Low-dose CT Lung Cancer Screening Research Articles

ALINA: Another stepping stone toward a future of LDCT lung cancer screening in never-smokers?

The ALINA trial1 demonstrated that 2 years of adjuvant alectinib achieved statistically significantly improved 2-year overall and central nervous system (CNS) disease-free survival over platinum-doublet chemotherapy in resected early-stage (IB ≥ 4cm to IIIA) ALK+ non-small cell lung cancer (NSCLC). Identifying early-stage ALK+ NSCLC patients (60% were never-smokers in the ALINA trial) may require low-dose computed tomography (LDCT) lung cancer screening in never-smokers.

Yorkshire Lung Screening Trial (YLST) pathway navigation study: a protocol for a nested randomised controlled trial to evaluate the effect of a pathway navigation intervention on lung cancer screening uptake

IntroductionLung cancer is the most common cause of cancer death globally. In 2022 the UK National Screening Committee recommended the implementation of a national targeted lung cancer screening programme, aiming...

Analysis of diagnosis patterns and implications on cancer stage in patients with lung cancer: A single-institution experience.

e22508 Background: Lung cancer is the most common cause of cancer-related death in the US. Patients are often diagnosed at advanced stages, challenging treatment and improving life expectancy. There has been tremendous progress in developing newer treatments; however, it is crucial to emphasize low-dose CT (LDCT) screening as it may improve earlier diagnosis and ultimately lead to better outcomes. Research Question What is the utilization of LDCT for lung cancer screening in newly diagnosed lung cancer patients? Do patients with lung cancer diagnosed after LDCT have earlier-stage disease when compared to patients who did not have LDCT? Methods: We conducted an observational cohort study on all new lung cancer patients seen in our clinic from January 2022 to July 2023 (18 months). Data collected included the age, type of lung cancer (NSCLC/SCLC), reason and method of initial identification of lung mass/nodule, methodology of biopsy, cancer stage, and if the patient was eligible for LDCT. Results: Over the study duration, 143 newly diagnosed lung cancer patients were seen in our oncology clinic. Of these, only 12% (18) patients had LDCT before diagnosis. Amongst these 18 patients, 44% (8) were diagnosed with stage I, two patients were diagnosed with stage II, and 44% (4+4) were diagnosed with stages III and IV. A total of 56% of patients (combined stage I/II) were eligible for curative treatment in this group. The remaining 82% of patients were diagnosed with lung cancer after imaging for other reasons (non-LDCT group). In this group, the most common stage at diagnosis was stage IV, constituting 39% (56) of the patients, followed by patients diagnosed at stage III with 24% (34). Only 37% (combined stage I/II) were eligible for curative treatment in this group. Conclusions: The USPSTF recommends annual lung cancer screening with LDCT for adults aged 50 to 80 years who have a 20-pack-year smoking history and are current smokers or have quit within the past 15 years. In an observational study conducted in 2021 from our institution, we identified a lower utilization of LDCT in eligible patients. We improved the screening rates by more than 50% from baseline with lung navigation intervention. In this study, we show that patients are getting diagnosed with lung cancer from X-rays and CTs performed for reasons including but not limited to chest pain, shortness of breath, workup for pulmonary embolism, and preoperative X-rays. Most patients diagnosed after LDCT (44%) had stage I disease compared to only 18% of patients in the non-LDCT group. We also observed that a significantly higher percentage (56% vs 37%) of patients are eligible for curative treatment (stage I/II) when diagnosed with LDCT. We highlight the importance of LDCT screening potentially offering a curable treatment in patients with lung cancer.

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.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study.

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning. A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001). The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT

BackgroundThis study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable.Materials and methodsA standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models.ResultsVolumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR.ConclusionWe observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

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.

When to reinvite initially ineligible populations for targeted lung cancer screening?

IntroductionTargeted low-dose CT lung cancer screening reduces lung cancer mortality. England’s Targeted Lung Health Check programme uses risk prediction tools to determine eligibility for biennial screening among people with a...

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.

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.

The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures. In this study, we investigate kernel harmonization in a multi-vendor low-dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT-based assessments. Using the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization. Our approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p<0.05) and an increase in the PSNR (p<0.05) and SSIM (p<0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features. Kernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.

Opportunistic lung cancer screening with low-dose computed tomography in National Cancer Center of China: The first 14 years' experience.

