Assessing the benefits and harms of low-dose computed tomography screening for lung cancer

Genomics and the early diagnosis of lung cancer

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages, resulting in poor survival rates. Early detection significantly improves outcomes, yet current screening methods, such as low-dose computed tomography (LDCT), are limited by high false-positive rates, radiation exposure, and restricted eligibility criteria. This review highlights the transformative potential of genomic and molecular technologies in advancing the early detection of LC. Key innovations include liquid biopsy tools, such as circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) analysis, which offer minimally invasive approaches to detect tumor-specific genetic and epigenetic alterations. Emerging biomarkers, including methylation signatures, cfDNA fragmentomics, and multi-omics profiles, demonstrate improved sensitivity and specificity in identifying early-stage tumors. Advanced platforms like next-generation sequencing (NGS) and machine-learning algorithms further enhance diagnostic accuracy. Integrated approaches that combine genomic data with LDCT imaging and artificial intelligence (AI) show promise in addressing current limitations by improving risk stratification and nodule characterization. The review also explores multi-cancer early detection assays and precision diagnostic strategies tailored for diverse at-risk populations. By leveraging these advancements, clinicians can achieve earlier diagnoses, reduce unnecessary procedures, and ultimately decrease LC mortality.

Artificial intelligence-assisted point-of-care devices for lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, primarily due to late-stage detection, which limits treatment options. Early detection and screening can increase survival rates, but traditional medical imaging methods are costly and inconvenient. Point-of-care biosensors present a promising alternative, being user-friendly, less labor-intensive, and minimally invasive. With high sensitivity and selectivity, these biosensors detect lung cancer-associated biomarkers, including protein and nucleic acid, in biological fluids such as serum, urine, and saliva. Integrating artificial intelligence (AI) with biosensors has further improved their performance. AI algorithms can analyze complex data, differentiate lung cancer patients from healthy individuals, and even predict the risk of cancer metastasis. Despite these advancements, a comprehensive review of AI-coupled biosensors for lung cancer screening and detection has not yet been conducted. The clinical translation of these biosensors is challenged by a lack of standardization in biomarker selection, the number of biomarkers tested, and the determination of clinical cut-off values. This review focuses on recent advances in biosensors for lung cancer screening and detection, the challenges in their clinical application, and the role of AI in improving biosensor performance. Additionally, it explores future perspectives on the evolution of AI-assisted biosensors into comprehensive health monitoring systems, aiming to bridge the gap between technological innovation and practical clinical use.

Secretome and Proteome of Extracellular Vesicles Provide Protein Markers of Lung and Colorectal Cancer

Colorectal cancer (CRC) and lung cancer (LC) are leading causes of cancer-related mortality, highlighting the need for minimally invasive diagnostic, prognostic, and predictive markers for these cancers. Proteins secreted by a tumor into the extracellular space directly, known as the tumor secretome, as well as proteins in the extra-cellular vesicles (EVs), represent an attractive source of biomarkers for CRC and LC. We performed proteomic analyses on secretome and EV samples from LC (A549, NCI-H23, NCI-H460) and CRC (Caco2, HCT116, HT-29) cell lines and targeted mass spectrometry on EVs from plasma samples of 20 patients with CRC and 19 healthy controls. A total of 782 proteins were identified across the CRC and LC secretome and EV samples. Of these, 22 and 44 protein markers were significantly elevated in the CRC and LC samples, respectively. Functional annotation revealed enrichment in proteins linked to metastasis and tumor progression for both cancer types. In EVs isolated from the plasma of patients with CRC, ITGB3, HSPA8, TUBA4A, and TLN1 were reduced, whereas FN1, SERPINA1, and CST3 were elevated, compared to healthy controls. These findings support the development of minimally invasive liquid biopsy methods for the detection, prognosis, and treatment monitoring of LC and CRC.

Colocalization of Cancer-Associated Biomarkers on Single Extracellular Vesicles for Early Detection of Cancer

Detection of cancer early, when it is most treatable, remains a significant challenge because of the lack of diagnostic methods sufficiently sensitive to detect nascent tumors. Early-stage tumors are small relative to their tissue of origin, heterogeneous, and infrequently manifest in clinical symptoms. The detection of early-stage tumors is challenging given the lack of tumor-specific indicators (ie, protein biomarkers, circulating tumor DNA) to enable detection using a noninvasive diagnostic assay. To overcome these obstacles, we have developed a liquid biopsy assay that interrogates circulating extracellular vesicles (EVs) to detect tumor-specific biomarkers colocalized on the surface of individual EVs. We demonstrate the technical feasibility of this approach in human cancer cell line-derived EVs, where we show strong correlations between assay signal and cell line gene/protein expression for the ovarian cancer-associated biomarkers bone marrow stromal antigen-2, folate receptor-α, and mucin-1. Furthermore, we demonstrate that detecting distinct colocalized biomarkers on the surface of EVs significantly improves discrimination performance relative to single biomarker measurements. Using this approach, we observe promising discrimination of high-grade serous ovarian cancer versus benign ovarian masses and healthy women in a proof-of-concept clinical study.

Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result

Background: Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures. Consequently, there is a critical need for advanced diagnostics after an initial low-dose computed tomography (LDCT) scan. Objectives: This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. Methods: A cost offset model was developed to evaluate the net expected savings associated with the use of CyPath® Lung relative to the current standard of care for individuals whose IPNs range from 6 to 29 mm. Perspectives from both Medicare and private payers in a US setting are included, with a 1-year time horizon. Cost calculations included procedure expenses, complication costs, and diagnostic assessment costs per patient. Primary outcomes of this analysis include cost savings per cohort and cost savings per patient. Results: Our analysis showed positive cost savings from a private payer's perspective, with expected savings of 895   202   311 p e r c o h o r t a n d 6460 per patient, across all patients. Scenario analysis resulted in cost savings of 890   829   889 p e r c o h o r t , a n d 6429 per patient. Similarly, savings of 378   689   020 p e r c o h o r t o r 2733 per patient were yielded for Medicare payers, across all patients. In addition, scenario analysis accounting for false negative patients from a Medicare payer perspective yielded savings of 376   902   203 p e r c o h o r t a n d 2720 per patient. Discussion: The results suggest substantial cost savings, primarily due to reductions in follow-up diagnostic assessments and procedures, and highlight the importance of accurate diagnostic tools in reducing unnecessary healthcare expenditures. Conclusion: CyPath® Lung utilization yields savings for private and Medicare payers relative to the current standard of care in a US setting for individuals with 6 to 20 mm IPNs.

Cost-Effectiveness of Lung Cancer Screening with Low-Dose Computed Tomography: Comparing Hungarian Screening Protocols with the US NLST

We aimed to directly compare the cost-effectiveness of Hungarian (following the NELSON trial) and NLST screening protocols, two trials influencing lung-cancer-screening implementation internationally. A decision-analytic model analyzing the cost-effectiveness of Hungarian protocols was manipulated to reflect the protocols of the NLST, while maintaining features specific to the Hungarian healthcare setting. In the Hungarian protocol, there are three possible outcomes to the initial round of screening, positive, negative, and indeterminate, indicating an uncertain degree of suspicion for lung cancer. This protocol differs from the NLST, in which the only possible screening outcomes are positive or negative, with no indeterminate option. The NLST pathway for smokers aged 55-74 resulted in a EUR 43 increase in the total average lifetime costs compared to the Hungarian screening pathway and resulted in a lifetime gain of 0.006 QALYs. The incremental costs and QALYs yielded an ICER of 7875 EUR/QALY. Our results demonstrate that assigning any suspicious LDCT screen as a positive result (NLST protocol) rather than indeterminate (Hungarian protocol) can reduce patient uncertainty and yield a slight QALY gain that is worth the additional use of resources according to Hungary's willingness-to-pay threshold. A stratified analysis by age was also conducted, revealing decreasing cost-effectiveness when screening older cohorts. Our study provides insight into the cost-effectiveness, advantages, and disadvantages of various LDCT screening protocols for lung cancer and can assist other countries as they implement their screening programs.

Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach

Purpose

Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs.

Approach

In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing.

Results

The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a p -value of 0.0054 for precision and a p -value of 0.00034 for specificity.

Conclusions

In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Cell-free DNA methylation in the clinical management of lung cancer

The clinical use of cell-free DNA (cfDNA) methylation in managing lung cancer depends on its ability to differentiate between malignant and healthy cells, assign methylation changes to specific tissue sources, and elucidate opportunities for targeted therapy. From a technical standpoint, cfDNA methylation analysis is primed as a potential clinical tool for lung cancer screening, early diagnosis, prognostication, and treatment, pending the outcome of elaborate validation studies. Here, we discuss the current state of the art in cfDNA methylation analysis, examine the unique features and limitations of these new methods in a clinical context, propose two models for applying cfDNA methylation data for lung cancer screening, and discuss future research directions.

Global respiratory health priorities at the beginning of the 21st century

Respiratory health has become a prevailing priority amid the diverse global health challenges that the 21st century brings, due to its substantial impact on individuals and communities on a global scale. Due to rapid advances in medicine, emerging knowledge gaps appear along with new challenges and ethical considerations. While breakthroughs in medical science can bring about encouraging possibilities for better treatments and interventions, they also lead to unanswered questions and areas where further research is warranted. A PubMed search on the topic "global respiratory health priorities" between the years 2000 and 2023 was conducted, which returned 236 articles. Of these, 55 were relevant and selected for inclusion in this article. The selection process took into account literature reviews, opinions from expert groups and careful analysis of existing gaps and challenges within the field; our selection encompasses specific infectious and noninfectious respiratory conditions in both adults and children. The global respiratory health priorities identified were selected on the basis that they have been recognised as critical areas of investigation and potential advancement and they span across clinical, translational, epidemiological and population health domains. Implementing these priorities will require a commitment to fostering collaboration and knowledge-sharing among experts in different fields with the ultimate aim to improve respiratory health outcomes for individuals and communities alike.

Performance of Lung-RADS in different target populations: a systematic review and meta-analysis

OBJECTIVES

Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations.

METHODS

We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses.

