Compared with adults from the NHIS who met the criteria (n = 1257), screening recipients in the LCSR were older (34.7% vs. 44.8% were aged 65 to 74 years; PR, 1.29 [95% CI, 1.20 to 1.39]), more likely to be female (41.8% vs. 48.1%; PR, 1.15 [CI, 1.08 to 1.23]), and more likely to currently smoke (52.3% vs. 61.4%; PR, 1.17 [CI, 1.11 to 1.23]). Only 22.3% had a repeated annual LDCT. If follow-up was extended to 24 months and more than 24 months, 34.3% and 40.3% were adherent, respectively. LIMITATIONS: Underreporting of LCS and missing data may skew demographic characteristics of persons reported to be screened. Underreporting of adherence may result in underestimates of follow-up. CONCLUSION: Approximately 91% of persons who had LCS met USPSTF eligibility criteria. In addition to continuing to target all eligible adults, men, those who formerly smoked, and younger eligible patients may be less likely to be screened. Adherence to annual follow-up screening was poor, potentially limiting screening effectiveness. PRIMARY FUNDING SOURCE: None.

Evaluating the Patient With a Pulmonary Nodule: A Review.

IMPORTANCE: Pulmonary nodules are identified in approximately 1.6 million patients per year in the US and are detected on approximately 30% of computed tomographic (CT) images of the chest. Optimal treatment of an individual with a pulmonary nodule can lead to early detection of cancer while minimizing testing for a benign nodule. OBSERVATIONS: At least 95% of all pulmonary nodules identified are benign, most often granulomas or intrapulmonary lymph nodes. Smaller nodules are more likely to be benign. Pulmonary nodules are categorized as small solid (<8 mm), larger solid (≥8 mm), and subsolid. Subsolid nodules are divided into ground-glass nodules (no solid component) and part-solid (both ground-glass and solid components). The probability of malignancy is less than 1% for all nodules smaller than 6 mm and 1% to 2% for nodules 6 mm to 8 mm. Nodules that are 6 mm to 8 mm can be followed with a repeat chest CT in 6 to 12 months, depending on the presence of patient risk factors and imaging characteristics associated with lung malignancy, clinical judgment about the probability of malignancy, and patient preferences. The treatment of an individual with a solid pulmonary nodule 8 mm or larger is based on the estimated probability of malignancy; the presence of patient comorbidities, such as chronic obstructive pulmonary disease and coronary artery disease; and patient preferences. Management options include surveillance imaging, defined as monitoring for nodule growth with chest CT imaging, positron emission tomography-CT imaging, nonsurgical biopsy with bronchoscopy or transthoracic needle biopsy, and surgical resection. Part-solid pulmonary nodules are managed according to the size of the solid component. Larger solid components are associated with a higher risk of malignancy. Ground-glass pulmonary nodules have a probability of malignancy of 10% to 50% when they persist beyond 3 months and are larger than 10 mm in diameter. A malignant nodule that is entirely ground glass in appearance is typically slow growing. Current bronchoscopy and transthoracic needle biopsy methods yield a sensitivity of 70% to 90% for a diagnosis of lung cancer. CONCLUSIONS AND RELEVANCE: Pulmonary nodules are identified in approximately 1.6 million people per year in the US and approximately 30% of chest CT images. The treatment of an individual with a pulmonary nodule should be guided by the probability that the nodule is malignant, safety of testing, the likelihood that additional testing will be informative, and patient preferences.

The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society.

