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Background CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. Methods We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4-20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. Results The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. Conclusions Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. Trial registration NCT00047385, NCT01785342.

Background Chronic tobacco smoke exposure results in a broad range of lung pathologies including emphysema, airway disease and parenchymal fibrosis as well as a multitude of extra-pulmonary comorbidities. Prior work using CT imaging has identified several clinically relevant subgroups of smoking related lung disease, but these investigations have generally lacked organ specific molecular correlates. Research Question Can CT imaging be used to identify clinical phenotypes of smoking related lung disease that have specific bronchial epithelial gene expression patterns to better understand disease pathogenesis? Study Design and Methods Using K-means clustering, we clustered participants from the COPDGene study (n = 5,273) based on CT imaging characteristics and then evaluated their clinical phenotypes. These clusters were replicated in the Detection of Early Lung Cancer Among Military Personnel (DECAMP) cohort (n = 360), and were further characterized using bronchial epithelial gene expression. Results Three clusters (preserved, interstitial predominant and emphysema predominant) were identified. Compared to the preserved cluster, the interstitial and emphysema clusters had worse lung function, exercise capacity and quality of life. In longitudinal follow-up, individuals from the emphysema group had greater declines in exercise capacity and lung function, more emphysema, more exacerbations, and higher mortality. Similarly, genes involved in inflammatory pathways (tumor necrosis factor-α, interferon-β) are more highly expressed in bronchial epithelial cells from individuals in the emphysema cluster, while genes associated with T-cell related biology are decreased in these samples. Samples from individuals in the interstitial cluster generally had intermediate levels of expression of these genes. Interpretation Using quantitative CT imaging, we identified three groups of individuals in older ever-smokers that replicate in two cohorts. Airway gene expression differences between the three groups suggests increased levels of inflammation in the most severe clinical phenotype, possibly mediated by the tumor necrosis factor-α and interferon-β pathways. Clinical Trial Registration COPDGene (NCT00608764), DECAMP-1 (NCT01785342), DECAMP-2 (NCT02504697)

IntroductionWe performed an external validation of the Brock model using the National Lung Screening Trial (NLST) data set, following strict guidelines set forth by the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement. We report how external validation results can be interpreted and highlight the role of recalibration and model updating.Materials and methodsWe assessed model discrimination and calibration using the NLST data set. Adhering to the inclusion/exclusion criteria reported by McWilliams et al, we identified 7879 non-calcified nodules discovered at the baseline low-dose CT screen with 2 years of follow-up. We characterised differences between Pan-Canadian Early Detection of Lung Cancer Study and NLST cohorts. We calculated the slope on the prognostic index and the intercept coefficient by fitting the original Brock model to NLST. We also assessed the impact of model recalibration and the addition of new covariates such as body mass index, smoking status, pack-years and asbestos.ResultsWhile the area under the curve (AUC) of the model was good, 0.905 (95% CI 0.882 to 0.928), a histogram plot showed that the model poorly differentiated between benign and malignant cases. The calibration plot showed that the model overestimated the probability of cancer. In recalibrating the model, the coefficients for emphysema, spiculation and nodule count were updated. The updated model had an improved calibration and achieved an optimism-corrected AUC of 0.912 (95% CI 0.891 to 0.932). Only pack-year history was found to be significant (pConclusionWhile the Brock model achieved a high AUC when validated on the NLST data set, the model benefited from updating and recalibration. Nevertheless, covariates used in the model appear to be insufficient to adequately discriminate malignant cases.

Background: Randomized controlled trials (RCT) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST). Methods: Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations. Results: Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST [16% (95% confidence interval [CI]: 4–24)] could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening [e.g., 27% (95% CI, 11–37) for the population with 80% females and 80% current smokers] than those with lower prevalence of females and current smokers. Conclusions: This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest, including those that more closely mirror real-world populations. Impact: Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.

Purpose: Integrative analysis combining diagnostic imaging and genomic information can uncover biological insights into lesions that are visible on radiologic images. We investigate techniques for interrogating a deep neural network trained to predict quantitative image (radiomic) features and histology from gene expression in non-small cell lung cancer (NSCLC). Approach: Using 262 training and 89 testing cases from two public datasets, deep feedforward neural networks were trained to predict the values of 101 computed tomography (CT) radiomic features and histology. A model interrogation method called gene masking was used to derive the learned associations between subsets of genes and a radiomic feature or histology class [adenocarcinoma (ADC), squamous cell, and other]. Results: Overall, neural networks outperformed other classifiers. In testing, neural networks classified histology with area under the receiver operating characteristic curves (AUCs) of 0.86 (ADC), 0.91 (squamous cell), and 0.71 (other). Classification performance of radiomics features ranged from 0.42 to 0.89 AUC. Gene masking analysis revealed new and previously reported associations. For example, hypoxia genes predicted histology ( >0.90 AUC ). Previously published gene signatures for classifying histology were also predictive in our model ( >0.80 AUC ). Gene sets related to the immune or cardiac systems and cell development processes were predictive ( >0.70 AUC ) of several different radiomic features. AKT signaling, tumor necrosis factor, and Rho gene sets were each predictive of tumor textures. Conclusions: This work demonstrates neural networks' ability to map gene expressions to radiomic features and histology types in NSCLC and to interpret the models to identify predictive genes associated with each feature or type.

SummaryDetermining the clinical significance of CT scan-detected subsolid pulmonary nodules requires an understanding of the molecular and cellular features that may foreshadow disease progression. We studied the alterations at the transcriptome level in both immune and non-immune cells, utilizing single-cell RNA sequencing, to compare the microenvironment of subsolid, solid, and non-involved lung tissues from surgical resection specimens. This evaluation of early spectrum lung adenocarcinoma reveals a significant decrease in the cytolytic activities of natural killer and natural killer T cells, accompanied by a reduction of effector T cells as well as an increase of CD4+ regulatory T cells in subsolid lesions. Characterization of non-immune cells revealed that both cancer-associated alveolar type 2 cells and fibroblasts contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid lesions through ligand-receptor interactions. These findings suggest a decrement of immune surveillance in subsolid lesions.

Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.