RESULTS

A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90-0.99) and pooled specificity was 0.90 (95% CI: 0.87-0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92-0.99] vs. 0.84 [95% CI: 0.50-0.96]) and lower specificity (0.87 [95% CI: 0.85-0.88] vs. 0.95 (95% CI: 0.92-0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91-0.99] vs. 0.91 [95% CI: 0.67-0.98]) and lower specificity (0.88 [95% CI: 0.85-0.90] vs. 0.93 [95% CI: 0.88-0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83-0.96) and specificity was 0.81 (95% CI: 0.67-0.90).

CONCLUSION

Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence.

CLINICAL RELEVANCE STATEMENT

Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence.

KEY POINTS

• High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0. • In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies. • There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.

Rates of Downstream Procedures and Complications Associated With Lung Cancer Screening in Routine Clinical Practice : A Retrospective Cohort Study

BACKGROUND

Lung cancer screening (LCS) using low-dose computed tomography (LDCT) reduces lung cancer mortality but can lead to downstream procedures, complications, and other potential harms. Estimates of these events outside NLST (National Lung Screening Trial) have been variable and lacked evaluation by screening result, which allows more direct comparison with trials.

OBJECTIVE

To identify rates of downstream procedures and complications associated with LCS.

DESIGN

Retrospective cohort study.

SETTING

5 U.S. health care systems.

PATIENTS

Individuals who completed a baseline LDCT scan for LCS between 2014 and 2018.

MEASUREMENTS

Outcomes included downstream imaging, invasive diagnostic procedures, and procedural complications. For each, absolute rates were calculated overall and stratified by screening result and by lung cancer detection, and positive and negative predictive values were calculated.

RESULTS

Among the 9266 screened patients, 1472 (15.9%) had a baseline LDCT scan showing abnormalities, of whom 140 (9.5%) were diagnosed with lung cancer within 12 months (positive predictive value, 9.5% [95% CI, 8.0% to 11.0%]; negative predictive value, 99.8% [CI, 99.7% to 99.9%]; sensitivity, 92.7% [CI, 88.6% to 96.9%]; specificity, 84.4% [CI, 83.7% to 85.2%]). Absolute rates of downstream imaging and invasive procedures in screened patients were 31.9% and 2.8%, respectively. In patients undergoing invasive procedures after abnormal findings, complication rates were substantially higher than those in NLST (30.6% vs. 17.7% for any complication; 20.6% vs. 9.4% for major complications).

LIMITATION

Assessment of outcomes was retrospective and was based on procedural coding.

CONCLUSION

The results indicate substantially higher rates of downstream procedures and complications associated with LCS in practice than observed in NLST. Diagnostic management likely needs to be assessed and improved to ensure that screening benefits outweigh potential harms.

PRIMARY FUNDING SOURCE

National Cancer Institute and Gordon and Betty Moore Foundation.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification

BACKGROUND

Radiomics, defined as quantitative features extracted from images, provide a non-invasive means of assessing malignant versus benign pulmonary nodules. In this study, we evaluate the consistency with which perinodular radiomics extracted from low-dose computed tomography images serve to identify malignant pulmonary nodules.

MATERIALS AND METHODS

Using the National Lung Screening Trial (NLST), we selected individuals with pulmonary nodules between 4mm to 20mm in diameter. Nodules were segmented to generate four distinct datasets; 1) a Tumor dataset containing tumor-specific features, 2) a 10 mm Band dataset containing parenchymal features between the segmented nodule boundary and 10mm out from the boundary, 3) a 15mm Band dataset, and 4) a Tumor Size dataset containing the maximum nodule diameter. Models to predict malignancy were constructed using support-vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) approaches. Ten-fold cross validation with 10 repetitions per fold was used to evaluate the performance of each approach applied to each dataset.

RESULTS

With respect to the RF, the Tumor, 10mm Band, and 15mm Band datasets achieved areas under the receiver-operator curve (AUC) of 84.44%, 84.09%, and 81.57%, respectively. Significant differences in performance were observed between the Tumor and 15mm Band datasets (adj. p-value <0.001). However, when combining tumor-specific features with perinodular features, the 10mm Band + Tumor and 15mm Band + Tumor datasets (AUC 87.87% and 86.75%, respectively) performed significantly better than the Tumor Size dataset (66.76%) or the Tumor dataset. Similarly, the AUCs from the SVM and LASSO were 84.71% and 88.91%, respectively, for the 10mm Band + Tumor.

CONCLUSIONS

The combined 10mm Band + Tumor dataset improved the differentiation between benign and malignant lung nodules compared to the Tumor datasets across all methodologies. This demonstrates that parenchymal features capture novel diagnostic information beyond that present in the nodule itself. (data agreement: NLST-163).

