Lung Cancer Screening Program Research Articles

Improving LDCT screening rates within a large multidisciplinary community based practice using integrated ACR accredited imaging centers.

e13582 Background: Lung cancer remains the leading cause of cancer-related death in the US, accounting for about 22% of such deaths. Lung cancer detected by low-dose computed tomography (LDCT) screening is discovered at an early stage 64-85% of the time. The benefits of LDCT screening have been demonstrated in numerous trials, but there has been difficulty in establishing programs that screen a high percentage of eligible patients. We present data from the first 18 months of our lung cancer screening program that is designed to provide the highest quality screening, follow-up and access to prevention, diagnosis, and treatment including clinical trials within their local communities. Methods: New York Cancer and Blood Specialists (NYCBS) is a community practice with over 50 locations across NY. While most lung cancer screening programs are hospital-based, NYCBS has a series of American College of Radiology (ACR) accredited imaging centers that perform these screenings and are closely affiliated with oncology, primary care, Ob-Gyn, nephrology, urology and other specialty medical and surgical practices. These centers are strategically located in areas that have high rates of tobacco use as suggested by heat map data from the NY City Community Health Survey . Upon initial patient presentation, all NYCBS and affiliated sites screen patients with tablet-based questionnaires that prompt additional questions during follow-up appointments. Patients with positive findings are discussed during a monthly, system-wide multidisciplinary lung nodule review conference. Follow-up interventions include referrals to pulmonology, interventional radiology, or further imaging with PET/CT. Per ACR guidelines, patients not requiring acute intervention continue with follow-up CT. Results: In 2023, 29,501 patients were screened with an in-office questionnaire. Of those patients, 3,606 were eligible for screening, 225 declined, and 896 (29%) high risk patients proceeded to undergo LDCT screening. In screened patients, 128 underwent follow-up CT, 36 patients had findings requiring immediate intervention, and 6 were diagnosed with lung cancer. 5 of these patients were diagnosed with stage I and 1 patient was diagnosed with stage IV lung cancer. Conclusions: LDCT screening is a proven method to discover early stage lung cancer and improve the survival rate of diagnosed patients but has not been well adopted in practice. With access to a diverse array of multidisciplinary practices across NY and integrated accredited imaging centers, we have increased the accessibility of lung cancer screening and improved transitions to follow-up care. Nationally, only 4.5% of patients at high risk are screened, however, we found that with our approach to patient care, we have achieved among the highest reported screening rates in eligible individuals.

Interventional bronchoscopy in lung cancer treatment.

Interventional bronchoscopy has seen significant advancements in recent decades, particularly in the context of lung cancer. This method has expanded not only diagnostic capabilities but also therapeutic options. In this article, we will outline various therapeutic approaches employed through either a rigid or flexible bronchoscope in multimodal lung cancer treatment. A pivotal focus lies in addressing central airway obstruction resulting from cancer. We will delve into the treatment of initial malignant changes in central airways and explore the rapidly evolving domain of early peripheral malignant lesions, increasingly discovered incidentally or through lung cancer screening programmes. A successful interventional bronchoscopic procedure not only alleviates severe symptoms but also enhances the patient's functional status, paving the way for subsequent multimodal treatments and thereby extending the possibilities for survival. Interventional bronchoscopy proves effective in treating initial cancerous changes in patients unsuitable for surgical or other aggressive treatments due to accompanying diseases. The key advantage of interventional bronchoscopy lies in its minimal invasiveness, effectiveness and favourable safety profile.

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 >90%. These interventions can be considered for use in other LCS programs to improve adherence to LCS follow-up.

Telemedicine-Enhanced Lung Cancer Screening Using Mobile Computed Tomography Unit with Remote Artificial Intelligence Assistance in Underserved Communities: Initial Results of a Population Cohort Study in Western China.

