Lung Cancer Screening Program Research Articles

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?

Quality requirements to establish a successful lung cancer screening program

Quality requirements to establish a successful lung cancer screening program

Abstract 4790: Hospitalization as an opportunity to improve lung cancer screening in high-risk patients

Abstract Background: Lung cancer screening with annual low-dose computed tomography (LDCT) in high-risk patients with exposure to smoking reduces lung cancer-related mortality, yet the screening rate of eligible adults is low. As hospitalization is a critical moment to engage patients in their overall health, it may be an opportunity to improve rates of lung cancer screening. Prior to implementing a hospital-based lung cancer screening referral program, this study assesses the association between hospitalization and completion of lung cancer screening. Methods: A retrospective cohort study of evaluated completion of at least one LDCT from 2014-2021 using electronic health record data using hospitalization as the primary exposure. Patients aged 55-80 who received care from a university-based internal medicine clinic and reported cigarette use were included. Univariate analysis and logistic regression models evaluated the association of hospitalization and completion of LDCT. The secondary outcome was completion of any CT of the chest. Results: Of the 1,935 current smokers identified, 47% had at least one hospitalization, and 21% completed a LDCT during the study period. While a higher proportion of patients with a hospitalization had a LDCT (24%) compared to patients without a hospitalization (18%, p<0.001), there was no association between hospitalization and completion of a LDCT after adjusting for potentially confounding covariates (95%CI 0.680 - 1.149). Of cohort patients, 38% completed any CT of the chest and hospitalization was associated with increased odds of receiving chest CT imaging in the adjusted model (OR 1.72; 95%CI 1.37 - 2.17). Conclusions: In a cohort of patients at risk for lung cancer, only 1 in 5 completed lung cancer screening with LDCT and hospitalization events were not associated with LDCT completion. A hospitalist driven lung cancer screening program has the potential to increase the suboptimal rates of lung cancer screening in high-risk patients. Citation Format: Ellen M. Nielsen, Jingwen Zhang, Justin Marsden, Chloe Bays, William P. Moran, Patrick D. Mauldin, Leslie A. Lenert, Benjamin A. Toll, Andrew D. Schreiner, Marc Heincelman. Hospitalization as an opportunity to improve lung cancer screening in high-risk patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4790.

Abstract 3661: Non-coding RNAs as biomarkers for early detection of lung cancer

Abstract Introduction: Screening programs proven to reduce lung cancer deaths in high-income countries. Biomarkers detection in body fluids may guide more precise selection of high-risk subjects but this approach has not been currently employed for lung cancer screening. Aims: To identify ncRNA-based signatures for early detection of lung cancer in liquid biopsies. Methods: This is a case-control prospective study, conducted at the Barretos Cancer Hospital Screening Program. We analyzed miRNAs in plasma and sputum samples from high-risk subjects (n=51), and patients (n=44) diagnosed with non-small cell lung cancer (NSCLC), no metastatic, at Barretos Cancer Hospital (Brazil). We analyzed circRNAs in plasma samples from high-risk subjects (n=100), and patients (n=86) with NSCLC at Hospital Universitari Quiron Dexeus (Spain). Samples were subjected to RNA isolation followed expression analysis, for miRNA expression was used nCounter Human V3 microRNA painel, and for circRNA expression was use a nCounter customized painel (NanoString). Counts were normalized by housekeeping, and differentially expressed miRNAs were filtered out according to fold-change (≥|1.5|), p-value (≤0.05). Results: All cancer patients were diagnosed with adenocarcinoma (n=68), squamous cell carcinoma (n=41) and others/no information (n=36). No differences were observed related to age, sex, and tobacco exposure between high-risk and cancer groups (p>0.05). For Brazilian group, we observed six differentially expressed miRNAs in plasma able to distinguish cancer from non-cancer samples - three miRNAs upregulated, and three miRNAs downregulated -, and three of them presented with high accuracy (AUC = 0.70). For sputum, we observed four differentially expressed miRNAs able to distinguish cancer from non-cancer samples, and the 4-miRNA signature present with high accuracy (AUC = 0.73). None of miRNAs were shared between plasma and sputum. In both sample types we have miRNAs associated with zinc finger (ZNF99 and ZNF268), that predicted to be involved in regulation of transcription by RNA polymerase II. In Spanish cohort, we observed 4-circRNA signature with high accuracy (AUC = 0.745). These four circRNAs are associated with insulin resistance and proto-oncogenes. Conclusion: We identified ncRNA-based biomarkers in biofluids potentially to be employed in lung cancer screening programs to better guide selection of high-risk subjects for early detection of lung cancer. Citation Format: Giovanna Maria Stanfoca Casagrande, Alessandro Pascon Filho, Alexia Polo Siqueira, Rodrigo Sampaio Chiarantano, Fabiana de Lima Vazquez, Miguel Angel Molina, Carlos Pedraz-Valdunciel, Ana Gimenez-Capitán, Rui Manuel Reis, Leticia Ferro Leal. Non-coding RNAs as biomarkers for early detection of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3661.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities.

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Promoting Lung Cancer Screen Decision-Making and Early Detection Behaviors: A Systematic Review and Meta-analysis.