With more than 900 000 confirmed cases worldwide and nearly 50 000 deaths during the first 3 months of 2020, the coronavirus disease 2019 (COVID-19) pandemic has emerged as an unprecedented health care crisis. The spread of COVID-19 has been heterogeneous, resulting in some regions having sporadic transmission and relatively few hospitalized patients with COVID-19 and others having community transmission that has led to overwhelming numbers of severe cases. For these regions, health care delivery has been disrupted and compromised by critical resource constraints in diagnostic testing, hospital beds, ventilators, and health care workers who have fallen ill to the virus exacerbated by shortages of personal protective equipment. Although mild cases mimic common upper respiratory viral infections, respiratory dysfunction becomes the principal source of morbidity and mortality as the disease advances. Thoracic imaging with chest radiography and CT are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pretest probability, risk factors for disease progression, and critical resource constraints. To address this deficit, a multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing patients with COVID-19 across a spectrum of health care environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints. Fourteen key questions, corresponding to 11 decision points within the three scenarios and three additional clinical situations, were rated by the panel based on the anticipated value of the information that thoracic imaging would be expected to provide. The results were aggregated, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of chest radiography and CT in the management of COVID-19.

Incorporating Coexisting Chronic Illness into Decisions about Patient Selection for Lung Cancer Screening. An Official American Thoracic Society Research Statement.

BACKGROUND: Lung cancer screening (LCS) has the potential to reduce the risk of lung cancer death in healthy individuals, but the impact of coexisting chronic illnesses on LCS outcomes has not been well defined. Consideration of the complex relationship between baseline risk of lung cancer, treatment-related harms, and risk of death from competing causes is crucial in determining the balance of benefits and harms of LCS. OBJECTIVES: To summarize evidence, identify knowledge and research gaps, prioritize topics, and propose methods for future research on how best to incorporate comorbidities in making decisions regarding LCS. METHODS: A multidisciplinary group of international clinicians and researchers reviewed available data on the effects of comorbidities on LCS outcomes, focusing on the juxtaposition of lung cancer risk and competing risks of death, consideration of benefits and risks in patients with chronic obstructive pulmonary disease, communication of risk, and treatment of screen-detected lung cancer. RESULTS: This statement identifies gaps in knowledge regarding how comorbidities and competing causes of death impact outcomes in LCS, and we have developed questions to help guide future research efforts to better inform patient selection, education, and implementation of LCS. CONCLUSIONS: There is an urgent need for further research that can help guide clinical decision-making with patients who may not benefit from LCS owing to coexisting chronic illness. This statement establishes a research framework to address essential questions regarding how to incorporate and communicate risks of comorbidities into patient selection and decisions regarding LCS.

Electromagnetic Navigational Bronchoscopy versus CT-guided Percutaneous Sampling of Peripheral Indeterminate Pulmonary Nodules: A Cohort Study.

Purpose To compare the diagnostic yield and complication rates of electromagnetic navigational bronchoscopic (ENB)-guided and computed tomography (CT)-guided percutaneous tissue sampling of lung nodules. Materials and Methods Retrospectively identified were 149 patients sampled percutaneously with CT guidance and 146 patients who underwent ENB with transbronchial biopsy of a lung lesion between 2013 and 2015. Clinical data, incidence of complications, and nodule pathologic analyses were assessed through electronic medical record review. Lung nodule characteristics were reviewed through direct image analysis. Molecular marker studies and pathologic analyses from surgical excision were reviewed when available. Multiple-variable logistic regression models were built to compare the diagnostic yield and complication rates for each method and for different patient and disease characteristics. Results CT-guided sampling was more likely to be diagnostic than ENB-guided biopsy (86.0% [129 of 150] vs 66.0% [99 of 150], respectively), and this difference remained significant even after adjustments were made for patient and nodule characteristics (P < .001). Age, American Society of Anesthesiologists class, emphysema grade, nodule size, and distance from pleura were not significant predictors of increased diagnostic yield. Intraprocedural time for physicians was significantly lower with CT-guided sampling (P < .001). Similar yield for molecular analyses was noted with the two approaches (ENB-guided sampling, 88.9% [32 of 36]; CT-guided sampling, 82.0% [41 of 50]). The two groups had similar rates of major complications (symptomatic hemorrhage, P > .999; pneumothorax requiring chest tube and/or admission, P = .417). Conclusion CT-guided transthoracic biopsy provided higher diagnostic yield in the assessment of peripheral pulmonary nodules than navigational bronchoscopy with a similar rate of clinically relevant complications. © RSNA, 2017 Online supplemental material is available for this article.