Introduction: Lung cancer is a leading cause of cancer deaths globally. Despite favorable recommendations, low-dose computed tomography (LDCT) lung screening adoption remains low in China. Barriers such as limited infrastructure, costs, distance, and personnel shortages restrict screening access in disadvantaged regions. We initiated a telemedicine-enabled lung cancer screening (LCS) program in a medical consortium to serve people at risk in underserved communities. The objective of this study was to describe the implementation and initial results of the program. Methods: From 2020 to 2021, individuals aged 40-80 years were invited to take LCS by mobile computed tomography (CT) units in three underserved areas in Western China. Numerous CT scans were remotely reported by radiologists aided by artificial intelligence (AI) diagnostic systems. Abnormal cases were tracked through an integrated hospital network for follow-up. A retrospective cohort study documented participant demographics, health history, LDCT results, and outcomes. Descriptive analysis was conducted to report baseline characteristics and first-year follow-up results. Results: Of the 28,728 individuals registered in the program, 19,517 (67.94%) participated in the screening. The study identified 2.68% of participants with high-risk pulmonary nodules and diagnosed 0.55% with lung cancer after a 1-year follow-up. The majority of high-risk participants received timely treatment in hospitals. Conclusions: This study demonstrated mobile CT units with remote AI assistance improved access to LCS in underserved areas, with high participation and early detection rates. Our implementation supports the feasibility of deploying telemedicine-enabled LCS to increase access to a large scale of basic radiology and diagnostic services in resource-limited settings. Clinical Trial Registration Number: ChiCTR1900024623.

Budget impact model of LungFlag, a predictive risk model for lung cancer screening.

10534 Background: Lung cancer screening (LCS) programs using low-dose computed tomography (LDCT) for early detection reduce mortality and have been widely recommended. LungFlag is a machine-learning risk prediction model that uses patient-level data to identify individuals at high risk of developing non-small cell lung cancer (NSCLC), prompting their physician to recommend screening with LDCT. Methods: A budget impact model was developed to estimate the costs associated with adoption of LungFlag as an adjunct to existing US Preventive Services Task Force (USPSTF) screening guidelines for a hypothetical US commercial health plan population of 1 million beneficiaries. The model calculates the total expected annual costs of screening for NSCLC with LDCT in scenarios with and without LungFlag, including healthcare resource utilization for detecting NSCLC and treatment of patients diagnosed with NSCLC. Incremental costs were evaluated over a 5-year period. Results: Among 36,803 USPSTF-eligible persons, we assumed that 4600 (12.5%) had already initiated LCS, leaving 32,203 persons who were candidates for pre-screening with LungFlag. The model estimated that 17 additional NSCLC diagnoses per year would be detected by screening when using LungFlag, with most in stage 1. Over 5 years, LungFlag was estimated to result in 33 fewer patients with stage 3 or stage 4 NSCLC at diagnosis and 22 fewer NSCLC-related deaths. Use of LungFlag increased annual costs during the first 2 years and provided cost savings from Year 4 onwards (Table). Cost savings from LungFlag were attributable to reductions in the costs of advanced NSCLC treatment. Conclusions: In a population of 1 million commercial health plan beneficiaries, the adoption of LungFlag as an adjunct to existing screening guidelines for USPSTF-eligible patients was estimated to prevent 22 additional NSCLC-related deaths, with a cost savings of $2.87 million over 5 years from a US commercial payer perspective. [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&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<35) and high diagnostic accuracy of stage I&II NSCLC (AUC>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]

Artificial intelligence–aided lung cancer screening in routine clinical practice: A pilot of LungFlag at Geisinger.