Promoting lung cancer screening (LCS) is complex. Previous studies have overlooked that LCS behaviors are stage based and thus did not identify the characteristics of LCS interventions at different screening stages. The aims of this study were to explore the characteristics and efficacy of interventions in promoting LCS decision making and behaviors and to evaluate these interventions. We conducted a study search from the inception of each bibliographic database to April 8, 2023. The precaution adoption process model was used to synthesize and classify the evidence. The RE-AIM framework was used to evaluate the effectiveness of LCS programs. Heterogeneity tests and meta-analysis were performed using RevMan 5.4 software. We included 31 studies that covered 4 LCS topics: knowledge of lung cancer, knowledge of LCS, value clarification exercises, and LCS supportive resources. Patient decision aids outperformed educational materials in improving knowledge and decision outcomes with a significant reduction in decision conflict (standardized mean difference, 0.81; 95% confidence interval, -1.15 to -0.47; P < .001). Completion rates of LCS ranged from 3.6% to 98.8%. Interventions that included screening resources outperformed interventions that used patient decision aids alone in improving LCS completion. The proportions of reported RE-AIM indicators were highest for reach (69.59%), followed by adoption (43.87%), effectiveness (36.13%), implementation (33.33%), and maintenance (9.68%). Evidence from 31 studies identified intervention characteristics and effectiveness of LCS interventions based on different stages of decision making. It is crucial to develop targeted and systematic interventions based on the characteristics of each stage of LCS to maximize intervention effectiveness and reduce the burden of lung cancer.

Factors Associated With Delay in Lung Cancer Diagnosis and Surgery in a Lung Cancer Screening Program.

Delays to biopsy and surgery after lung nodule detection can impact survival from lung cancer. The aim of this study was to identify factors associated with delay in a lung cancer screening (LCS) program. We evaluated patients in an LCS program from May 2015 through October 2021 with a malignant lung nodule classified as lung CT screening reporting and data system (Lung-RADS) 4B/4X. A cutoff of more than 30 days between screening computed tomography (CT) and first tissue sampling and a cutoff of more than 60 days between screening CT and surgery were considered delayed. We evaluated the relationship between delays to first tissue sampling and surgery and patient sex, age, race, smoking status, median income by zip code, language, Lung-RADS category, and site of surgery (academic vs community hospital). A total of 185 lung cancers met the inclusion criteria, of which 150 underwent surgical resection. The median time from LCS CT to first tissue sampling was 42 days, and the median time from CT to surgery was 52 days. 127 (69%) patients experienced a first tissue sampling delay and 60 (40%) had a surgical delay. In multivariable analysis, active smoking status was associated with delay to first tissue sampling (odds ratio: 3.0, CI: 1.4-6.6, P = 0.005). Only performing enhanced diagnostic CT of the chest before surgery was associated with delayed lung cancer surgery (odds ratio: 30, CI: 3.6-252, P = 0.02). There was no statistically significant difference in delays with patients' sex, age, race, language, or Lung-RADS category. Delays to first tissue sampling and surgery in a LCS program were associated with current smoking and performing diagnostic CT before surgery.

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: 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 years; 994 men, 8 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, were 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 56.6% by AI software I, and 60.1% 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%. MACE 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% (p<.001); for AI software I was 1.3%, 3.0%, 7.9%, and 11.3% (p<.001); and for AI software II was 1.2%, 5.0%, 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 healthcare practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.

Asian Americans in a Lung Cancer Screening Program Have Lower Annual Adherence Compared With Other Race Groups

Asian Americans in a Lung Cancer Screening Program Have Lower Annual Adherence Compared With Other Race Groups

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.

Recommended approaches for screening and early detection of lung cancer in the Middle East and Africa (MEA) region: a consensus statement.

The prevalence of lung cancer in the Middle East and Africa (MEA) region has steadily increased in recent years and is generally associated with a poor prognosis due to the late detection of most of the cases. We explored the factors leading to delayed diagnoses, as well as the challenges and gaps in the early screening, detection, and referral framework for lung cancer in the MEA. A steering committee meeting was convened in October 2022, attended by a panel of ten key external experts in the field of oncology from the Kingdom of Saudi Arabia, United Arab Emirates, South Africa, Egypt, Lebanon, Jordan, and Turkey, who critically and extensively analyzed the current unmet needs and challenges in the screening and early diagnosis of lung cancer in the region. As per the experts' opinion, lack of awareness about disease symptoms, misdiagnosis, limited screening initiatives, and late referral to specialists were the primary reasons for delayed diagnoses emphasizing the need for national-level lung cancer screening programs in the MEA region. Screening guidelines recommend low-dose computerized tomography (LDCT) for lung cancer screening in patients with a high risk of malignancy. However, high cost and lack of awareness among the public as well as healthcare providers prevented the judicious use of LDCT in the MEA region. Well-established screening and referral guidelines were available in only a few of the MEA countries and needed to be implemented in others to identify suspected cases early and provide timely intervention thus improving patient outcomes. There is a great need for large-scale screening programs, preferably integrated with tobacco-control programs and awareness programs for physicians and patients, which may facilitate higher adherence to lung cancer screening and improve survival outcomes.

RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Spatial and spatio-temporal clusters of lung cancer incidence by stage of disease in Michigan, United States 1985-2018.

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review

BackgroundLung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.MethodsElectronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model’s average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.DiscussionThe results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.Systematic review registrationThis protocol has been registered in PROSPERO under the registration number CRD42023483824.