Current Lung Cancer Screening Research Articles

Comparative Analysis of Clinical Characteristics and Prognosis Between Primary Lung Cancers Identified as Screening Detected and Incidentally Detected Pulmonary Nodules.

Although the importance of low-dose computed tomography (LDCT) screening is increasingly emphasized and implemented, many lung cancers continue to be incidentally detected during routine medical practices, and data on incidentally detected lung cancer (IDLC) remain scarce. This study aimed to investigate the clinical characteristics and prognosis of IDLCs by comparing them with screening-detected lung cancers (SDLCs). In this retrospective study, subjects with cT1 (≤3 cm) pulmonary nodules detected on baseline computed tomography (CT), later pathologically confirmed as primary lung cancer in 2015, were included. Patients were categorized into IDLC and SDLC groups based on the setting of the first pulmonary nodule detection. Out of 457 subjects, 129 (28.2%) were IDLCs and 328 (71.8%) were SDLCs. The IDLC group, consisted of older individuals with a higher prevalence of smokers and underlying pulmonary disease, compared to the SDLC group. Adenocarcinomas were more frequently detected in SDLCs (87.5%) than in IDLCs (76.7%, p<0.001). The time to treatment initiation (TTI) and 5-year overall survival (OS) rates were similar. Multivariate analyses revealed underlying interstitial lung disease, DLCO, solidity of nodules and TNM stage as independent risk factors associated with mortality. Less than 30% of study participants would have been eligible for the current lung cancer screening program. The IDLC group was associated with older age, higher rate of smokers, underlying pulmonary disease, and non-adenocarcinoma histology. However, prognosis was similar to that of the SDLC group, attributable to the similarity in TNM stage, strict adherence to guidelines, and short TTI. Furthermore, less than 30% of the participants would have been suitable for the existing lung cancer screening program, indicating a potential need to reconsider the scope for screening candidates.

E-cigarette use and United States Preventive Services Task Force (USPSTF) lung cancer screening (LCS) eligibility.

10531 Background: Electronic cigarettes (E-cigarettes) have become frequently used as a smoking cessation tool. Emerging research has shown that e-cigarettes have similar carcinogenic effects as combustible cigarettes. While current lung cancer screening (LCS) guidelines primarily rely on individuals’ smoking history, it's becoming increasingly important to understand the prevalence of e-cigarette use among individuals, especially in the context of their LCS eligibility. Methods: Individuals aged 40-80 years were extracted from the 2022 Behavioral Risk Factor Surveillance System. Eligibility was defined using the 2021 USPSTF LCS criteria, i.e., aged 50-80 years who ever smoked, with at least a 20-pack-year smoking history, currently smoke or quit within the past 15 years. We compared the prevalence of current and ever e-cigarette use among LCS-eligible and non-eligible populations, overall and by smoking (combustible cigarettes) status using chi-square tests. All analyses were weighted. The significance level was set at a 2-sided p-value of &lt;0.05. Results: Among the 208,317 individuals included, 9.9% were eligible for LCS. Overall, 3.8% and 22.1% reported current and ever cigarette use, respectively. Fewer LCS-ineligible individuals reported currently using e-cigarettes than their LCS-eligible counterparts (Table). Among former combustible cigarette users, LCS-ineligible individuals were less likely to be current e-cigarette user than LCS-eligible individuals. However, among current combustible cigarette users, LCS-ineligible individuals were more likely to be current e-cigarettes users than LCS-eligible individuals. When examining the prevalence of ever e-cigarette use, similar findings were observed. Conclusions: Our study reveals a significant prevalence of e-cigarette usage among older adults (aged 40-80), with 1 in 5 individuals reporting ever having used e-cigarettes. Moreover, current cigarette users who were ineligible for LCS were more likely to use e-cigarette than their LCS-eligible counterparts. Future epidemiological studies are warranted to assess the risk of e-cigarette use (including intensity, duration and interactions with combustible cigarettes) and lung cancer risks. E-cigarette use may need to be considered in formulating future LCS guidelines. [Table: see text]

Development of a serum miRNA panel for detection of early stage non-small cell lung cancer.