Development of a Novel Circulating Autoantibody Biomarker Panel for the Identification of Patients with 'Actionable' Pulmonary Nodules

Due to poor compliance and uptake of LDCT screening among high-risk populations, lung cancer is often diagnosed in advanced stages where treatment is rarely curative. Based upon the American College of Radiology's Lung Imaging and Reporting Data System (Lung-RADS) 80-90% of patients screened will have clinically "non-actionable" nodules (Lung-RADS 1 or 2), and those harboring larger, clinically "actionable" nodules (Lung-RADS 3 or 4) have a significantly greater risk of lung cancer. The development of a companion diagnostic method capable of identifying patients likely to have a clinically actionable nodule identified during LDCT is anticipated to improve accessibility and uptake of the paradigm and improve early detection rates. Using protein microarrays, we identified 501 circulating targets with differential immunoreactivities against cohorts characterized as possessing either actionable (n = 42) or non-actionable (n = 20) solid pulmonary nodules, per Lung-RADS guidelines. Quantitative assays were assembled on the Luminex platform for the 26 most promising targets. These assays were used to measure serum autoantibody levels in 841 patients, consisting of benign (BN; n = 101), early-stage non-small cell lung cancer (NSCLC; n = 245), other early-stage malignancies within the lung (n = 29), and individuals meeting United States Preventative Screening Task Force (USPSTF) screening inclusion criteria with both actionable (n = 87) and non-actionable radiologic findings (n = 379). These 841 patients were randomly split into three cohorts: Training, Validation 1, and Validation 2. Of the 26 candidate biomarkers tested, 17 differentiated patients with actionable nodules from those with non-actionable nodules. A random forest model consisting of six autoantibody (Annexin 2, DCD, MID1IP1, PNMA1, TAF10, ZNF696) biomarkers was developed to optimize our classification performance; it possessed a positive predictive value (PPV) of 61.4%/61.0% and negative predictive value (NPV) of 95.7%/83.9% against Validation cohorts 1 and 2, respectively. This panel may improve patient selection methods for lung cancer screening, serving to greatly reduce the futile screening rate while also improving accessibility to the paradigm for underserved populations.

Liquid biopsies based on DNA methylation as biomarkers for the detection and prognosis of lung cancer

Lung cancer (LC) is the main cause of cancer-related mortality. Most LC patients are diagnosed in an advanced stage when the symptoms are obvious, and the prognosis is quite poor. Although low-dose computed tomography (LDCT) is a routine clinical examination for early detection of LC, the false-positive rate is over 90%. As one of the intensely studied epigenetic modifications, DNA methylation plays a key role in various diseases, including cancer and other diseases. Hypermethylation in tumor suppressor genes or hypomethylation in oncogenes is an important event in tumorigenesis. Remarkably, DNA methylation usually occurs in the very early stage of malignant tumors. Thus, DNA methylation analysis may provide some useful information about the early detection of LC. In recent years, liquid biopsy has developed rapidly. Liquid biopsy can detect and monitor both primary and metastatic malignant tumors and can reflect tumor heterogeneity. Moreover, it is a minimally invasive procedure, and it causes less pain for patients. This review summarized various liquid biopsies based on DNA methylation for LC. At first, we briefly discussed some emerging technologies for DNA methylation analysis. Subsequently, we outlined cell-free DNA (cfDNA), sputum, bronchoalveolar lavage fluid, bronchial aspirates, and bronchial washings DNA methylation-based liquid biopsy for the early detection of LC. Finally, the prognostic value of DNA methylation in cfDNA and sputum and the diagnostic value of other DNA methylation-based liquid biopsies for LC were also analyzed.

Assessment of Uptake Appropriateness of Computed Tomography for Lung Cancer Screening According to Patients Meeting Eligibility Criteria of the US Preventive Services Task Force

Importance

Currently, computed tomography (CT) is used for lung cancer screening (LCS) among populations with various levels of compliance to the eligibility criteria from the US Preventive Services Task Force (USPSTF) recommendations and may represent suboptimal allocation of health care resources.

Objective

To evaluate the appropriateness of CT LCS according to the USPSTF eligibility criteria.

Design, Setting, and Participants

This cross-sectional study used the 2019 Behavioral Risk Factor Surveillance System (BRFSS) survey. Participants included individuals who responded to the LCS module administered in 20 states and had valid answers to questions regarding screening and smoking history. Data were analyzed between October 2021 and August 2022.

Exposures

Screening eligibility groups were categorized according to the USPSTF 2013 recommendations, and subgroups of individuals who underwent LCS were analyzed.

Main Outcomes and Measures

Main outcomes included LCS among the screening-eligible population and the proportions of the screened populations according to compliance categories established from the USPSTF 2013 and 2021 recommendations. In addition, the association between respondents' characteristics and LCS was evaluated for the subgroup who were screened despite not meeting any of the 3 USPSTF screening criteria: age, pack-year, and years since quitting smoking.

Results

A total of 96 097 respondents were identified for the full study cohort, and 2 subgroups were constructed: (1) 3374 respondents who reported having a CT or computerized axial tomography to check for lung cancer and (2) 33 809 respondents who did not meet any screening eligibility criteria. The proportion of participants who were under 50 years old was 53.1%; between 50 and 54, 9.1%; between 55 and 79, 33.8%; and over 80, 4.0%. A total of 51 536 (50.9%) of the participants were female. According to the USPSTF 2013 recommendation, 807 (12.8%) of the screening-eligible population underwent LCS. Among those who were screened, only 807 (20.9%) met all 3 screening eligibility criteria, whereas 538 (20.1%) failed to meet any criteria. Among respondents in subgroup 2, being of older age and having a history of stroke, chronic obstructive pulmonary disease, kidney disease, or diabetes were associated with higher likelihood of LCS.