Purpose The aim of this study was to examine radiologists’ beliefs about existing guidelines for pulmonary nodule evaluation. Methods A self-administered survey was developed to ascertain awareness of, agreement with, and adherence to published guidelines, including those from the Fleischner Society and the Lung CT Screening Reporting and Data System (Lung-RADS™). Surveys were distributed to 514 radiologists at 13 health care systems that are participating in a large, pragmatic trial of pulmonary nodule evaluation. Prespecified comparisons were made among groups defined by type of health system, years of experience, reader volume, and study arm. Results The response rate was 26.3%. Respondents were most familiar with guidelines from Fleischner (94%) and Lung-RADS (71%). For both incidental and screening-detected nodules, self-reported adherence to preferred guidelines was very high (97% and 94%, respectively), and most respondents believed that the benefits of adherence outweigh the harms (81% and 74%, respectively). Underlying evidence was thought to be high in quality by 68% of respondents for screening-detected nodules and 41% for incidental nodules. Approximately 70% of respondents believed that the frequency of recommended follow-up was “just right” for both guidelines. Radiologists who practice in nonintegrated health care systems were more likely to believe that the evidence was high in quality (79.5% versus 57.1%) and that the benefits of adherence outweigh the harms (85.1% versus 67.5%). Low-volume readers had lower awareness and self-reported adherence than higher volume readers. Conclusions Radiologists reported high levels of familiarity and agreement with and adherence to guidelines for pulmonary nodule evaluation, but many overestimated the quality of evidence in support of the recommendations.

ImportanceWhile cholinergic receptor nicotinic alpha 5 (CHRNA5) variants have been linked to lung cancer, chronic obstructive pulmonary disease (COPD) and smoking addiction in case–controls studies, their corelationship is not well understood and requires retesting in a cohort study.ObjectiveTo re-examine the association between the CHRNA5 variant (rs16969968 AA genotype) and the development of lung cancer, relative to its association with COPD and smoking.MethodsIn 9270 Non-Hispanic white subjects from the National Lung Screening Trial, a substudy of high-risk smokers were followed for an average of 6.4 years. We compared CHRNA5 genotype according to baseline smoking exposure, lung function and COPD status. We also compared the lung cancer incidence rate, and used multiple logistic regression and mediation analysis to examine the role of the AA genotype of the CHRNA5 variant in smoking exposure, COPD and lung cancer.ResultsAs previously reported, we found the AA high-risk genotype was associated with lower lung function (p=0.005), greater smoking intensity (pConclusionIn this large-prospective study, we found the CHRNA5 rs 16 969 968 AA genotype to be independently associated with smoking exposure, COPD and lung cancer (triple whammy effect).

We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.

Importance The potential to achieve greater reductions in lung cancer mortality than originally estimated by the National Lung Screening Trial with the inclusion of more Black participants stresses the importance of improving access to lung cancer screening for Black current and former smokers, a population presently with the highest lung cancer morbidity and mortality. Objective To estimate lung cancer and all-cause mortality reductions achievable with lung cancer screening via low-dose computed tomography (LDCT) of the chest in populations with greater proportions of Black screening participants than seen in the original NLST cohort. Design, setting, and participants This cohort study was conducted as a secondary analysis of existing data from the National Lung Screening Trial, a large national randomized clinical trial conducted from 2002 through 2009. NLST participants were current or former smokers, aged between 55 and 74 years, with at least 30 pack-years of smoking history and less than 15 years since quitting. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% CIs of lung cancer mortality and all-cause mortality according to LDCT screening compared with chest radiograph screening. Using a transportability formula, we estimated outcomes for LDCT screening among hypothetical populations by varying the distributions of Black individuals, women, and current smokers. Data were analyzed between September 2020 and March 2021. Exposures Lung screening with LDCT of the chest compared with chest radiography. Main outcomes and measures Lung cancer mortality and all-cause mortality. Results This study included a total of 53 452 participants enrolled in the NLST. Of 2376 Black individuals and 51 076 non-Black individuals, 21 922 (41.0%) were women and the mean (SD) age was 61.4 (5.0) years. Over a median (interquartile range) follow-up of 6.7 (6.2-7.0) years, LDCT screening among the synthesized population with a higher proportion of Black individuals (13.4%, mirroring US Census data) was associated with a greater relative reduction of lung cancer mortality (eg, Black individuals: HR, 0.82; 95% CI, 0.72-0.92; vs entire NLST cohort: HR, 0.84; 95% CI, 0.76-0.96). Further reductions in lung cancer mortality by LDCT screening were found among a hypothetical population with a higher proportion of men or current smokers, along with a higher proportion of Black individuals (ie, 60% Black participants; 20% to 40% women) (HR, 0.68; 95% CI, 0.48-0.97). Conclusions and relevance The potential to achieve greater reductions in lung cancer mortality than originally estimated by the NLST with the inclusion of more Black participants stresses the critical importance of improving access to lung cancer screening for Black current and former smokers.

Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: the low fraction of tumor DNA in cfDNA, the molecular heterogeneity of cancer, and sample sizes that are too small to reflect the heterogeneous patient population. We have developed an integrated cancer detection system, CancerRadar, that addresses all three challenges. It consists of (1) a cost-effective experimental assay, cfMethyl-Seq, for genome-wide methylation profiling of cfDNA, which provides >12-fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in CpG islands; and (2) a computational platform to extract information from cfMethyl-Seq data and diagnose the patient. The platform derives cfDNA methylations, cfDNA fragment sizes, copy number variations (CNV), and microbial composition from the raw cfMethyl-Seq data, and performs multi-feature ensemble learning. We demonstrate the power of CancerRadar by detecting and locating cancer in a cohort of 275 colon, liver, lung, and stomach cancer patients and 204 non-cancer individuals. For cancer detection, we achieve a sensitivity of 85.6%± 6.7% across all stages and 80.6%±9.1% for early stages (I and II), with a specificity of 99% in both cases. These metrics are derived using leave-one-out cross-validation. During independent validation on a reserved subsample, it achieves a sensitivity of 89.1%±11.3% across all stages and 85.7%±14.2% for early stages, with a specificity of 97% (one false positive). For locating a tumor's tissue of origin (TOO), CancerRadar achieved an accuracy of 91.5%±5.0% for all stages and 89.1%±7.3% for early stages, on an independent subsample. This study is the first to integrate cfDNA methylation, cfDNA fragment size, CNV, and microbial composition analyses for cancer detection on the same patient cohort. cfDNA methylation was the most useful for detecting cancer, but including features from other categories significantly increased the performance, especially for early-stage cancer. In contrast, with respect to TOO prediction, methylation-derived features were overwhelmingly important while including other features did not further improve performance. To fully exploit the power of cfDNA methylation, we identified four types of methylation markers with different characteristics. We have also improved our previous read-level deconvolution algorithm to more accurately identify trace tumor signals. Finally, our data show that as training sample sizes increase, the detection power of CancerRadar continues to increase. Although all existing cancer detection studies are limited by training sample sizes, the CancerRadar system uniquely and cost-effectively retains the genome-wide epigenetic and genetic profiles of cancer abnormalities, thereby permitting the classification models to learn and exploit newly significant features as training cohorts grow, as well as expanding their scope to other cancer types. Citation Format: Mary Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin Jung Chen, Paul Winograd, Shize Li, Zorawar Noor, Edward Garon, Samuel French, Clara Magyar, Sarah Dry, Clara Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven Dubinett, Denise Aberle, Vatche Agopian, Steven-Huy Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou. Multi-feature ensemble learning on cell-free dna for accurately detecting and locating cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 24.