e13604 Background: Use of low-dose computed tomography (LDCT) in lung cancer screening is recommended for early detection and can reduce mortality. LungFlag (Medial-EarlySign) is a machine-learning risk prediction model that identifies individuals at high risk of non-small cell lung cancer so their care team may recommend LDCT screening. The LungFlag model was developed using data from Kaiser Permanente Southern California and is being implemented in a pilot at Geisinger. Here we provide an overview of the clinical workflow for the LungFlag pilot. Methods: Geisinger has implemented LungFlag into routine clinical practice via integration with the electronic medical record system. This pilot is examining the performance and clinical impact of LungFlag in the first year. LungFlag runs every month to provide risk evaluation for individuals 50 to 80 years old who are eligible for annual lung cancer screening according to US Preventive Services Task Force recommendations. Eligible individuals have a 20 pack-year smoking history, no lung cancer history, and currently smoke or quit within the past 15 years. Patients with cancer history in the past 5 years and those screened or scheduled for screening in the past year are excluded. LungFlag identifies the top 30 individuals with the highest LungFlag scores per month (high-risk patients) and refers them for targeted outreach from a lung cancer screening program nurse navigator. A positive LungFlag result prompts nurse navigator chart review, patient outreach, and coordination with the primary care provider. Flagged individuals are contacted (up to 3 phone calls) and encouraged to receive lung cancer screening. Individuals not flagged by the model (non–high-risk patients) receive usual care consisting of reminders for lung cancer screening during primary care visits without additional targeted outreach and high-risk prioritization. Results: The LungFlag pilot was initiated in August 2023 and will continue until August 2024. As of January 2024, risk profiles of >12,000 patients were analyzed and 180 high-risk patients were identified. Conclusions: This pilot is the first evaluation of the implementation process for the LungFlag model in routine clinical practice. After the pilot, a retrospective observational study is planned to further assess the impact of LungFlag and the associated clinical workflow in identifying high-risk individuals and increasing compliance with LDCT screening among high-risk individuals.

Percutaneous Lung Biopsy Prevalence and Use of the Lung CT Scan and Reporting Data System in the Veterans Health Administration Lung Cancer Screening Program

Percutaneous Lung Biopsy Prevalence and Use of the Lung CT Scan and Reporting Data System in the Veterans Health Administration Lung Cancer Screening Program

Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events.

BACKGROUND. Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asian country to initiate a national LDCT lung cancer screening program, although CAC-related outcomes are poorly explored. OBJECTIVE. The purpose of this article is to evaluate CAC prevalence and severity using visual analysis and artificial intelligence (AI) methods and to characterize CAC's association with major adverse cardiovascular events (MACEs) in patients undergoing LDCT in Korea's national lung cancer screening program. METHODS. This retrospective study included 1002 patients (mean age, 62.4 ± 5.4 [SD] years; 994 men, eight women) who underwent LDCT at two Korean medical centers between April 2017 and May 2023 as part of Korea's national lung cancer screening program. Two radiologists independently assessed CAC presence and severity using visual analysis, consulting a third radiologist to resolve differences. Two AI software applications were also used to assess CAC presence and severity. MACE occurrences were identified by EMR review. RESULTS. Interreader agreement for CAC presence and severity, expressed as kappa, was 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 60.1% by AI software I, and 56.6% by AI software II. CAC severity was mild, moderate, and severe by consensus visual analysis in 28.0%, 10.3%, and 15.1%; by AI software I in 39.9%, 14.0%, and 6.2%; and by AI software II in 34.9%, 14.3%, and 7.3%. MACEs occurred in 36 of 625 (5.6%) patients with follow-up after LDCT (median, 1108 days). MACE incidence in patients with no, mild, moderate, and severe CAC for consensus visual analysis was 1.1%, 5.0%, 2.9%, and 8.6%, respectively (p < .001); for AI software I, it was 1.3%, 3.0%, 7.9%, and 11.3% (p < .001); and for AI software II, it was 1.2%, 3.4%, 7.7%, and 9.6% (p < .001). CONCLUSION. For Korea's national lung cancer screening program, MACE occurrence increased significantly with increasing CAC severity, whether assessed by visual analysis or AI software. The study is limited by the large sex imbalance for Korea's national lung cancer screening program. CLINICAL IMPACT. The findings provide reference data for health care practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.