Conclusions and Relevance

In this cross-sectional study of the BRFSS 2019 survey, the low uptake rate among screening-eligible patients undermined the goal of LCS of early detection. Suboptimal screening patterns could increase health system costs and add financial stress, psychological burden, and physical harms to low-risk patients, while failing to provide high-quality preventive services to individuals at high risk of lung cancer.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study

BACKGROUND

Although lung cancer screening (LCS) has been proven effective in reducing lung cancer mortality, it is associated with some potential harms, such as false positives and invasive follow-up procedures. Determining the time to next screen based on individual risk could reduce harms while maintaining health gains. Here, we evaluate the benefits and harms of LCS strategies with adaptive schedules, and compare these with those from non-adaptive strategies.

METHODS

We extended the Lee and Zelen risk threshold method to select screening schedules based on individual's lung cancer risk and life expectancy (adaptive schedules). We compared the health benefits and harms of these adaptive schedules with regular (non-adaptive) schedules (annual, biennial and triennial) using a validated lung cancer microsimulation model. Outcomes include lung cancer deaths (LCD) averted, life years gained (LYG), discounted quality adjusted life years (QALYs) gained, and false positives per LCD averted. We also explored the impact of varying screening-related disutilities.

RESULTS

In comparison to standard regular screening recommendations, risk-dependent adaptive screening reduced screening harms while maintaining a similar level of health benefits. The net gains and the balance of benefits and harms from LCS with efficient adaptive schedules were improved compared to those from regular screening, especially when the screening-related disutilities are high.

CONCLUSIONS

Adaptive screening schedules can reduce the associated harms of screening while maintaining its associated lung cancer mortality reductions and years of life gained. Our study identifies individually tailored schedules that optimize the screening benefit/harm trade-offs.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

Controversies and challenges in lung cancer screening

Two large randomized controlled trials have shown mortality benefit from lung cancer screening (LCS) in high-risk groups. Updated guidelines by the United State Preventative Service Task Force in 2020 will allow for inclusion of more patients who are at high risk of developing lung cancer and benefit from screening. As medical clinics and lung cancer screening programs around the country continue to work on perfecting the LCS workflow, it is important to understand some controversial issues surrounding LCS that should be addressed. In this article, we identify some of these issues, including false positive rates of low-dose CT, over-diagnosis, cost expenditure, LCS disparities in minorities, and utility of biomarkers. We hope to provide clarity, potential solutions, and future directions on how to address these controversies.

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Background and Objective

Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases.

Methods

we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques.

Key Content and Findings

DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers.

Conclusions

DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness.

Tertiary lymphoid structure and decreased CD8+ T cell infiltration in minimally invasive adenocarcinoma

Knowledge of the tumor microenvironment (TME) in patients with early lung cancer, especially in comparison with the matched adjacent tissues, remains lacking. To characterize TME of early-stage lung adenocarcinoma, we performed RNA-seq profiling on 58 pairs of minimally invasive adenocarcinoma (MIA) tumors and matched adjacent normal tissues. MIA tumors exhibited an adaptive TME characterized by high CD4+ T cell infiltration, high B-cell activation, and low CD8+ T cell infiltration. The high expression of markers for B cells, activated CD4+ T cells, and follicular helper T (Tfh) cells in bulk MIA samples and three independent single-cell RNA-seq datasets implied tertiary lymphoid structures (TLS) formation. Multiplex immunohistochemistry staining validated TLS formation and revealed an enrichment of follicular regulatory T cells (Tfr) in TLS follicles, which may explain the lower CD8+ T cell infiltration and attenuated anti-tumor immunity in MIA. Our study demonstrates how integrating transcriptome and pathology characterize TME and elucidate potential mechanisms of tumor immune evasion.

Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment

PURPOSE

To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics.

METHODS

A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCOm2012) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera.

RESULTS

The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCOm2012 model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCOm2012 model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCOm2012 model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria.

CONCLUSION

A blood-based biomarker panel in combination with PLCOm2012 significantly improves lung cancer risk assessment for lung cancer screening.

Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project

OBJECTIVES

To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers.

METHODS

We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold.

RESULTS

Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average transverse (0.980) and effective diameters (0.981) showed similar AUCs (p = .739). Elevating the average transverse diameter threshold from 6 to 9 mm resulted in a significantly increased specificity (91.7 to 96.7%, p < .001), a modest reduction in sensitivity (96.2 to 94.2%, p = .317), a 60.2% estimated reduction of unnecessary follow-up LDCTs, and a diagnostic delay in 1.9% of lung cancers. Elevating the threshold to 10 mm led to a significant reduction in sensitivity (86.5%, p = .025).

CONCLUSIONS

Elevating the diameter threshold for solid nodules from 6 to 9 mm may lead to a substantial reduction in unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers.

KEY POINTS

• Elevation of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.

Verification of a Blood-Based Targeted Proteomics Signature for Malignant Pleural Mesothelioma

BACKGROUND

We have verified a mass spectrometry (MS)-based targeted proteomics signature for the detection of malignant pleural mesothelioma (MPM) from the blood.