The reduced lung cancer mortality observed with low-dose computed tomography (LDCT) screening in the National Lung Screening Trial (NLST) has led to annual screening in the United States as a covered benefit by both private insurers and the federal health insurance programme, the Centers for Medicare and Medicaid Services. Reimbursement for screening requires data submission to a federal registry on all individuals, whether privately or federally insured. Data must document individual patient eligibility as well as shared decision-making regarding the benefits and risks of LDCT screening, smoking cessation counselling, and the importance of annual screening. Beyond these requirements lie opportunities to maximise the benefits of screening in the radiology setting. Individuals eligible for screening account for a minority of those diagnosed with lung cancer in the US; the evidence needed to improve patient selection must be collected systematically for both screen-detected and incidentally detected lung nodules. Current nodule management and tracking guidelines reduce the false-positive rates observed in the NLST, but fall short in their ability to correctly classify nodules as benign or malignant. Smoking cessation is inadequately managed in most busy clinician practices. As a common nidus for tobacco-associated lung diseases, imagers are uniquely poised to collect the longitudinal data to better inform screening eligibility and to improve indeterminate nodule management, while maximising the setting of screening to motivate and provide smoking cessation. By re-engineering the notion of imaging practice, radiologists can be major contributors to lung cancer early detection and mortality reduction.

Although epidemiological studies consistently show that chronic obstructive pulmonary disease is associated with an increased risk of lung cancer, debate exists as to whether there is a linear relationship between the severity of airflow limitation and lung cancer risk.We examined this in a large, prospective study of older heavy smokers from the American College of Radiology Imaging Network subcohort of the National Lung Screening Trial (ACRIN). Airflow limitation was defined by prebronchodilator spirometry subgrouped according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4.In the National Lung Screening Trial-ACRIN cohort of 18,473 screening participants, 6,436 had airflow limitation (35%) and 12,037 (65%) had no airflow limitation. From these groups, 758 lung cancer cases were prospectively identified. Participants with airflow limitation were stratified according to GOLD groups 1 (n = 1,607), 2 (n = 3,528), 3 (n = 1,083), and 4 (n = 211). Lung cancer incidence at study end (mean follow-up, 6.4 yr) was compared between the GOLD groups and those with no airflow limitation (referent group).Compared with those with no airflow limitation, where lung cancer incidence was 3.78/1,000 person years, incidence rates increased in a simple linear relationship: GOLD 1 (6.27/1,000 person yr); GOLD 2 (7.86/1,000 person yr); GOLD 3 (10.71/1,000 person yr); and GOLD 4 (13.25/1,000 person yr). All relationships were significant versus the reference group at a P value of 0.0001 or less.In a large prospective study of high-risk cigarette smokers, we report a strong linear relationship between increasing severity of airflow limitation and risk of lung cancer.

This study retrospectively analyses the screening CT examinations and outcomes of the National Lung Screening Trial (NLST) participants who had interval lung cancer diagnosed within 1 year after a negative CT screen and before the next annual screen.The screening CTs of all 44 participants diagnosed with interval lung cancer (cases) were matched with negative CT screens of participants who did not develop lung cancer (controls). A majority consensus process was used to classify each CT screen as positive or negative according to the NLST criteria and to estimate the likelihood that any abnormalities detected retrospectively were due to lung cancer.By retrospective review, 40/44 cases (91%) and 17/44 controls (39%) met the NLST criteria for a positive screen (P 0.001). Cases had higher estimated likelihood of lung cancer (P 0.001). Abnormalities included pulmonary nodules ≥4 mm (n = 16), mediastinal (n = 8) and hilar (n = 6) masses, and bronchial lesions (n = 6). Cancers were stage III or IV at diagnosis in 32/44 cases (73%); 37/44 patients (84%) died of lung cancer, compared to 225/649 (35%) for all screen-detected cancers (P 0.0001).Most cases met the NLST criteria for a positive screen. Awareness of missed abnormalities and interpretation errors may aid lung cancer identification in CT screening.• Lung cancer within a year of a negative CT screen was rare. • Abnormalities likely due to lung cancer were identified retrospectively in most patients. • Awareness of error types may help identify lung cancer sooner.

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches.

e18592 Background: The National Lung Screening Trial (NLST) demonstrated a 20% reduction in lung cancer mortality when screened with low dose computed tomography (LDCT) as opposed to chest radiography. Notably, participants’ adherence to the screening protocol was 90%. To date, published evidence on the adherence of patients enrolled in clinical lung cancer screening (LCS) programs to LungRADS recommendations is limited. We investigate the adherence rate at our institution and determine the predictors of non-adherence to LungRADS recommendations. Methods: We performed a retrospective analysis on patients aged 50-80 years at time of baseline screen with initial screening exam at our institution between Jan 1, 2015 and Jan 12, 2021. Patients were excluded if 1) their follow-up period was insufficient to determine adherence as of Jan 28, 2021, 2) the follow-up recommendation was inconsistent with LungRADS guidelines, or 3) they died before the expected follow-up date. Adherence was defined as completion of recommended or more invasive follow-up at our institution within 12 months for LungRADS 0, 15 months for LungRADS 1/2, 9 months for LungRADS 3, 5 months for LungRADS 4A, and 3 months for LungRADS 4B/4X. A univariate logistic regression was used to determine predictors of non-adherence. Results: Among the 2120 eligible patients, 1266 (60%) were male and 854 (40%) were female with a median age of 65 at the baseline screen. One thousand four hundred and seventy-seven (70%) patients identified as White, 286 (13%) declared another racial group, and 357 (17%) did not disclose their race. One hundred and nine (5%) patients identified as Hispanic and 165 (8%) patients did not state their ethnicity. There were 1113 (53%) former smokers, 748 (35%) current smokers, and 259 (12%) patients of unspecified smoking status. Median tobacco exposure was 30 pack years (range 0.15 to 240). Fifty-seven percent of patients had private or commercial insurance while 39% had Medicare as primary insurance (3 patients were unspecified). The distribution of baseline LungRADS scores was 0: < 1%, 1: 14%, 2: 71%, 3: 7%, 4A: 4%, 4B: 2%, and 4X: < 1%. Overall adherence was 31% with 0: 38%, 1: 21%, 2: 27%, 3: 46%, 4A: 68%, 4B: 80%, and 4X: 100%. Of the 1463 non-adherent patients, 528 completed a follow-up exam beyond the expected date while 935 did not have any follow-up before the end of the study. Patients who were over 65 at baseline screen (OR = 1.34, 95% CI: 1.11, 1.61), former smokers (OR = 1.24, 95% CI: 1.02, 1.52), had Medicare insurance (OR = 1.35 95% CI: 1.12, 1.63), or had LungRADS 3/4 (referent: LR 1/2, OR = 4.29, 95% CI: 3.32, 5.55) were more likely to be adherent. Conclusions: Patient adherence to LungRADS recommendations at time of baseline screen in clinical practice is suboptimal, particularly among those with negative screens (LungRADS 1/2), with a non-adherence rate of > 70%. Baseline LungRADS scores, age, smoking status, and insurance are predictive of LCS non-adherence.