Exercise capacity and cancer-specific quality of life following curative intent treatment of stage I-IIIA lung cancer.

PURPOSE: Lung cancer survivors are at risk for health impairments resulting from the effects and/or treatment of lung cancer and comorbidities. Practical exercise capacity (EC) assessments can help identify impairments that would otherwise remain undetected. In this study, we characterized and analyzed the association between functional EC and cancer-specific quality of life (QoL) in lung cancer survivors who previously completed curative intent treatment. METHODS: In a cross-sectional study of 62 lung cancer survivors who completed treatment ≥ 1 month previously, we assessed functional EC with the 6-min walk distance (6MWD) and cancer-specific QoL with the European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 (EORTC-QLQ-C30). Cancer-specific QoL was defined using a validated composite EORTC-QLQ-C30 summary score. Univariable (UVA) and multivariable linear regression analyses (MVA) were performed to assess the relationship between functional EC and cancer-specific QoL. RESULTS: Lung cancer survivors had reduced functional EC (mean 6MWD = 335 m, 65% predicted) and QoL (mean EORTC-QLQ-C30 summary score = 77, scale range 0-100). In UVA, 6MWD was significantly associated with cancer-specific QoL (R2 = 0.16, p = 0.001). In MVA, in a final model that also included heart failure, obstructive sleep apnea, and psychiatric illness, 6MWD was independently associated with cancer-specific QoL (partial R2 = 0.20, p = 0.001). CONCLUSIONS: Functional EC was independently associated with cancer-specific QoL in lung cancer patients postcurative intent treatment. Exercise-based interventions aimed at improving EC may improve cancer-specific QoL in these patients.

Evaluating Molecular Biomarkers for the Early Detection of Lung Cancer: When Is a Biomarker Ready for Clinical Use? An Official American Thoracic Society Policy Statement.

BACKGROUND: Molecular biomarkers have the potential to improve the current state of early lung cancer detection. The goal of this project was to develop a policy statement that provides guidance about the level of evidence required to determine that a molecular biomarker, used to support early lung cancer detection, is appropriate for clinical use. METHODS: An ad hoc project steering committee was formed, to include individuals with expertise in the early detection of lung cancer and molecular biomarker development, from inside and outside of the Assembly on Thoracic Oncology. Key questions, generated from the results of a survey of the project steering committee, were discussed at an in-person meeting. Results of the discussion were summarized in a policy statement that was circulated to the steering committee and revised multiple times to achieve consensus. RESULTS: With a focus on the clinical applications of lung cancer screening and lung nodule evaluation, the policy statement outlines categories of results that should be reported in the early phases of molecular biomarker development, discusses the level of evidence that would support study of the clinical utility, describes the outcomes that should be proven to consider a molecular biomarker clinically useful, and suggests study designs capable of assessing these outcomes. CONCLUSIONS: The application of molecular biomarkers to assist with the early detection of lung cancer has the potential to substantially improve our ability to select patients for lung cancer screening, and to assist with the characterization of indeterminate lung nodules. We have described relevant considerations and have suggested standards to apply when determining whether a molecular biomarker for the early detection of lung cancer is ready for clinical use.

Stakeholder Research Priorities for Smoking Cessation Interventions within Lung Cancer Screening Programs. An Official American Thoracic Society Research Statement.