METHODS

A seven-peptide biomarker MPM signature by targeted proteomics in serum was identified in a previous independent study. Here, we have verified the predictive accuracy of a reduced version of that signature, now composed of six-peptide biomarkers. We have applied liquid chromatography-selected reaction monitoring (LC-SRM), also known as multiple-reaction monitoring (MRM), for the investigation of 402 serum samples from 213 patients with MPM and 189 cancer-free asbestos-exposed donors from the United States, Australia, and Europe.

RESULTS

Each of the biomarkers composing the signature was independently informative, with no apparent functional or physical relation to each other. The multiplexing possibility offered by MS proteomics allowed their integration into a single signature with a higher discriminating capacity than that of the single biomarkers alone. The strategy allowed in this way to increase their potential utility for clinical decisions. The signature discriminated patients with MPM and asbestos-exposed donors with AUC of 0.738. For early-stage MPM, AUC was 0.765. This signature was also prognostic, and Kaplan-Meier analysis showed a significant difference between high- and low-risk groups with an HR of 1.659 (95% CI, 1.075-2.562; P = 0.021).

CONCLUSIONS

Targeted proteomics allowed the development of a multianalyte signature with diagnostic and prognostic potential for MPM from the blood.

IMPACT

The proteomic signature represents an additional diagnostic approach for informing clinical decisions for patients at risk for MPM.

Proteomic Approach for Searching for Universal, Tissue-Specific, and Line-Specific Markers of Extracellular Vesicles in Lung and Colorectal Adenocarcinoma Cell Lines

Tumor-derived extracellular vesicles (EVs), including exosomes, contain proteins that mirror the molecular landscape of producer cells. Being potentially detectible in biological fluids, EVs are of great interest for the screening of cancer biomarkers. To reveal universal, tissue-specific, and line-specific markers, we performed label-free mass spectrometric profiling of EVs originating from the human colon cancer cell lines Caco-2, HT29, and HCT-116, as well as from the lung cancer cell lines NCI-H23 and A549. A total of 651 proteins was identified in the EV samples using at least two peptides. These proteins were highly enriched in exosome markers. We found 11 universal, eight tissue-specific, and 29 line-specific markers, the levels of which were increased in EVs compared to the whole lysates. The EV proteins were involved in the EGFR, Rap1, integrin, and microRNA signaling associated with metastasis and cancer progression. An EV protein-based assay could be developed as a liquid biopsy tool.

Lung Cancer CT Screening and Lung-RADS in a Tuberculosis-endemic Country: The Korean Lung Cancer Screening Project (K-LUCAS)

Background Low-dose CT screening for lung cancer in a tuberculosis-endemic country may be less effective because of false-positive results caused by tuberculosis sequelae. Purpose To evaluate the impact of tuberculosis sequelae at CT screening according to the American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) using data from the Korean Lung Cancer Screening Project (K-LUCAS). Materials and Methods This is a secondary analysis of K-LUCAS (ClinicalTrials.gov identifier NCT03394703), a nationwide Asian population-based, multicenter, prospective cohort study. Participants at high risk for lung cancer were enrolled between April 2017 and December 2018. Associations of tuberculosis sequelae with a positive screening result for lung cancer (defined as Lung-RADS categories 3 or 4) and diagnosis of lung cancer were analyzed with multivariable logistic regression. The diagnostic performance of Lung-RADS in predicting lung cancer was compared between participants with and participants without tuberculosis sequelae by using the χ² test. Results A total of 11 394 participants (median age, 62 years; interquartile range, 58-67 years; 11 098 men) were evaluated. Positive screening results were found in 1868 of the 11 394 participants (16%); lung cancer was diagnosed in 65 of the 11 394 participants (0.6%). Tuberculosis sequelae were identified in 1509 of the 11 394 participants (13%) on the basis of CT scans. Tuberculosis sequelae were associated with positive CT screening results (odds ratio [OR] with one nodule, 1.22; 95% confidence interval [CI]: 1.02, 1.45; P = .03), but no evidence was found of an association with lung cancer (OR, 0.9; 95% CI: 0.4, 1.6; P = .64). Specificity of Lung-RADS was higher for participants without tuberculosis sequelae (85% [8327 of 9829 participants]; 95% CI: 84.0%, 85.4%) than for those with tuberculosis sequelae (80% [1198 of 1500 participants]; 95% CI: 77.7%, 82%; P < .001). Sensitivity was not different between participants with tuberculosis sequelae (100% [nine of nine participants]; 95% CI: 62.9%, 100%) and those without tuberculosis sequelae (98% [55 of 56 participants]; 95% CI: 89.2%, 99.9%; P > .99). Conclusion In an at-risk population, tuberculosis sequelae resulted in a reduced specificity of CT screening for lung cancer using the Lung CT Screening Reporting and Data System. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ketai in this issue.