Incidence and Management of Cardiothoracic Relevant Extrapulmonary Findings Found on Low-Dose Computed Tomography

BackgroundWe aimed to investigate the incidence of extrapulmonary findings identified on low-dose computed tomography (CT) that may warrant evaluation by cardiothoracic surgeons and describe their management and referral patterns at our institution. MethodsWe conducted a retrospective cohort study of patients who underwent low-dose CT through a centralized Lung Cancer Screening Program at Thomas Jefferson University Hospital between January 2018 and December 2022. An electronic medical record review was performed for patients with incidental findings. Demographic, workup, referral, and management data were collected. ResultsOf 2285 patients who underwent low-dose CT, 368 (16%) had an extrapulmonary finding that may have an indication for clinical evaluation by a cardiothoracic surgeon. The most common incidental finding was a hiatal hernia, with a prevalence of 6.3% (144 of 2285), followed by ascending thoracic aneurysms, with a prevalence of 3.6% (82 of 2285), and small pericardial effusions, with a prevalence of 1.2% (28 of 2285). Of the patients with symptomatic hiatal hernias, 29% (14 of 48) were referred to a cardiothoracic surgeon compared with only 6.25% (6 of 96) in the asymptomatic group. Of the patients with thoracic aneurysms, 48% (39 of 82) had aneurysms ≥4.2 cm. Of the ≥4.2 cm group, 18% (7 of 39) were monitored by a cardiothoracic surgeon compared with 11.6% (5 of 43) in patients with aneurysms <4.2 cm. ConclusionsHiatal hernias and ascending thoracic aneurysms were the 2 most prevalent incidental findings identified on low-dose CT during lung cancer screening. We demonstrated potential gaps in hiatal hernia referral patterns. Referring patients with thoracic aneurysms to cardiothoracic surgeons may not be initially warranted.

The Role of Biomarkers in Lung Cancer Screening.

Lung Cancer Screening (LCS) is an evolving field with variations in its implementation in various countries. There are only scarce data from National LCS programs. We aim to provide an up-to-date overview of the current evidence regarding the use of biomarkers in LCS. A multidisciplinary Task Force experts' panel collaborated and conducted a systematic literature search, followed by screening, review and synthesis of available evidence. Biomarkers in LCS could be used to improve risk stratification in high-risk participants, improve clarification regarding indeterminate lung nodules and avoid overdiagnosis in suspicious lung findings. Currently, there seem to be promising biomarkers (blood/serum/breath) that have been studied in various trials; however, there is still a lack of solid evidence in clinical validation that would pave the way for their integration into LCS programs. Biomarkers are the next logical step in improving the LCS pathway and its efficiency by playing an adjuvant role in a minimally invasive way. National LCS programs and pilot studies should integrate biomarkers to validate their accuracy in real-life LCS participants.

Lung cancer screening: new frontiers

Lung cancer is the leading cause of cancer-related deaths worldwide. In early, asymptomatic stages, curative treatment is possible, but the disease is often diagnosed too late. Lung cancer screening (LCS) using low-dose computed tomography (LDCT) helps to detect potentially malignant lesions in early stages and to reduce lung cancer mortality. The application of artificial intelligence (AI) algorithms enables amore precise analysis of LDCT scans. Ameta-analysis of eight LCS studies revealed astatistically significant 12% relative reduction in lung cancer mortality. Based on strong scientific evidence, arecommendation for astructured lung cancer screening program using LDCT for the high-risk population in Germany was issued. The holistic LCS program requires aclear definition of the high-risk population, individual risk assessment, qualified personnel for conducting and reading examinations, verification of all diagnostic and therapeutic steps, central documentation and quality assurance, as well as the integration of tobacco cessation programs.

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently.Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

HUNCHEST projects-advancing low-dose CT lung cancer screening in Hungary.