3058 Background: Lung cancer remains the leading cause of cancer deaths, largely due to late-stage diagnosis. Early detection significantly improves survival rates, but current methods like chest X-rays and low-dose CT (LDCT) scans face limitations in sensitivity and specificity. MicroRNAs (miRNAs) are small non-coding RNAs whose dysregulation has been widely observed in different stages of cancer. Evidence suggests that circulating miRNAs could be promising biomarkers for early diagnosis of lung cancer. In this study, we developed and validated a 15-miRNA panel as a blood-based minimally invasive test for early detection of non-small cell lung cancer (NSCLC) using the proprietary multiplex quantitative real-time PCR (qPCR). Methods: 332 serum samples (182 Stage I&amp;II NSCLC, 150 healthy controls) were divided into training (n=266) and validation (n=66) cohorts. Differentially expressed serum miRNAs were screened with Miseq sequencing followed by qPCR validation. The multi-miR panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic curves were constructed to evaluate the diagnostic accuracy of the panel. A stem-loop quadruplex qPCR assay was invented for concurrent detection of 4 miRs in a single reaction. Results: 15 differentially expressed miRNAs were selected for logistic model construction based on their Ct values (Ct&lt;35) and high diagnostic accuracy of stage I&amp;II NSCLC (AUC&gt;0.7). The 15-miRNA panel model achieved 88.4% sensitivity, 81.7% specificity (AUC = 0.912) in the training cohort (Table), and was validated with 94.4% sensitivity, 73.3% specificity (AUC = 0.892) in the validation cohort. Quantification of 15 miRs and 1 internal control was performed in 4 reactions tubes via quadruplex qPCR assay. Conclusions: The serum 15-miR panel developed and validated in this retrospective study exhibited robust performance for detection of early stage NSCLC. The panel demonstrates potential diagnostic applications and clinical utility to be implemented together with LDCT in current lung cancer screening program as a blood biomarker to reduce false positive results. In addition, the proposed stem-loop quadruplex qPCR system offers an efficient and promising approach for miRNA profiling in future clinical applications. [Table: see text]

Participant adherence in a lung cancer screening (LCS) program in a non-smoking population.

10556 Background: LCS guidelines in smoking populations includes an annual low-dose CT Chest (LDCT) scan. Historically, real-world adherence to repeat imaging following the baseline LDCT has been low at 22% (Silvestri et al. Chest 2023). Various reasons for non-adherence have been cited and can be amplified in populations where English is not the primary language, low health literacy, or logistical reasons that interfere with scheduling. We are conducting the Female Asian Nonsmoker Screening Study (FANSS), which is a pilot study for Asian women with no smoking history to provide LDCTs to this population. We describe the strategies utilized to address potential barriers to adherence and report the participant (pt) adherence observed to date for this population. Methods: FANSS is an IRB-approved trial that provides LDCTs annually for 3 years and has an interim follow-up phone call every 6 months. All aspects of the study other than the LDCT could be conducted virtually pending pt preference. All pt interactions were conducted in the pt’s requested language. LDCTs were interpreted according to Lung-RADS. Review of the LDCT results were available in-person or virtually by a nurse practitioner or the principal investigator. The timeline for the 2nd and 3rd LDCT after the baseline scan was determined as follows: pts with negative findings were recommended for a repeat LDCT in 1 year; pts with suspicious findings were reviewed at the NYU Multidisciplinary Nodule Board and recommended for a repeated LDCT in a 3 or 6 month interval. A bilingual (English and Chinese speaking) research coordinator and medical secretaries reached out to pts at their preferred contact (email, phone, or text) for scheduling the follow-up LDCT. Up to three attempts to reach pts to schedule their follow-up LDCT were made. Results: From 3/5/21 to 1/27/24, 634 subjects completed their baseline LDCTs. Language interpretation services were utilized in 30% of pts. As of 1/27/24, 215 pts were due for their 2nd LDCT: 195 (91%) completed (186) or scheduled (9) the LDCT, 5 pts withdrew their consent, and 15 did not complete the scan. The main reasons for not completing the scans were: living outside New York City, other health concerns, and lost to follow-up. As of 1/27/24, 95 pts were due for their 3rd LDCT: 87 (92%) completed (82) and scheduled (5) the LDCT, and 8 did not complete the scan due to being lost to follow-up. Conclusions: The FANSS population is a unique pt cohort comprised of all Asian women who do not qualify for current LCS guidelines. Interventions to address potential adherence issues were implemented. In our research-based setting, virtual conduct of most of the study procedures allowed pt flexibility. Pt outreach to ensure follow-up by contacting through various methods also ensured adherence of &gt;90%. These interventions can be considered for use in other LCS programs to improve adherence to LCS follow-up.