Each year, more than 1 million persons worldwide are found to have a lung nodule that carries a risk of being malignant. In reality, the vast majority of lung nodules are benign, whether identified by screening or incidentally. The consequences of delaying or missing the diagnosis of lung cancer can be substantial, as can be the consequences of invasive procedures on patients with benign lung nodules. The challenge for the clinician caring for these patients is to differentiate between benign and malignant nodules with the least harm possible. In this review, we will discuss management strategies of the indeterminate pulmonary nodule and will review recent advances and harm-reduction strategies.

Rationale and Objectives: The current paradigm of cancer diagnosis involves uncoordinated communication of findings from radiology and pathology to downstream physicians. Discordance between these findings can require additional time from downstream users to resolve, or given incorrect resolution, may adversely impact treatment decisions. To mitigate this problem, we developed a webbased system, called RadPath, for correlating and integrating radiology and pathology reporting. Materials and Methods: RadPath includes interfaces to our institution’s clinical information systems, which are used to retrieve reports, images, and test results that are structured into an interactive compendium for a diagnostic patient case. The system includes an editing interface for physicians, allowing for the inclusion of additional clinical data, as well as the ability to retrospectively correlate and contextualize imaging findings following pathology diagnosis. Results: During pilot deployment and testing over the course of 1 year, physicians at our institution have completed 60 RadPath cases, requiring an average of 128 seconds from a radiologist and an average of 93 seconds from a pathologist per case. Several technical and workflow challenges were encountered during development, including interfacing with diverse clinical information systems, automatically structuring report contents, and determining the appropriate physicians to create RadPath summaries. Reaction to RadPath has been positive, with users valuing the system’s ability to consolidate diagnostic information. Conclusions: With the increasing complexity of medicine and the movement toward team-based disease management, there is a need for improved clinical communication and information exchange. RadPath provides a platform for generating coherent and correlated diagnostic summaries in cancer diagnosis with minimal additional effort from physicians.

The diagnostic definition of indeterminate lung nodules as malignant or benign poses a major challenge for clinicians. We discovered a potential marker, the sodium-dependent glucose transporter 2 (SGLT2), whose activity identified metabolically active lung premalignancy and early-stage lung adenocarcinoma (LADC). We found that SGLT2 is expressed early in lung tumorigenesis and is found specifically in premalignant lesions and well-differentiated adenocarcinomas. SGLT2 activity could be detected in vivo by positron emission tomography (PET) with the tracer methyl 4-deoxy-4-[18F] fluoro-alpha-d-glucopyranoside (Me4FDG), which specifically detects SGLT activity. Using a combination of immunohistochemistry and Me4FDG PET, we identified high expression and functional activity of SGLT2 in lung premalignancy and early-stage/low-grade LADC. Furthermore, selective targeting of SGLT2 with FDA-approved small-molecule inhibitors, the gliflozins, greatly reduced tumor growth and prolonged survival in autochthonous mouse models and patient-derived xenografts of LADC. Targeting SGLT2 in lung tumors may intercept lung cancer progression at early stages of development by pairing Me4FDG PET imaging with therapy using SGLT2 inhibitors.

Introduction In the National Lung Screening Trial (NLST) all cases with a 4-mm nodule (micronodule) and no other findings were classified as a negative study. The prevalence and malignant potential of micronodules in the NLST is evaluated to understand if this classification was appropriate. Methods and Materials In the NLST a total of 53,452 participants were enrolled with 26,722 undergoing low-dose computed tomography (CT) screening. To determine whether a micronodule developed into a lung cancer, a list from the NLST database of those participants who developed lung cancer and had a micronodule recorded was selected. The CT images of this subset were reviewed by experienced, fellowship-trained thoracic radiologists (R.F.M., C.C., P.M.B., and D.R.A.), all of whom participated as readers in the NLST. Results There were 26,722 participants who underwent CT in the NLST, of which 11,326 (42%) participants had at least one CT with a micronodule. Five thousand five hundred sixty (49%) of these participants had at least one positive CT examination, of which 409 (3.6%) subsequently were diagnosed with lung cancer. Of the 409 lung cancer cases with a micronodule recorded, there were 13 cases in which a micronodule developed into lung cancer. Considering the 13 cases, they represent 1.2% (13 of 1089) of the lung cancers diagnosed in the CT arm of the NLST and 0.11% (13 of 11,326) of the total micronodule cases. Additionally they represent 0.23% (13 of 5560) of the micronodule and at least one positive CT examination cases and 3.2% (13 of 409) of the micronodule cases diagnosed with lung cancer. The average size of the nodule at baseline (recorded as maximum diameter by perpendicular diameter) was 3.0 × 2.5 mm (ranges 2 x 4 mm and 2 x 4 mm) and at the positive CT the nodule was 11.1 × 8.6 mm (ranges, 6 x 20 mm and 5 x 14 mm); a difference of average change in size of 8.1 × 6.1 mm. The average number of days from first CT with a micronodule recorded to positive CT was 459 days (range, 338 – 723 days), the mean time from first CT with micronodule to lung cancer diagnosis was 617 days (range, 380 – 1140 days) and the mean time from positive CT to lung cancer diagnosis was 160 days (range, 18 – 417 days). Histologically, there was one small cell carcinoma and 12 non–small cell with stages of IA in 8 (62%), stage IB in 2 (15%), and 1 each stage IIIA, IIIB, and IV. The overall survival of NSCLC cases with a micronodule was not significantly different than the survival of the CT subset diagnosed with NSCL (p = 0.36). Conclusions Micronodules are common among lung cancer–screened participants and are capable of developing into lung cancer; however, following micronodules by annual CT screening surveillance is appropriate and does not impact overall survival or outcome.