MicroRNA Biomarker hsa-miR-195-5p for Detecting the Risk of Lung Cancer

Background

Lung cancer is one of the leading diagnosed cancers worldwide, and microRNAs could be used as biomarkers to diagnose lung cancer. hsa-miR-195 has been demonstrated to affect the prognosis of NSCLC (non-small-cell lung cancer) in a previous study. However, the diagnostic value of hsa-miR-195-5p in lung cancer has not been investigated.

Methods

To evaluate the ability of hsa-miR-195-5p to diagnose lung cancer, we compared the expression of hsa-miR-195-5p in lung cancer patients, COPD patients, and normal controls. Receiver operating characteristic (ROC) curve analysis was performed to investigate the sensitivity and specificity of hsa-miR-195-5p. Coexpression network and pathway analysis were carried out to explore the mechanism.

Results

We found that hsa-miR-195-5p had lower expression in lung cancer and COPD patients than in normal controls, and the AUC was 0.92 for diagnosing lung cancer. hsa-miR-143 correlated with hsa-miR-195-5p, and by combining these two microRNAs, the AUC was 0.97 for diagnosing lung cancer.

Conclusions

hsa-miR-195-5p may act as a biomarker that contributes to the diagnosis of lung cancer and the detection of its high-risk population.

Computed Tomography Scanning for Early Detection of Lung Cancer

Parallel and often unrelated developments in health care and technology have all been necessary to bring about early detection of lung cancer and the opportunity to decrease mortality from lung cancer through early detection of the disease by computed tomography. Lung cancer screening programs provide education for patients and clinicians, support smoking cessation as primary prevention for lung cancer, and facilitate health care for tobacco-associated diseases, including cardiovascular and chronic lung diseases. Guidelines for lung cancer screening will need to continue to evolve as additional risk factors and screening tests are developed. Data collection from lung cancer screening programs is vital to the further development of fiscally responsible guidelines to increase detection of lung cancer, which may include small groups with elevated risk for reasons other than tobacco exposure.

A volatile biomarker in breath predicts lung cancer and pulmonary nodules

BACKGROUND

previous studies have reported volatile organic compounds (VOCs) in the breath as apparent biomarkers of lung cancer. We tested the hypothesis that a robust breath VOC biomarker of lung cancer should also predict pulmonary nodules in chest CT images.

METHODS

Biomarker discovery study (unblinded): 301 subjects were screened for lung cancer with low dose chest CT (LDCT), and donated duplicate samples of alveolar breath for analysis with gas chromatography mass spectrometry (GC MS). Monte Carlo analysis of breath chromatograms revealed a mass ion as a biomarker that identified biopsy-proven lung cancer as well as suspicious pulmonary nodules on LDCT. The biomarker was termed Mass Abnormalities in Gaseous Ions with Imaging Correlates (MAGIIC). The chemical structure of MAGIIC was tentatively identified from the NIST library of mass spectra; the best-fit compounds included C4 and C5 alkane derivatives that were consistent with metabolic products of oxidative stress. Blinded validation of MAGIIC: the abundance of the MAGIIC biomarker was determined in a different group of 161 subjects undergoing screening with LDCT. They donated duplicate alveolar breath VOC samples that were analyzed at two independent laboratories. The study was blinded and monitored with Good Clinical Practice. The abundance of MAGIIC in breath predicted biopsy-proven lung cancer with 84% accuracy, sensitivity = 75.4% and specificity = 85.0%. MAGIIC also predicted pulmonary nodules in LDCT with 80.5% accuracy, sensitivity = 80.1% and specificity = 75.0%. Breath MAGIIC abundance was not significantly affected by tobacco smoking history.

CONCLUSIONS

in a blinded study, breath VOC MAGIIC accurately predicted lung cancer confirmed on a tissue biopsy, as well as suspicious pulmonary nodules observed on LDCT. MAGIIC may have been a product of oxidative stress and it could potentially be employed as an ancillary to LDCT to predict the likelihood that a pulmonary nodule is malignant.

Cancer overdiagnosis: a biological challenge and clinical dilemma

For cancer screening to be successful, it should primarily detect cancers with lethal potential or their precursors early, leading to therapy that reduces mortality and morbidity. Screening programmes have been successful for colon and cervical cancers, where subsequent surgical removal of precursor lesions has resulted in a reduction in cancer incidence and mortality. However, many types of cancer exhibit a range of heterogeneous behaviours and variable likelihoods of progression and death. Consequently, screening for some cancers may have minimal impact on mortality and may do more harm than good. Since the implementation of screening tests for certain cancers (for example, breast and prostate cancers), a spike in incidence of in situ and early-stage cancers has been observed, but a link to reduction in cancer-specific mortality has not been as clear. It is difficult to determine how many of these mortality reductions are due to screening and how many are due to improved treatments of tumours. In cancers with lower incidence but high mortality (for example, pancreatic cancer), screening has focused on high-risk populations, but challenges similar to those for general population screening remain, particularly with regard to finding lesions with difficult-to-characterize malignant potential (for example, intraductal papillary mucinous neoplasms). More sensitive screening methods are detecting smaller and smaller lesions, but this has not been accompanied by a comparable reduction in the incidence of invasive cancers. In this Opinion article, we focus on the contribution of screening in general and high-risk populations to overdiagnosis, the effects of overdiagnosis on patients and emerging strategies to reduce overdiagnosis of indolent cancers through an understanding of tumour heterogeneity, the biology of how cancers evolve and progress, the molecular and cellular features of early neoplasia and the dynamics of the interactions of early lesions with their surrounding tissue microenvironment.