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm)

Objectives. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy. Materials and Methods. Data were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China. The study includes patients with small pulmonary nodules (≤10 mm). We developed a neural network, termed MIFNN, that synergistically combines computed tomography (CT) images and morphological characteristics of pulmonary nodules. The network is designed to acquire clinically relevant deep learning features, thereby elevating the diagnostic accuracy of existing models. Importantly, the network’s simple architecture and use of standard screening variables enable seamless integration into standard lung cancer screening protocols. Results. In summary, the study analyzed a total of 382 small pulmonary nodules (85 malignant) from the LUNA16 dataset and 101 small pulmonary nodules (33 malignant) obtained from four specialized centers in Beijing, China, for model training and external validation. Both internal and external validation metrics indicate that the MIFNN significantly surpasses extant state-of-the-art models, achieving an internal area under the curve (AUC) of 0.890 (95% CI: 0.848–0.932) and an external AUC of 0.843 (95% CI: 0.784–0.891). Conclusion. The MIFNN model significantly enhances the diagnostic accuracy of small pulmonary nodules, outperforming existing benchmarks by Zhang et al with a 6.34% improvement for nodules less than 10 mm. Leveraging advanced integration techniques for imaging and clinical data, MIFNN increases the efficiency of lung cancer screenings and optimizes nodule management, potentially reducing false positives and unnecessary biopsies. Clinical relevance statement. The MIFNN enhances lung cancer screening efficiency and patient management for small pulmonary nodules, while seamlessly integrating into existing workflows due to its reliance on standard screening variables.

A Retrospective Analysis: Investigating Factors Linked to High Lung-RADS Scores in a Nonsmoking, Non-Family History Population.

Low-dose computed tomography screening for lung cancer is currently targeted at heavy smokers or those with a family history of lung cancer. This study aimed to identify risk factors for lung cancer in individuals who do not meet the current lung cancer screening criteria as stipulated by the Taiwan Health Promotion Agency's low-dose computed tomography (LDCT) screening policy. A cohort analysis was conducted on 12,542 asymptomatic healthy subjects aged 20-80 years old who voluntarily underwent LDCT scans from January 2016 to December 2021. Logistic regression demonstrated that several factors, including age over 55 years, female gender, a body mass index (BMI) less than 23, a previous history of respiratory diseases such as tuberculosis or obstructive respiratory diseases (chronic obstructive pulmonary disease [COPD], asthma), and previous respiratory symptoms such as cough or dyspnea, were associated with high-risk lung radiology scores according to LDCT scans. These findings indicate that risk-based assessments using primary data and questionnaires to identify risk factors other than heavy smoking and a family history of lung cancer may improve the efficiency of lung cancer screening.

Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers.

Personalized LCS facilitated by a risk model and/or a biomarker test is proposed as an alternative to existing programs. Acceptability of personalized approach among PCPs is unknown. The goal of this study is to assess the acceptability of personalized LCS among PCPs.

Cost effective quality improvement initiative in lung cancer screening: A single institutional pilot program.