Objective It is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on “contextualized semantic maps,” captures the findings of biomedical studies as well as the contexts associated with patient population and study design. This paper presents a user-oriented evaluation of Casama in comparison to a context-free representation, SemRep. Materials and methods The effectiveness of the representation was evaluated by presenting users with manually annotated Casama and SemRep summaries of ten articles on driver mutations in cancer. Automatic annotations were evaluated on a collection of articles on EGFR mutation in lung cancer. Seven users completed a questionnaire rating the summarization quality for various topics and applications. Results Casama had higher median scores than SemRep for the majority of the topics (p ≤ 0.00032), all of the applications (p ≤ 0.00089), and in overall summarization quality (p ≤ 1.5e-05). Casama's manual annotations outperformed Casama's automatic annotations (p = 0.00061). Discussion Casama performed particularly well in the representation of strength of evidence, which was highly rated both quantitatively and qualitatively. Users noted that Casama's less granular, more targeted representation improved usability compared to SemRep. Conclusion This evaluation demonstrated the benefits of a contextualized representation for summarizing biomedical literature on cancer. Iteration on specific areas of Casama's representation, further development of its algorithms, and a clinically-oriented evaluation are warranted.

In a landmark analysis, investigators of the Multicentric Italian Lung Detection (MILD) trial have confirmed 10-year mortality reductions with lung cancer screening using low-dose helical CT (LDCT). These data complement the reduced lung cancer-specific mortality reported in the National Lung Screening Trial and reinforce the rationale for broad implementation of LDCT screening in high-risk populations.

Rationale and Objectives The purpose of this study was to quantify the degree of imaging–histologic discordance in a cohort of patients undergoing computed tomography (CT)–guided lung biopsy for focal lung disease. Materials and Methods A retrospective review was performed of 186 patients who underwent percutaneous lung biopsy of a parenchymal lesion at our institution between January and December 2009. Diagnostic radiology reports of CT or positron emission tomography–CTs performed before biopsy were used to classify the lesion as malignant or benign by five readers. Pathology reports of the biopsied lesions were classified by three readers. Inter-reader agreement and imaging–histologic concordance were quantified using kappa statistics. Discordant benign cases were then revisited to determine downstream effects. Results Inter-reader agreement on report content was substantial or almost perfect with kappas >0.783. Kappas for concordance were as follows: malignant (0.448), primary lung cancer (0.517), metastatic disease to lung (0.449), benign (0.510), and overall agreement (0.381). Of the twelve discordant benign cases that were revisited, four were found to be false negatives, resulting in a delay in diagnosis. Conclusions Our study of imaging–histologic discordance in percutaneous biopsy of lung lesions supports the need for imaging report standardization and improved integration and communication between the fields of radiology and pathology.

We estimated mean life-years, quality-adjusted life-years (QALYs), costs per person, and incremental cost-effectiveness ratios (ICERs) for three alternative strategies: screening with low-dose CT, screening with radiography, and no screening. Estimations of life-years were based on the number of observed deaths that occurred during the trial and the projected survival of persons who were alive at the end of the trial. Quality adjustments were derived from a subgroup of participants who were selected to complete quality-of-life surveys. Costs were based on utilization rates and Medicare reimbursements. We also performed analyses of subgroups defined according to age, sex, smoking history, and risk of lung cancer and performed sensitivity analyses based on several assumptions. RESULTS As compared with no screening, screening with low-dose CT cost an additional $1,631 per person (95% confidence interval [CI], 1,557 to 1,709) and provided an additional 0.0316 life-years per person (95% CI, 0.0154 to 0.0478) and 0.0201 QALYs per person (95% CI, 0.0088 to 0.0314). The corresponding ICERs were $52,000 per life-year gained (95% CI, 34,000 to 106,000) and $81,000 per QALY gained (95% CI, 52,000 to 186,000). However, the ICERs varied widely in subgroup and sensitivity analyses. CONCLUSIONS We estimated that screening for lung cancer with low-dose CT would cost $81,000 per QALY gained, but we also determined that modest changes in our assumptions would greatly alter this figure. The determination of whether screening outside the trial will be cost-effective will depend on how screening is implemented. (Funded by the National Cancer Institute; NLST ClinicalTrials.gov number, NCT00047385.)

Patient portals have the potential to provide content that is specifically tailored to a patient's information needs based on diagnoses and other factors. In this work, we conducted a survey of 41 lung cancer patients at an outpatient lung cancer clinic at the medical center of the University of California Los Angeles, to gain insight into these perceived information needs and opinions on the design of a portal to fulfill them. We found that patients requested access to information related to diagnosis and imaging, with more than half of the patients reporting that they did not anticipate an increase in anxiety due to access to medical record information via a portal. We also found that patient educational background did not lead to a significant difference in desires for explanations of reports and definitions of terms.

This year marks the 50th anniversary of the 1964 Surgeon General's Report on Smoking and Health . This landmark publication found a definitive, causal link between smoking and lung cancer in men, and, along with the subsequent reports, spurred a series of important public health initiatives that