Lung Cancer Screening: Use of Low-Dose Computed Tomography

INTRODUCTION

The prognosis of lung cancer (LC) correlates directly with the stage of the disease at the time of diagnosis.

MATERIAL AND METHODS

We performed low-dose CT (LDCT) in asymptomatic individuals ≥50years old, smokers or former smokers of ≥10 pack-years, with no history of cancer. We followed an evaluation algorithm, according to the size and morphology of the nodules. The appropriate treatment for the LC diagnosis was given and patients were followed up for 5years.

RESULTS

We studied 4,951 individuals (65.4% males) with an average age of 56.89±5.26years; 550 presented nodules. Of the 3,891 nodules detected, 692 (19.57%) were considered positive, and 38 tumors (36LC) were identified. In the annual follow-up, nodules were found in 224 subjects, 288 (7.91%) of which were positive (13LC). In 80%, the study was performed with LDCT, and biopsy was indicated in 5.8% (baseline) and in 7.6% (annual) of the positive nodules. Prevalence was 0.89 and incidence was 0.1%. The sensitivity, specificity, PPV and NPV in the baseline study were 92.31, 89.54, 6.55 and 99.93%, respectively, and in the annual study, they were 76.92, 95.7, 4.52 and 99.94%, respectively. A total of 52 tumors were detected (49LC), 25 (52.08%) in stageI. The 5-year overall survival rate for LC was 58.5% and cancer-specific survival was 67.1% (75.8% in surgical patients).

CONCLUSION

LDCT integrated into an elaborate nodule detection and evaluation program is a useful tool for diagnosing early-stage LC.

Lung cancer CT screening: Psychological responses in the presence and absence of pulmonary nodules

OBJECTIVES

To determine the psychological response (thoughts, perceptions and affect) to a diagnosis of pulmonary nodules following a novel antibody blood test and computed tomography (CT) scans within a UK population.

MATERIALS AND METHODS

This study was nested within a randomised controlled trial of a blood test (Early CDT®-Lung test), followed by a chest x-ray and serial CT-scanning of those with a positive blood test for early detection of lung cancer (ECLS Study). Trial participants with a positive Early CDT®-Lung test were invited to participate (n = 338) and those agreeing completed questionnaires assessing psychological outcomes at 1, 3 and 6 months following trial recruitment. Responses of individuals with pulmonary nodules on their first CT scan were compared to those without (classified as normal CT) at 3 and 6 months follow-up using random effects regression models to account for multiple observations per participant, with log_e transformation of data where modelling assumptions were not met.

RESULTS

There were no statistically significant differences between the nodule and normal CT groups in affect, lung cancer worry, health anxiety, illness perceptions, lung cancer risk perception or intrusive thoughts at 3 or 6 months post-recruitment. The nodule group had statistically significantly fewer avoidance symptoms compared to the normal CT group at 3 months (impact of events scale avoidance (IES-A) difference between means -1.99, 95%CI -4.18, 0.21) than at 6 months (IES-A difference between means 0.88, 95%CI -1.32, 3.08; p-value for change over time = 0.003) with similar findings using log_e transformed data.

CONCLUSION

A diagnosis of pulmonary nodules following an Early CDT®-Lung test and CT scan did not appear to result in adverse psychological responses compared to those with a normal CT scan.

Patient Perspectives on Low-Dose Computed Tomography for Lung Cancer Screening, New Mexico, 2014

INTRODUCTION

National guidelines call for annual lung cancer screening for high-risk smokers using low-dose computed tomography (LDCT). The objective of our study was to characterize patient knowledge and attitudes about lung cancer screening, smoking cessation, and shared decision making by patient and health care provider.

METHODS

We conducted semistructured qualitative interviews with patients with histories of heavy smoking who received care at a Federally Qualified Health Center (FQHC Clinic) and at a comprehensive cancer center-affiliated chest clinic (Chest Clinic) in Albuquerque, New Mexico. The interviews, conducted from February through September 2014, focused on perceptions about health screening, knowledge and attitudes about LDCT screening, and preferences regarding decision aids. We used a systematic iterative analytic process to identify preliminary and emergent themes and to create a coding structure.

RESULTS

We reached thematic saturation after 22 interviews (10 at the FQHC Clinic, 12 at the Chest Clinic). Most patients were unaware of LDCT screening for lung cancer but were receptive to the test. Some smokers said they would consider quitting smoking if their screening result were positive. Concerns regarding screening were cost, radiation exposure, and transportation issues. To support decision making, most patients said they preferred one-on-one discussions with a provider. They also valued decision support tools (print materials, videos), but raised concerns about readability and Internet access.

CONCLUSION

Implementing lung cancer screening in sociodemographically diverse populations poses significant challenges. The value of tobacco cessation counseling cannot be overemphasized. Effective interventions for shared decision making to undergo lung cancer screening will need the active engagement of health care providers and will require the use of accessible decision aids designed for people with low health literacy.