458 Background: US Preventive Services Task Force (USPSTF) recommends lung cancer screening (LCS) with annual low-dose computed tomography (LDCT) screening for individuals aged 50-80 years with ≥20 pack-year smoking history. Nevertheless, only 4%-13% of eligible individuals are screened nationally. Lung cancer mortality is higher in the South where low socioeconomic status and racial minorities predominate and the screening rate in Alabama is between 4.7% - 7.7%. Thomas Hospital belongs to this catchment area and the goal of our study was to improve LCS with LDCT by embarking on a quality improvement initiative which was commenced as a pilot program for a larger healthcare system (&gt;5 large outpatient clinics with over 12,000 visits/month). Methods: Data were obtained retrospectively by reviewing the electronic medical records of eligible patients for LCS from 01/22 to 01/23 who visited the Thomas Hospital Internal Medicine Residency Clinic (pilot study site). A multidisciplinary team was created involving Thoracic oncologists, radiologists, Thoracic surgeons, resident physicians, and PCPs to identify barriers to LCS and discuss methods on how to improve LCS. We identified areas to be improved on and utilized performance improvement tools such as a Pareto chart and a PICK chart. Data was then collected prospectively. Results: In the initial 12-month period, only 6.95% (total n=185) of patients who qualified for LCS had LDCT done for LCS. The major barriers to screening were poor smoking history documentation, lack of proper identification of screening candidates, lack of awareness of screening guidelines, infrequent follow-up, discrepancies in smoking history within the EMR, and lack of a consistent system for LDCT referral. We identified 4 main initiatives which were introduced monthly, one at a time. The interventions include Educational services/Awareness campaigns on LCS, Distribution of current LCS guidelines in the clinic rooms/offices, Patient self-reported questionnaire on smoking history, and Implementation of an EPIC flag tool for LCS. We reviewed our performance after the interventions and the total LCS after 4 interventions included 48 (47%) patients compared to 102 patients who fit the screening criteria. 5 patients had LDCT ordered but yet to be completed and 6 patients had findings that needed immediate evaluation and received appropriate referrals. Conclusions: Lung cancer screening remains suboptimal in the United States, especially in the southern region. With our approach, the screening rates have increased exponentially only within a few months compared to the previous year and this model is to be adopted by other clinics affiliated with the umbrella health system. LDCT does carry certain risks including false positive results and unnecessary biopsies; hence shared decision-making is needed before ordering the test.

Biomarkers of lung cancer for screening and in never-smokers-a narrative review.

Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies. Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS). Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies. Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.

Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes

Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration. The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups. A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy. The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.

Lung cancer in nonsmokers- A risk factor analysis

Institutions:stony brook medical centerrationaleLung Cancer screening for the high-risk smoking population has been proven to save lives. However, in 2022, 20% of newly diagnosed lung cancers (47,300) were in nonsmokers. These patients were found to be diagnosed at later stages. This may be at least partly due to not meeting criteria for and participating in current lung cancer screening. This study aims to describe characteristics of a never smoker patient population to help identify common risk factors which might merit inclusion in lung cancer screening and thus improve patient outcomes. MethodsThis retrospective single center study included never-smoker patients diagnosed with lung nodules and never-smoker patients diagnosed with lung cancer from 2016 to 2022. Data was obtained from the Stony Brook Medical Center electronic medical record. 16,056 patients were identified as never-smokers who were asked by the medical assistant if they ever smoked in their lifetime. Patients were eliminated if they had any smoking history up to first diagnosis date. Demographics, radiology, histology, diagnosis dates, comorbidities, smoking status, and exposures collected through ICD10 codes and not self-reported, were investigated. ResultsOf 16,056 never-smoking patients, 9315 (58.02%) were females diagnosed with lung nodules and 6741 (41.98%) were males diagnosed with lung nodules.The univariate analysis showed significant differences between gender, age at nodule diagnosis, and patients with and without comorbidities including chronic obstructive pulmonary disease (COPD), hypertension (HTN), and family history (FHX) of lung cancer. The percentage of lung cancer patients among females was significantly higher than among males. Patients having lung cancer were older. The percentages of lung cancer patients with these comorbidities were significantly higher than those without. However, there was no significant difference found between patients with and without diabetes mellitus (DM).The multivariable logistic regression suggested that age at nodule diagnosis and comorbidities including COPD (which included asthma, emphysema and chronic bronchitis) and family history of lung cancer were significantly associated with lung cancer. Older patients and patients with those comorbidities had a higher risk of developing cancer than those who were younger or without those comorbidities. The study excluded HTN and included age at nodule diagnosis in the logistic regression model as HTN was found to be protective against lung cancer due to age at lung nodule diagnosis. Please refer to the appendix for further details. ConclusionNever-smoker patients who were older and with COPD and Family History of lung cancer had higher risk of developing lung cancer than younger patients without these comorbidities. In this study, gender had no impact on outcome.