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

Background: The majority of NSCLC patients do not respond to single agent PD-1/PD-L1 inhibitors, in part due to the lack of cytolytic T cell infiltration at the tumor site. To improve the efficacy of checkpoint blockade, in situ vaccination with functional antigen presenting cells (APCs) is designed to take advantage of the full repertoire of tumor antigens. This converts the tumor into a lymph node-like environment to promote T cell activation both locally and systemically. The chemokine CCL21 promotes co-localization of naive T cells and DCs to facilitate T cell activation. Our preclinical studies and recently completed phase I trial demonstrated that intratumoral (IT) administration of DC genetically modified to overexpress CCL21 (CCL21-DC) augments tumor antigen presentation in situ, resulting in more effective T cell responses and systemic antitumor immunity. We also observed increased PD-L1 expression in the tumor microenvironment (TME) in a subset of patients, suggesting that tumor-mediated impairment of T cell function may be forestalling a more robust antitumor response. Similarly, the lack of PD-1/PD-L1 inhibitor efficacy could be combated by enhanced T cell infiltration and augmented APC function following IT CCL21-DC delivery. Therefore, in a phase I trial initiated in November 2018, we are combining IT CCL21-DC with intravenous (IV) pembrolizumab to amplify host antitumor immunity in NSCLC with low or absent baseline PD-L1 expression, who often do not respond to PD-1 inhibition alone. Methods: This is a single institution, non-randomized, dose-escalating, multi-cohort trial followed by an expansion cohort at the dose established during dose escalation. Patients will be included with pathologically confirmed and radiographically measurable stage IV NSCLC expressing PD-L1 in less than 50% of cells without EGFR or ALK mutations, who have tumor accessible by either CT-guided intervention or bronchoscopy, and who are naïve to systemic treatment for NSCLC. Up to 12 patients will be evaluated in a dose escalation cohort. An additional 12 patients will be enrolled in an expansion cohort. Patients will receive three IT injections of autologous CCL21-DC (days 0, 21, 42) together with IV pembrolizumab. The IV pembrolizumab will continue every 3 weeks for up to 1 year. All patients will be monitored for clinical efficacy as well as local and systemic immune responses to define potential determinants of the response. The primary objective in the dose escalation will be to determine the safety and maximum tolerated dose (MTD) of IT CCL21-DC (5x106, 1x107, 3x107) when combined with IV pembrolizumab. The primary objective in the dose expansion will be to evaluate the objective response rate (ORR) in patients treated with MTD of CCL21-DC combined with IV pembrolizumab. This trial is registered at ClinicalTrial.gov (NCT03546361), and is currently open for enrollment. Citation Format: Bin Liu, Edward B. Garon, Aaron E. Lisberg, Ramin Salehi-Rad, Jay M. Lee, Linh M. Tran, Kostyantyn Krysan, Rui Li, Raymond J. Lim, Manash Paul, Ying Lin, Zhe Jing, Fereidoun Abtin, Robert D. Suh, Scott Oh, Denise R. Aberle, Lauren E. Winter, William Dean Wallace, David Elashoff, Sherven Sharma, Steven M. Dubinett. A Phase I trial of in situ vaccination with autologous CCL21-modified dendritic cells (CCL21-DC) combined with pembrolizumab for advanced NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT226.

Lung cancer is the leading cause of cancer-related death, and lung adenocarcinoma (LUAD) is the most frequent histology. Early diagnosis and treatment are essential; however, there are no targeted diagnostic and therapeutic approaches for early LUAD. An important hallmark of cancer is the increased glucose uptake via GLUT transporters. GLUT activity is imaged in cancer by positron emission tomography (PET) with 2-[18F] fluorodeoxyglucose (FDG). FDG PET is a standard tool for staging advanced lung cancer, but has low sensitivity for early-stage LUAD. This is considered a consequence of slow growth and low requirement of glucose. However, we discovered a novel system of metabolic supply not detected by FDG PET: the sodium glucose transporter 2 (SGLT2)1. SGLT2 activity can be measured in vivo with the PET tracer methyl-4-[18F] fluorodeoxyglucose (Me4FDG). We identified high expression of SGLT2 in human lung premalignancy and early-stage LUAD. Me4FDG detects early-stage, FDG-negative LUAD in mouse models. Targeting SGLT2 with FDA-approved inhibitors significantly reduces tumor growth and prolongs survival in genetic and patient-derived murine models, confirming an important role of SGLT2 in early-stage LUAD2. The reliance of early stage LUAD on SGLT2 opens exciting diagnostic and therapeutic opportunities. The National Lung Screening Trial showed a 20% reduction in lung cancer mortality in high risk individuals using low-dose helical computed tomography (CT). CT is highly sensitive for detecting lung nodules, but is limited by low specificity, especially for LUAD. On CT, LUAD may appear as solid or subsolid nodules. Most subsolid nodules are not cancer, and many will remain stable or resolve; however, subsolid lesions can represent premalignant lesions or adenocarcinoma in situ. These lesions in the early spectrum of LUAD may persist for months to years before transforming into invasive disease. As a result, current standard of care is to follow these patients with CT imaging to monitor these indeterminate lesions for radiologic signs of malignant progression. The identification of novel biomarkers to predict the malignant potential of these nodules at their initial identification is of paramount importance. 1Scafoglio et al. Proc Natl Acad Sci U S A 2015;112(30):E4111-4119 2Scafoglio et al. Sci. Transl. Med. 10, eaat5933 (2018) Citation Format: Brendon Villegas, Eva Koziolek, Jie Liu, W. Dean Wallace, David Elashoff, Denise R. Aberle, David B. Shackelford, Jorge R. Barrio, Jane Yanagawa, Steven M. Dubinett, Claudio Scafoglio. SGLT2 is a diagnostic target for early stage lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4224.

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Background Lung cancer is the largest contributor to mortality from cancer. The National Lung Screening Trial (NLST) showed that screening with low-dose helical computed tomography (CT) rather than with chest radiography reduced mortality from lung cancer. We describe the screening, diagnosis, and limited treatment results from the initial round of screening in the NLST to inform and improve lung-cancer-screening programs. Methods At 33 U.S. centers, from August 2002 through April 2004, we enrolled asymptomatic participants, 55 to 74 years of age, with a history of at least 30 pack-years of smoking. The participants were randomly assigned to undergo annual screening, with the use of either low-dose CT or chest radiography, for 3 years. Nodules or other suspicious findings were classified as positive results. This article reports findings from the initial screening examination. Results A total of 53,439 eligible participants were randomly assigned to a study group (26,715 to low-dose CT and 26,724 to chest radiography); 26,309 participants (98.5%) and 26,035 (97.4%), respectively, underwent screening. A total of 7191 participants (27.3%) in the low-dose CT group and 2387 (9.2%) in the radiography group had a positive screening result; in the respective groups, 6369 participants (90.4%) and 2176 (92.7%) had at least one follow-up diagnostic procedure, including imaging in 5717 (81.1%) and 2010 (85.6%) and surgery in 297 (4.2%) and 121 (5.2%). Lung cancer was diagnosed in 292 participants (1.1%) in the low-dose CT group versus 190 (0.7%) in the radiography group (stage 1 in 158 vs. 70 participants and stage IIB to IV in 120 vs. 112). Sensitivity and specificity were 93.8% and 73.4% for low-dose CT and 73.5% and 91.3% for chest radiography, respectively. Conclusions The NLST initial screening results are consistent with the existing literature on screening by means of low-dose CT and chest radiography, suggesting that a reduction in mortality from lung cancer is achievable at U.S. screening centers that have staff experienced in chest CT. (Funded by the National Cancer Institute; NLST ClinicalTrials.gov number, NCT00047385.).