RATIONALE: Smoking cessation counseling in conjunction with low-dose computed tomography (LDCT) lung cancer screening is recommended in multiple clinical practice guidelines. The best approach for integrating effective smoking cessation interventions within this setting is unknown. OBJECTIVES: To summarize evidence, identify research gaps, prioritize topics for future research, and propose standardized tools for use in conducting research on smoking cessation interventions within the LDCT lung cancer screening setting. METHODS: The American Thoracic Society convened a multistakeholder committee with expertise in tobacco dependence treatment and/or LDCT screening. During an in-person meeting, evidence was reviewed, research gaps were identified, and key questions were generated for each of three research domains: (1) target population to study; (2) adaptation, development, and testing of interventions; and (3) implementation of interventions with demonstrated efficacy. We also identified standardized measures for use in conducting this research. A larger stakeholder panel then ranked research questions by perceived importance in an online survey. Final prioritization was generated hierarchically on the basis of average rank assigned. RESULTS: There was little consensus on which questions within the population domain were of highest priority. Within the intervention domain, research to evaluate the effectiveness in the lung cancer screening setting of evidence-based smoking cessation interventions shown to be effective in other contexts was ranked highest. In the implementation domain, stakeholders prioritized understanding strategies to identify and overcome barriers to integrating smoking cessation in lung cancer screening settings. CONCLUSIONS: This statement offers an agenda to stimulate research surrounding the integration and implementation of smoking cessation interventions with LDCT lung cancer screening.

RNAseq analysis of bronchial epithelial cells to identify COPD-associated genes and SNPs.

BACKGROUND: There is a need for more powerful methods to identify low-effect SNPs that contribute to hereditary COPD pathogenesis. We hypothesized that SNPs contributing to COPD risk through cis-regulatory effects are enriched in genes comprised by bronchial epithelial cell (BEC) expression patterns associated with COPD. METHODS: To test this hypothesis, normal BEC specimens were obtained by bronchoscopy from 60 subjects: 30 subjects with COPD defined by spirometry (FEV1/FVC < 0.7, FEV1% < 80%), and 30 non-COPD controls. Targeted next generation sequencing was used to measure total and allele-specific expression of 35 genes in genome maintenance (GM) genes pathways linked to COPD pathogenesis, including seven TP53 and CEBP transcription factor family members. Shrinkage linear discriminant analysis (SLDA) was used to identify COPD-classification models. COPD GWAS were queried for putative cis-regulatory SNPs in the targeted genes. RESULTS: On a network basis, TP53 and CEBP transcription factor pathway gene pair network connections, including key DNA repair gene ERCC5, were significantly different in COPD subjects (e.g., Wilcoxon rank sum test for closeness, p-value = 5.0E-11). ERCC5 SNP rs4150275 association with chronic bronchitis was identified in a set of Lung Health Study (LHS) COPD GWAS SNPs restricted to those in putative regulatory regions within the targeted genes, and this association was validated in the COPDgene non-hispanic white (NHW) GWAS. ERCC5 SNP rs4150275 is linked (D' = 1) to ERCC5 SNP rs17655 which displayed differential allelic expression (DAE) in BEC and is an expression quantitative trait locus (eQTL) in lung tissue (p = 3.2E-7). SNPs in linkage (D' = 1) with rs17655 were predicted to alter miRNA binding (rs873601). A classifier model that comprised gene features CAT, CEBPG, GPX1, KEAP1, TP73, and XPA had pooled 10-fold cross-validation receiver operator characteristic area under the curve of 75.4% (95% CI: 66.3%-89.3%). The prevalence of DAE was higher than expected (p = 0.0023) in the classifier genes. CONCLUSIONS: GM genes comprised by COPD-associated BEC expression patterns were enriched for SNPs with cis-regulatory function, including a putative cis-rSNP in ERCC5 that was associated with COPD risk. These findings support additional total and allele-specific expression analysis of gene pathways with high prior likelihood for involvement in COPD pathogenesis.

Obstacles to and Solutions for a Successful Lung Cancer Screening Program.