Lung cancer screening status and acceptability of a personalized approach in Texas.

e22516 Background: Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its proven benefit, only 5-15% of eligible patients receive LCS, with Texas having one of the lowest rates among states in the US. Personalized screening strategies select people for LCS based on their personal lung cancer risk and may increase LCS’s effectiveness and efficiency. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. Methods: We surveyed 32,983 Texas-based PCPs including primary care physicians, physician assistants, and nurse practitioners on an existing network (Protocol 2019-1257; PI: Dr. Shete), and 400 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. Deidentified demographics, current LCS practices, and acceptability of personalized LCS were obtained. We analyzed the responses by subgroups of interest using comparative statistics. Using nonparametric bootstrap, we derived an enriched dataset and utilized it to develop logistic regression models to further understand respondent tendencies related to current LCS practices and acceptability of personalized LCS. Results: Response rates were 0.3% (n=91) and 15% (n=60) for the 2019-1257 and TAFP surveys, respectively. Respondent demographics are provided in the table. Most (84%) respondents regularly assess LCS in their practice, with those in the following categories more likely to assess for LCS eligibility: 40-49 years old, female, non-Black, physicians, with &lt;10 years in practice, seeing &gt;50 patients per week, and work at a non-residency training site (all p-values &lt; 0.05). Half of the respondents were interested in adopting personalized LCS while an additional 40% wanted to learn more information. Respondents in the following categories were associated with increased interest in personalized LCS: younger than 60 years, non-White, non-physicians, seeing 100 or more patients per week, at a non-residency training site, and at a decentralized practice (all p values &lt; 0.05). Majority (66%) of respondents expressed concerns regarding time availability with the personalized LCS. Conclusions: There is a great need to enhance the awareness and engagement of Texas-based PCPs about LCS. Most of the respondents expressed interest in personalized LCS. Time availability was the main concern related to personalized LCS. Findings from this project highlight the need for better education of Texas-based PCPs on the benefits of LCS, and the development of efficient decision tools to facilitate shared decision-making sessions to ensure successful implementation of personalized LCS. [Table: see text]

An AI tool for chest X-ray interpretation and lung cancer pre-screening in developing countries.

1548 Background: The current lung cancer screening strategy has not had a major impact on public health, facing many challenges during its implementation in developing countries. Although chest radiography (CXR) is not the study of choice for screening; it is one of the most accessible studies used at all levels of care, so implementing an AI tool capable of CXR interpretation as well as lung cancer risk assessment on a large scale could improve the selection of patients for lung cancer screening. The aim of our study was to develop and evaluate this tool. Methods: The dataset used for this tool development was obtained from the NIH "ChestX-ray14” public database. The set was divided into 3 subgroups for training, testing, and validation in a ratio of 80/10/10% respectively. We performed a structuration of metadata and an exploratory analysis as well as preprocessing of the images to standardize their size and resolution before the training. A model based on the adaptation of the momentum for learning was used as an optimizer of the CNN algorithm used. Additionally, a questionnaire based on the PLCO clinical criteria was included for the identification of patients at high risk of developing lung cancer. Results: Standard performance metrics were evaluated as shown. Conclusions: Our model is comparable to certified radiologists in accuracy at identifying patterns of a CXR. The use of clinical patient information improves the pre-selection of patients with a high risk of developing lung cancer. In developing countries, the implementation of this tool on a large scale will allow more patients to be screened and early diagnosed.[Table: see text]