The National Lung Screening Trial (NLST) has provided compelling evidence of the efficacy of lung cancer screening using low-dose helical computed tomography (LDCT) to reduce lung cancer mortality. The NLST randomized 53,454 older current or former heavy smokers to receive LDCT or chest radiography (CXR) for three annual screens. Participants were observed for a median of 6.5 years for outcomes. Vital status was available in more than 95% of participants. LDCT was positive in 24.2% of screens, compared with 6.9% of CXRs; more than 95% of all positive LDCT screens were not associated with lung cancer. LDCT detected more than twice the number of early-stage lung cancers and resulted in a stage shift from advanced to early-stage disease. Complications of LDCT screening were minimal. Lung cancer–specific mortality was reduced by 20% relative to CXR; all-cause mortality was reduced by 6.7%. The major harms of LDCT are radiation exposure, high false-positive rates, and the potential for overdiagnosis. This review discusses the risks and benefits of LDCT screening as well as an approach to LDCT implementation that incorporates systematic screening practice with smoking cessation programs and offers opportunities for better determination of appropriate risk cohorts for screening and for better diagnostic prediction of lung cancer in the setting of screen-detected nodules. The challenges of implementation are considered for screening programs, for primary care clinicians, and across socioeconomic strata. Considerations for future research to complement imaging-based screening to reduce the burden of lung cancer are discussed.

Background eHealth apps have the potential to meet the information needs of patient populations and improve health literacy rates. However, little work has been done to document perceived usability of portals and health literacy of specific topics. Objective Our aim was to establish a baseline of lung cancer health literacy and perceived portal usability. Methods A survey based on previously validated instruments was used to assess a baseline of patient portal usability and health literacy within the domain of lung cancer. The survey was distributed via Amazon’s Mechanical Turk to 500 participants. Results Our results show differences in preferences and literacy by demographic cohorts, with a trend of chronically ill patients having a more positive reception of patient portals and a higher health literacy rate of lung cancer knowledge (P

Summary Background Annual low-dose CT screening for lung cancer has been recommended for high-risk individuals, but the necessity of yearly low-dose CT in all eligible individuals is uncertain. This study examined rates of lung cancer in National Lung Screening Trial (NLST) participants who had a negative prevalence (initial) low-dose CT screen to explore whether less frequent screening could be justified in some lower-risk subpopulations. Methods We did a retrospective cohort analysis of data from the NLST, a randomised, multicentre screening trial comparing three annual low-dose CT assessments with three annual chest radiographs for the early detection of lung cancer in high-risk, eligible individuals (aged 55–74 years with at least a 30 pack-year history of cigarette smoking, and, if a former smoker, had quit within the past 15 years), recruited from US medical centres between Aug 5, 2002, and April 26, 2004. Participants were followed up for up to 5 years after their last annual screen. For the purposes of this analysis, our cohort consisted of all NLST participants who had received a low-dose CT prevalence (T0) screen. We determined the frequency, stage, histology, study year of diagnosis, and incidence of lung cancer, as well as overall and lung cancer-specific mortality, and whether lung cancers were detected as a result of screening or within 1 year of a negative screen. We also estimated the effect on mortality if the first annual (T1) screen in participants with a negative T0 screen had not been done. The NLST is registered with ClinicalTrials.gov, number NCT00047385. Findings Our cohort consisted of 26 231 participants assigned to the low-dose CT screening group who had undergone their T0 screen. The 19 066 participants with a negative T0 screen had a lower incidence of lung cancer than did all 26 231 T0-screened participants (371·88 [95% CI 337·97–408·26] per 100 000 person-years vs 661·23 [622·07–702·21]) and had lower lung cancer-related mortality (185·82 [95% CI 162·17–211·93] per 100 000 person-years vs 277·20 [252·28–303·90]). The yield of lung cancer at the T1 screen among participants with a negative T0 screen was 0·34% (62 screen-detected cancers out of 18 121 screened participants), compared with a yield at the T0 screen among all T0-screened participants of 1·0% (267 of 26 231). We estimated that if the T1 screen had not been done in the T0 negative group, at most, an additional 28 participants in the T0 negative group would have died from lung cancer (a rise in mortality from 185·82 [95% CI 162·17–211·93] per 100 000 person-years to 212·14 [186·80–239·96]) over the course of the trial. Interpretation Participants with a negative low-dose CT prevalence screen had a lower incidence of lung cancer and lung cancer-specific mortality than did all participants who underwent a prevalence screen. Because overly frequent screening has associated harms, increasing the interval between screens in participants with a negative low-dose CT prevalence screen might be warranted. Funding None.

Lung cancer screening identifies cancers with heterogeneous behaviors. Some lung cancers will be identified among patients who had prior negative CT screens and upon follow-up scans develop a de novo nodule that was determined to be cancerous. Other lung cancers will be identified among patients who had one or more prior stable positive scans that were not determined to be lung cancer (indeterminate pulmonary nodules), but in follow-up scans was diagnosed with an incidence lung cancer. Using data from the CT arm of the National Lung Screening Trial, this analysis investigated differences in patient characteristics and survival endpoints between prevalence-, interval-, and screen-detected lung cancers, characterized based on sequence of screening results. Lung cancers immediately following a positive baseline (T0), and prior to the T1 screen, formed the prevalence cohort. Interval cancers were diagnosed following a negative screen at any time point prior to the next screening round. Two cohorts of screen-detected lung cancers (SDLC) were identified that had a baseline positive screen that was that was not determined to be lung cancer (i.e., an indeterminate pulmonary nodule), but in follow-up scans was diagnosed with an incidence lung cancer 12 (SDLC1) or 24 (SDLC2) months later. Two other incidence cohorts had screen-detected lung cancers that had baseline negative screen and upon follow-up scans developed a de novo nodule determined to be cancerous at 12 (SDLC3) or 24 (SDLC4) months later. Differences in patient characteristics, progression-free survival (PFS), and overall survival (OS) were assessed. The lung cancer-specific death rate was higher for SDLC3/SDLC4 compared to SDLC1/SDLC2 lung cancers (136.6/1,000 person-years vs. 71.3/1,000 person-years, P < 0.001). Moreover, PFS and OS were significantly lower for SDLC3/SDLC4 compared to SDLC1/SDLC2 (P < 0.004; P < 0.002, respectively). The findings were consistent when stratified by stage and histology. Multivariable Cox proportional models revealed that the SDLC3/SDLC4 case groups were associated with significantly poorer PFS (HR = 1.89; 95% CI 1.31-2.74) and OS (HR = 1.80; 95% CI 1.21-2.67) compared to SDLC1/SDLC2 lung cancers (HR = 1.00). Lung cancer patients who develop a de novo nodule that determined to be cancerous (i.e., at least one negative CT screen prior to cancer diagnosis) had poorer survival outcomes compared to patients who had at least one positive screen prior to cancer diagnosis. As such, the observation that de novo screen-detected are associated with poorer survival could be attributed to faster growing, more aggressive cancers that arose from a lung environment previously lacking focal abnormalities.