Lung cancer screening with a low radiation dose chest CT scan has been shown to reduce the number of people, in a well-defined very high-risk cohort, who die from lung cancer. Many potential screening-related harms have been identified, including anxiety and morbidity related to the evaluation of screen-detected findings. A favorable balance of the benefit and harms of lung cancer screening requires careful implementation of a screening program, with a focus on several obstacles to the success of the program. In this review, evidence to support the benefit and harms of lung cancer screening is provided, followed by a discussion of 11 obstacles to the development of a high-quality program. For each obstacle, an approach is suggested, based on evidence and mandates as well as practicality and lessons learned. The approach to each of these obstacles highlights the multi-disciplinary nature of lung cancer screening, and the value of considering lung cancer screening a program rather than a test.

An official American Thoracic Society/American College of Chest Physicians policy statement: implementation of low-dose computed tomography lung cancer screening programs in clinical practice.

RATIONALE: Annual low-radiation-dose computed tomography (LDCT) screening for lung cancer has been shown to reduce lung cancer mortality among high-risk individuals and is now recommended by multiple organizations. However, LDCT screening is complex, and implementation requires careful planning to ensure benefits outweigh harms. Little guidance has been provided for sites wishing to develop and implement lung cancer screening programs. OBJECTIVES: To promote successful implementation of comprehensive LDCT screening programs that are safe, effective, and sustainable. METHODS: The American Thoracic Society (ATS) and American College of Chest Physicians (ACCP) convened a committee with expertise in lung cancer screening, pulmonary nodule evaluation, and implementation science. The committee reviewed the evidence from systematic reviews, clinical practice guidelines, surveys, and the experience of early-adopting LDCT screening programs and summarized potential strategies to implement LDCT screening programs successfully. MEASUREMENTS AND MAIN RESULTS: We address steps that sites should consider during the main three phases of developing an LDCT screening program: planning, implementation, and maintenance. We present multiple strategies to implement the nine core elements of comprehensive lung cancer screening programs enumerated in a recent ACCP/ATS statement, which will allow sites to select the strategy that best fits with their local context and workflow patterns. Although we do not comment on cost-effectiveness of LDCT screening, we outline the necessary costs associated with starting and sustaining a high-quality LDCT screening program. CONCLUSIONS: Following the strategies delineated in this policy statement may help sites to develop comprehensive LDCT screening programs that are safe and effective.

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer.

BACKGROUND: The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations. METHODS: Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup. RESULTS: 90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 - 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively. CONCLUSIONS: The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules.

BACKGROUND: Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION: Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS: Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS: In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION: The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Clinical validation of a cell-free DNA fragmentome assay for augmentation of lung cancer early detection.

Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.

Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors.

BACKGROUND: Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis. RESEARCH QUESTION: What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model? STUDY DESIGN AND METHODS: Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model. RESULTS: In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold. INTERPRETATION: The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.

Outcomes From More Than 1 Million People Screened for Lung Cancer With Low-Dose CT Imaging.

BACKGROUND: Lung cancer screening (LCS) with low-dose CT (LDCT) imaging was recommended in 2013, making approximately 8 million Americans eligible for LCS. The demographic characteristics and outcomes of individuals screened in the United States have not been reported at the population level. RESEARCH QUESTION: What are the outcomes among people screened and entered in the American College of Radiology's Lung Cancer Screening Registry compared with those of trial participants? STUDY DESIGN AND METHODS: This was a cohort study of individuals undergoing baseline LDCT imaging for LCS between 2015 and 2019. Predictors of adherence to annual screening were computed. LDCT scan interpretations by Lung Imaging Reporting and Data System (Lung-RADS) score, cancer detection rates (CDRs), and stage at diagnosis were compared with National Lung Cancer Screening Trial data. RESULTS: Adherence was 22.3%, and predictors of poor adherence included current smoking status and Hispanic or Black race. On baseline screening, 83% of patients showed negative results and 17% showed positive screening results. The overall CDR was 0.56%. The percentage of people with cancer detected at baseline was higher in the positive Lung-RADS categories at 0.4% for Lung-RADS category 3, 2.6% for Lung-RADS category 4A, 11.1% for Lung-RADS category 4B, and 19.9% for Lung-RADS category 4X. The cancer stage distribution was similar to that observed in the National Lung Cancer Screening Trial, with 53.5% of patients receiving a diagnosis of stage I cancer and 14.3% with stage IV cancer. Underreporting into the registry may have occurred. INTERPRETATION: This study revealed both the positive aspects of CT scan screening for lung cancer and the challenges that remain. Findings on CT imaging were correlated accurately with lung cancer detection using the Lung-RADS system. A significant stage shift toward early-stage lung cancer was present. Adherence to LCS was poor and likely contributes to the lower than expected cancer detection rate, all of which will impact the outcomes of patients undergoing screening for lung cancer.