Idiopathic pulmonary fibrosis and lung cancer: future directions and challenges.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease of pulmonary scarring. New treatments slow disease progression and allow pulmonary fibrosis patients to live longer. Persistent pulmonary fibrosis increases a patient's risk of developing lung cancer. Lung cancer in patients with IPF differs from cancers that develop in the non-fibrotic lung. Peripherally located adenocarcinoma is the most frequent cell type in smokers who develop lung cancer, while squamous cell carcinoma is the most frequent in pulmonary fibrosis. Increased fibroblast foci in IPF are associated with more aggressive cancer behaviour and shorter doubling times. Treatment of lung cancer in fibrosis is challenging because of the risk of inducing an exacerbation of fibrosis. In order to improve patient outcomes, modifications of current lung cancer screening guidelines in patients with pulmonary fibrosis will be necessary to avoid delays in treatment. 2-fluoro-2-deoxy-d-glucose (FDG) positron emission tomography (PET) computed tomography (CT) imaging can help identify cancer earlier and more reliably than CT alone. Increased use of wedge resections, proton therapy and immunotherapy may increase survival by decreasing the risk of exacerbation, but further research will be necessary.

The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.

Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.

Circulating nucleosomes to identify non-smoker subjects at risk of lung cancer and triage them for low-dose CT scan.

e20558 Background: Lung cancer (LC) is the leading cause of cancer death worldwide and the most frequently occurring in men and the second most diagnosed cancer in woman after breast cancer. The primary risk factor leading to LC is tobacco smoke. Nevertheless, several studies in the US, UK and Asia reported an increasing rate of LC in non-smokers. 15-20% of LC patients worldwide are non-smokers with this percentage rising to 60% in non-smoking women in Asia. Current LC screening guidelines recommend a CT scan only for high-risk smokers. Despite there being no guidelines to recommend LC screening in non-smokers those with increased risk factors could be monitored. Therefore, a blood-test could offer a simple way to identify non-smokers at high-risk of LC and triage them to go for low-dose CT scan. Methods: We measured circulating levels of nucleosomes and CEA in 722 non-smoker subjects referred for CT scan at National Taiwan University Hospital. The patients were later confirmed to have either a LC (n = 509, including 401 patients at stage 0-1), non-malignant nodules (n = 142), or no-nodules (n = 71). Whole blood was collected in EDTA plasma tubes for analysis by six different Nu.Q chemiluminescent magnetic microparticle immunoassays (Belgian Volition SRL, Belgium) targeting H3.1-nucleosomes or different histone modifications (H3K9Me3-, H3K27Me3-, H3K36Me3-, H3K9Ac-, H3K14Ac-nucleosomes). CEA was analyzed on the Abbott ARCHITECT analyzer. We defined by logistic regression the best combination of biomarkers to identify LC cases in this non-smoking population in a rule-in approach. Results: At 95% specificity, none of the individual biomarkers exceed 24.8% of sensitivity for LC vs non-malignant nodules and no nodules. A combination of 2 nucleosome biomarkers: H3K9Me3- and H3K9Ac-nucleosomes with CEA in a logistic regression model showed improved sensitivity detecting 32.4% of LC in the non-smoker subjects at 95% specificity for LC vs non-malignant nodules and no nodules (area under the curve (AUC): 0.76, R2: 19.1%). In a non-smoking women sub-group by adding a third nucleosome biomarker: H3K36Me3-nucleosomes to the model we could detect 34.3% of the women who have lung cancer at 95% specificity (AUC: 0.75; R2: 20.2%). Conclusions: Our results indicate that a blood test combining circulating nucleosomes targeting different epigenetic modifications with CEA allow the detection of LC cases in a non-smokers population. This blood test could be part of the screening criteria for the non-smoking population at risk to develop LC and could help to triage them for low-dose CT scan.