Lung cancer, the leading cause of malignancy-related deaths worldwide, demands proactive measures to mitigate its impact. Low-dose computer tomography (LDCT) has emerged as a promising tool for secondary prevention through lung cancer screening (LCS). The HUNCHEST study, inspired by the success of international trials, including the National Lung Cancer Screening Trial and the Dutch NELSON study, embarked on the first LDCT-based LCS program in Hungary. The initiative assessed the screening efficiency, incorporating lung function tests and exploring the interplay between lung cancer and chronic obstructive pulmonary disease (COPD). Building upon this foundation, an implementation trial involving 18 Hungarian centers supported by the Ministry of Human Capacities demonstrated the feasibility of LCS within a multicentric framework. These centers, equipped with radiology capabilities, collaborated with multidisciplinary oncology teams, ensuring optimal patient pathways. However, a critical challenge remained the patient recruitment. To address this, the HUNCHEST 3 project, initiated in 2023, seeks to engage general practitioners (GPs) to reach out to eligible patients within a municipality collective of 60 thousand inhabitants. The project's ultimate success is contingent upon the willingness of eligible individuals to undergo LDCT scans. In conclusion, the HUNCHEST program represents a crucial step in advancing lung cancer screening in Hungary. With a focus on efficiency, multidisciplinary collaboration, and innovative patient recruitment strategies, it endeavors to contribute to the reduction of lung cancer mortality and serve as a blueprint for potential nationwide LCS programs.

AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images

Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection.

Enhancing Lung Cancer Care in Portugal: Bridging Gaps for Improved Patient Outcomes.

Lung cancer has the highest incidence and cancer-related mortality worldwide. In Portugal, it ranks as the fourth most common cancer, with nearly 6000 new cases being diagnosed every year. Lung cancer is the main cause of cancer-related death among males and the third cause of cancer-related death in females. Despite the globally accepted guidelines and recommendations for what would be the ideal path for a lung cancer patient, several challenges occur in real clinical management across the world. The recommendations emphasize the importance of adequate screening of high-risk individuals, a precise tumour biopsy, and an accurate final diagnosis to confirm the neoplastic nature of the nodule. A detailed histological classification of the lung tumour type and a comprehensive molecular characterization are of utmost importance for the selection of an efficacious and patient-directed therapeutic approach. However, in the context of the Portuguese clinical organization and the national healthcare system, there are still several gaps in the ideal pathway for a lung cancer patient, involving aspects ranging from the absence of a national lung cancer screening programme through difficulties in histological diagnosis and molecular characterization to challenges in therapeutic approaches. In this manuscript, we address the most relevant weaknesses, presenting several proposals for potential solutions to improve the management of lung cancer patients, helping to decisively improve their overall survival and quality of life.

Perspectives of potentially eligible Indigenous Māori on a lung cancer screening programme: a qualitative study

aims: Lung cancer causes more deaths than any other cancer, globally and in Aotearoa New Zealand, where it disproportionately affects Māori. We aimed to understand Māori perspectives on lung cancer screening in Aotearoa New Zealand to guide its equity focussed implementation, including identifying enablers and barriers. methods: We took a Kaupapa Māori based co-design approach to inform future screening, recruiting Māori current/ex-smokers and members of their whānau (family) for three focus group phases held in Auckland, Aotearoa New Zealand in August 2019. Participants responded to a proposed lung cancer screening pathway and shared their attitudes and beliefs about lung cancer and screening. Results were thematically analysed. results: The 21 Māori participants supported future lung cancer screening in Aotearoa New Zealand. Perceived benefits included being more informed about lung cancer and screening and enabling healthier future generations. Barriers to screening were previous negative health service experiences; fear; stigma; and access, including time, cost and transport. Enablers included providers’ cultural competence; clear communication; a one-stop shop; and support with transport. A range of factors could potentially influence a decision to participate in screening. conclusions: Participants favoured future lung cancer screening and identified key barriers and facilitators of screening.

189 Primal Screen: Do contrast-enhanced CT staging scans add clinically significant information to patients referred from a lung cancer screening programme?

189 Primal Screen: Do contrast-enhanced CT staging scans add clinically significant information to patients referred from a lung cancer screening programme?

Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program

PurposeWe developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables’ value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program. Materials and Methods480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models. ResultsFor outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome. ConclusionsWe identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.

203 Should interactive 3D-CT models be used as an alternative to repeat contrast computed tomography in lung cancer screening programme patients for surgical planning?

203 Should interactive 3D-CT models be used as an alternative to repeat contrast computed tomography in lung cancer screening programme patients for surgical planning?