The effect of metformin and thiazolidinedione use on lung cancer in diabetics.

BACKGROUND: Metformin and the thiazolidinediones (TZDs) may have a protective effect against the development of lung cancer. METHODS: Patients with diabetes mellitus (DM) were identified from the electronic medical records of the Cleveland Clinic. Diabetics with lung cancer were identified then verified by direct review of their records. Control subjects were matched with cancer subjects 1:1 by date of birth, sex, and smoking history. The frequency and duration of diabetic medication use was compared between the groups. The cancer characteristics were compared between those with lung cancer who had and had not been using metformin and/or a TZD. RESULTS: 93,939 patients were identified as having DM. 522 lung cancers in 507 patients were confirmed. The matched control group was more likely to have used metformin and/or a TZD (61.0% vs. 41.2%, p < 0.001 for any use; 55.5% vs. 24.6%, p < 0.001 for >24 months vs. 0-12 months). In the group with lung cancer, those who had used metformin alone had a different histology distribution than those who received neither metformin nor a TZD, were more likely to present with metastatic disease (40.8% vs. 28.2%, p = 0.013), and had a shorter survival from the time of diagnosis (HR 1.47, p < 0.005). CONCLUSIONS: The use of metformin and/or the TZDs is associated with a lower likelihood of developing lung cancer in diabetic patients. Diabetics who develop lung cancer while receiving metformin may have a more aggressive cancer phenotype.

Lung Cancer Screening.

Lung cancer screening with low-dose computed tomography (LDCT) reduces lung cancer deaths by early detection. The United States Preventive Services Task Force recommends lung cancer screening with LDCT in adults of age 50 years to 80 years who have at least a 20 pack-year smoking history and are currently smoking or have quit within the past 15 years. The implementation of a lung-cancer-screening program is complex. High-quality screening requires the involvement of a multidisciplinary team. The aim of a screening program is to find balance between mortality reduction and avoiding potential harms related to false-positive findings, overdiagnosis, invasive procedures, and radiation exposure. Components and processes of a high-quality lung-cancer-screening program include the identification of eligible individuals, shared decision-making, performing and reporting LDCT results, management of screen-detected lung nodules and non-nodule findings, smoking cessation, ensuring adherence, data collection, and quality improvement.

Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis.

BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

Proposed Quality Metrics for Lung Cancer Screening Programs: A National Lung Cancer Roundtable Project.

Lung cancer screening with a low radiation dose chest CT scan is the standard of care for screening-eligible individuals. The net benefit of screening may be optimized by delivering high-quality care, capable of maximizing the benefit and minimizing the harms of screening. Valid, feasible, and relevant indicators of the quality of lung cancer screening may help programs to evaluate their current practice and to develop quality improvement plans. The purpose of this project was to develop quality indicators related to the processes and outcomes of screening. Potential quality indicators were explored through surveys of multidisciplinary lung cancer screening experts. Those that achieved predefined measures of consensus for each of the validity, feasibility, and relevance domains are proposed as quality indicators. Each of the proposed indicators is described in detail, with guidance on how to define, measure, and improve program performance within the indicator.