P10.08 Comparing Lung Cancer in Never Smokers and Ever Smokers in Asian or Asian American Patients Treated at a Tertiary Urban Public Hospital in New York

The higher incidence of targetable mutations in Asian patients with lung cancer clearly suggests potentially different risk factors and underlying biology. Most of the studies regarding lung cancer in Asian patients were conducted in Asia, but limited data is known about Asian or Asian American patients in the U.S. While current lung cancer screening guidelines only affect smoker populations, expanding screening to other high risk populations is of interest. The purpose of this study is to assess various characteristics of Asian or Asian American lung cancer patients at a public hospital in New York.

Risk Factors for Lung Cancer in an Underrepresented Safety-Net Screening Cohort

Introduction/BackgroundThe USPSTF (United States Preventive Services Task Force) guidelines suggest criteria centering on smoking status and age to select patients for lung cancer screening. Despite the significant advances in screening with low-dose computed tomography (LDCT), cancer detection rate is low (1.1%), highlighting the need to investigate possible ways to refine the current lung cancer screening strategy. Our aim was to determine clinical risk factors predictive of lung cancer in an urban safety-net hospital. Materials and MethodsWe performed a retrospective chart review of 2847 patients who received LDCT screening for lung cancer between 3/1/2015 and 12/31/2019. Patient demographics and medical history were collected. A bivariate logistic regression was used to evaluate predictors of lung cancer. ResultsCompared to the National Lung Cancer Screening Trial (NLST) population, our screening cohort had significantly more African Americans (38.2% vs. 4.5%, P < .0001), more obesity (32.7% vs. 28.3%, P < .0001), and higher rates of chronic obstructive pulmonary disease (COPD) (45.9% vs. 5.0%, P < .0001). The strongest predictors of lung cancer were COPD (odds ratio [OR] = 2.14, P < .0001) and a family history of lung cancer (OR = 2.77, P < .0001). Age (OR = 1.04, P< .001) and pack years (OR = 1.01, P< .001) were less predictive. ConclusionA diagnosis of COPD and family history of lung cancer were most predictive of lung cancer in a screening cohort at our urban safety-net hospital. Future studies should focus on whether inclusion of these additional risk-factors improves proportion of lung cancer detected via screening.

Imaging Administrators: The Overlooked Barrier to Lung Cancer Screening Implementation

Imaging Administrators: The Overlooked Barrier to Lung Cancer Screening Implementation

Contemporary Lung Cancer Screening and the Promise of Blood-Based Biomarkers.

In this issue, the study by Dagnino and colleagues represents an important addition to the maturing field of blood-based biomarkers for lung cancer screening. Their comprehensive approach to analyzing circulating inflammatory proteins identified CDCP1 as a potential biomarker for distinguishing patients with or without lung cancer, a finding that was confirmed in a validation cohort. CDCP1 blood levels, when combined with smoking history, gave an AUC receiver operator characteristic of 0.75. Analysis of transcripts in peripheral blood cells suggested a Wnt/β-catenin signaling-based mechanism for CDCP1 in tumorigenesis providing biologic plausibility. CDCP1 now joins the ranks of other potential blood-based lung cancer screening biomarkers (including epithelial tumor marker proteins, tumor-associated miRNA, antitumor antibodies, and tumor-specific DNA methylation) that need validation in future clinical trials. Further exploration of how CDCP1 levels might be integrated into current lung cancer screening programs, including both detection of lung cancer, and evaluation of the need for invasive biopsies, as well as how CDCP1 performs in different racial populations, is warranted.See related article by Dagnino et al., p. 3738.