OVERVIEW: The results of the National Lung Screening Trial (NLST) have provided the medical community and American public with considerable optimism about the potential to reduce lung cancer mortality with imaging-based screening. Designed as a randomized trial, the NLST has provided the first evidence of screening benefit by showing a 20% reduction in lung cancer mortality and a 6.7% reduction in all-cause mortality with low dose helical computed tomography (LDCT) screening relative to chest X-ray. The major harms of LDCT screening include the potential for radiation-induced carcinogenesis; high false-positivity rates in individuals without lung cancer, and overdiagnosis. Following the results of the NLST, the National Comprehensive Cancer Network (NCCN) published the first of multiple lung cancer screening guidelines under development by major medical organizations. These recommendations amalgamated screening cohorts, practices, interpretations, and diagnostic follow-up based on the NLST and other published studies to provide guidance for the implementation of LDCT screening. There are major areas of opportunity to optimize implementation. These include standardizing practices in the screening setting, optimizing risk profiles for screening and for managing diagnostic evaluation in individuals with indeterminate nodules, developing interdisciplinary screening programs in conjunction with smoking cessation, and approaching all stakeholders systematically to ensure the broadest education and dissemination of screening benefits relative to risks. The incorporation of validated biomarkers of risk and preclinical lung cancer can substantially enhance the effectiveness screening programs.

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" nodule�3 mm," " nodule

Background To promote results in the National Lung Screening Trial (NLST) that are generalizable across the entire US population, a subset of NLST sites developed dedicated strategies for minority recruitment. Purpose To report the effects of targeted strategies on the accrual of underrepresented groups, to describe participant characteristics, and to estimate the costs of targeted enrollment. Methods The 2002–2004 Tobacco Use Supplement was used to estimate eligible proportions of racial and ethnic categories. Strategic planning included meetings/conferences with key stakeholders and minority organizations. Potential institutions were selected based upon regional racial/ethnic diversity and proven success in recruitment of underrepresented groups. Seven institutions submitted targeted recruitment strategies with budgets. Accrual by racial/ethnic category was tracked for each institution. Cost estimates were based on itemized receipts for minority strategies relative to minority accrual. Results Of 18,842 participants enrolled, 1576 (8.4%) were minority participants. The seven institutions with targeted recruitment strategies accounted for 1223 (77.6%) of all minority participants enrolled. While there was a significant increase in the rate of minority accrual pre-implementation to post-implementation for the institutions with targeted recruitment (9.3% vs. 15.2%, p Limitations Data on minority recruitment processes were not consistently kept at the individual institutions. In addition, participant responses via newspaper advertisements and the efforts of minority staff hired by the institutions could not be coded on Case Report Forms. Conclusions Strategic efforts were associated with significant increases in minority enrollment. The greatest successes require that a priori goals be established based on eligible racial/ethnic proportions; the historical performance of sites in minority accrual should factor into the selection of sites; recruitment planning must begin well in advance of trial launch; and there must be endorsement by prominent representatives of the racial groups of interest.

The National Lung Screening Trial (NLST) is a randomized multicenter study comparing low-dose helical computed tomography (CT) with chest radiography in the screening of older current and former heavy smokers for early detection of lung cancer, which is the leading cause of cancer-related death in the United States. Five-year survival rates approach 70% with surgical resection of stage IA disease; however, more than 75% of individuals have incurable locally advanced or metastatic disease, the latter having a 5-year survival of less than 5%. It is plausible that treatment should be more effective and the likelihood of death decreased if asymptomatic lung cancer is detected through screening early enough in its preclinical phase. For these reasons, there is intense interest and intuitive appeal in lung cancer screening with low-dose CT. The use of survival as the determinant of screening effectiveness is, however, confounded by the well-described biases of lead time, length, and overdiagnosis. Despite previous attempts, no test has been shown to reduce lung cancer mortality, an endpoint that circumvents screening biases and provides a definitive measure of benefit when assessed in a randomized controlled trial that enables comparison of mortality rates between screened individuals and a control group that does not undergo the screening intervention of interest. The NLST is such a trial. The rationale for and design of the NLST are presented.

Background The National Lung Screening Trial (NLST), a randomized study conducted at 33 US sites, is comparing lung cancer mortality among persons screened with reduced dose helical computerized tomography and among persons screened with chest radiograph. In this article, we present characteristics of the study population. Methods Eligible participants were aged 55–74 years and were current or former smokers with a cigarette smoking history of at least 30 pack-years. Randomization was stratified by site, sex, and age. To assess representativeness of the study population, demographic characteristics of individuals from the general population who met NLST age and smoking history inclusion criteria were obtained from the Tobacco Use Supplement of the US Census Bureau Current Population Surveys. Results The NLST enrolled 53 456 persons, with 26 733 randomly assigned to chest radiograph screening and 26 723 to computerized tomography screening. Characteristics of the participants were as follows: 31 533 (59%) were men, 39 234 (73%) were younger than 65 years, 25 779 (48%) were current smokers, and 16 839 (32%) had a college or higher degree. Median cigarette exposure was 48 pack-years. Among Tobacco Use Supplement respondents who met NLST age and smoking history criteria, 59% were men, 65% were younger than 65 years, and 57% were current smokers. Median cigarette exposure among this group was 47 pack-years, and 14% had a college degree or higher. Conclusion The NLST cohort has a distribution of sex and pack-year history that is similar to the component of the general US population that meets the major NLST eligibility criteria; however, NLST participants are younger, better educated, and less likely to be current smokers.

Lung cancer continues to be a major public health problem, and more patients die from this disease than any other cancer. The vast majority of patients present with advanced stage disease when therapeutic options are limited and the overall 5 year survival rate remains approximately 15%. Screening with low dose helical computed tomography (CT)has been suggested for early detection, although the effect on mortality is currently under investigation. As part of the National Lung Cancer Screening Trial, a specimen biorepository including blood, sputum, and urine were collected serially for the primary purpose of validating early detection lung cancer biomarkers. In addition tumor samples have been obtained from patients diagnosed with lung cancer to be included in a tissue microarray. This commentary describes the rationale, composition, intent, and availability of specimen in the biorepository.