In China, over 50% of lung cancer cases occur in nonsmokers. Thus, identifying high-risk individuals for targeted lung cancer screening is crucial. Beyond age and smoking, determining other risk factors for lung cancer in the Asian population has become a focal point of research. Using 30,000 participants in the prospectively enrolled cohort at China's National Cancer Center (NCC) over the past 14 years, we categorized participants by risk, with an emphasis on nonsmoking females. Between November 2005 and December 2019, 31,431 individuals voluntarily underwent low-dose computed tomography (LDCT) scans for lung cancer screening at the NCC. We recorded details like smoking history, exposure to hazards, and family history of malignant tumors. Using the 2019 NCCN criteria, participants were categorized into high-, moderate-, and low-risk groups. Additionally, we separated non-high-risk groups into female never smokers (aged over 40) exposed to second-hand smoke (SHS) and others. Any positive results from initial scans were monitored per the I-ELCAP protocol (2006), and suspected malignancies were addressed through collaborative decisions between patients and physicians. We analyzed and compared the detection rates of positive results, confirmed lung cancers, and cancer stages across risk, age, and gender groups. Out of 31,431 participants (55.9% male, 44.1% female), 3695 (11.8%) showed positive baseline LDCT scans with 197 (0.6%; 106 females, 91 males) confirmed as lung cancer cases pathologically. Malignancy rate by age was 0.1% among those aged under 40 years, 0.4% among those aged 40-49 years, 0.8% among those aged 50-59 years, and 1.2% among those aged 60 years and older. From the 25,763 participants (56.9% male, 43.1% female) who completed questionnaires, 1877 (7.3%) were categorized as high risk, 6500 (25.2%) as moderate risk, and 17,386 (67.5%) as low risk. Of the 23,886 in the non-high-risk category, 8041 (33.7%) were females over 40 years old exposed to SHS. The high-risk group showed the highest lung cancer detection rate at 1.4%. However, females exposed to SHS had a notably higher detection rate than the rest of the non-high-risk group (1.1% vs. 0.5%; p < 0.0001). In this cohort, 84.8% of the detected lung cancers were at an early stage. In our study, using LDCT for lung cancer screening proved significant for high-risk individuals. For non-high-risk populations, LDCT screening could be considered for nonsmoking women with exposure to SHS.

Understanding Patient Barriers to Utilization of Low-dose CT Lung Cancer Screening in a High-risk Population

Understanding Patient Barriers to Utilization of Low-dose CT Lung Cancer Screening in a High-risk Population

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

Do we know enough about the effect of low-dose computed tomography screening for lung cancer on mortality to act? An updated systematic review, meta-analysis and network meta-analysis of randomised controlled trials 2017 to 2021

BackgroundFor people at high risk of lung cancer, low-dose computed tomography (LDCT) is proposed as a method to reduce mortality.MethodsOur objective was to estimate the effect of LDCT lung cancer screening on mortality in high-risk populations.A systematic review of randomised controlled trials (RCTs) comparing LDCT screening programmes with usual care (no screening) or other imaging screening programme (such as chest X-ray (CXR)) was conducted. RCTs of CXR screening were additionally included in the network meta-analyses. Bibliographic sources including MEDLINE, Embase, Web of Science and the Cochrane Library were searched to January 2017, and then further extended to November 2021. All key review steps were done by two persons. Quality assessment used the Cochrane Risk of Bias tool. Meta-analyses were performed.ResultsNine RCTs, with up to 12.3 years of follow-up from randomisation, were included in the direct meta-analysis, which showed that LDCT screening was associated with a statistically significant decrease in lung cancer mortality (pooled relative risk (RR) 0.86, 95% confidence interval [CI] 0.77 to 0.96). There was a statistically non-significant decrease in all-cause mortality (pooled RR 0.98, 95% CI 0.95 to 1.01). The statistical heterogeneity for both outcomes was minimal. Network meta-analysis including the nine RCTs in the direct meta-analysis plus two further RCTs comparing CXR with usual care confirmed the size of the effect of LDCT on lung cancer mortality and that this was very similar irrespective of whether the comparator was usual care or CXR screening.ConclusionsLDCT screening is effective in reducing lung cancer mortality in high-risk populations. The uncertainty of its effect on lung cancer mortality observed in 2018 has been much reduced with new trial results and updates to existing trials, emphasising the importance of updating systematic reviews. Although there are still a number of RCTs unreported or in progress, we predict that further evolution of summary mortality estimates is unlikely. The focus for debate now moves to resolving uncertainty about the cost-effectiveness of LDCT screening taking into account the balance between benefits and harms which occur in all screening programmes.

Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee

Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.

OA16.03 Taking A Health Systems Approach to Low-Dose CT Lung Cancer Screening: A Bespoke Framework to Support Implementation

OA16.03 Taking A Health Systems Approach to Low-Dose CT Lung Cancer Screening: A Bespoke Framework to Support Implementation

Effectiveness of a single low-dose computed tomography screening for lung cancer: A population-based perspective cohort study in China.

The purpose of this perspective cohort study was to evaluate the effectiveness of low-dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non-screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow-up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non-screened group had the highest cumulative lung cancer mortality and all-cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12-1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49-0.97]), and the all-cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68-0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all-cause mortality in China.

Swiss Pilot Low-Dose CT Lung Cancer Screening Study: First Baseline Screening Results.

This pilot study conducted in Switzerland aims to assess the implementation, execution, and performance of low-dose CT lung cancer screening (LDCT-LCS). With lung cancer being the leading cause of cancer-related deaths in Switzerland, the study seeks to explore the potential impact of screening on reducing mortality rates. However, initiating a lung cancer screening program poses challenges and depends on country-specific factors. This prospective study, initiated in October 2018, enrolled participants meeting the National Lung Cancer Study criteria or a lung cancer risk above 1.5% according to the PLCOm2012 lung cancer risk-model. LDCT scans were assessed using Lung-RADS. Enrollment and follow-up are ongoing. To date, we included 112 participants, with a median age of 62 years (IQR 57-67); 42% were female. The median number of packs smoked each year was 45 (IQR 38-57), and 24% had stopped smoking before enrollment. The mean PLCOm2012 was 3.7% (±2.5%). We diagnosed lung cancer in 3.6% of participants (95%, CI:1.0-12.1%), with various stages, all treated with curative intent. The recall rate for intermediate results (Lung-RADS 3,4a) was 15%. LDCT-LCS in Switzerland, using modified inclusion criteria, is feasible. Further analysis will inform the potential implementation of a comprehensive lung cancer screening program in Switzerland.

A multivariate analysis of low-dose CT lung cancer screening in a Michigan community hospital network.

10576 Background: Lung cancer is the leading cause of cancer deaths in the United States. The USPSTF recommended screening for lung cancer with low-dose chest computer tomography (LDCT) in 2013. This was based on the National Lung Screening Trial (NLST) which showed a 20% relative reduction in mortality from LDCT compared with chest radiography in high-risk individuals. The major NLST eligibility criteria were age 55-74, a 30 + pack year smoking history and current smoking status or having quit in the last 15 years. The impact of other risk factors on screening results besides inclusion criteria is unknown. We sought to identify other factors for the risk of lung cancer detection in screened individuals. Methods: This was a retrospective cohort study employing a Michigan based McLaren Hospital Network Lung Cancer Screening Program (LCSP) database from 2015-2022. Participant eligibility in screening was the USPSTF criteria except for an age range of 50-80. Analyses were stratified by BMI, smoking pack-years, insurance status, smoking status, sex, other CT findings, pre- vs. during COVID pandemic (before and after January 2020). Multivariate logistic regression analysis was used for odds ratios (OR) with 95% confidence intervals (CI) as estimates of the relative risk of an elevated Lung-RADS category ≥3 (high risk of detecting lung cancer). Results: A total of 2638 subjects, 49.91% females, 50.09% males, mean BMI 29.50 ± 7.17 (SD), pack-years 44.52 ±18.29, Medicaid 6.66%, Medicare 32.01%, Private Insurance 57.62%, Self-pay /unknown 3.71%. Current smoker 65.49%, Former smoker 34.51%, other CT findings 51.99%, pre-COVID 46.08%, during COVID pandemic 53.92%. Lung-RADS category 0 (0.04%), 1 (35.33%), 2, (50.61%), 3 (6.56%), 4a (4.85%), 4b (1.55%), 4x (1.06%). A statistically significant association with a Lung-RADS category ≥3 was found with lower BMI, OR=0.980 (95% CI, 0.969-0.991), p=0.0005, and female gender, OR=1.232 (95% CI, 1.039-1.462), p=0.0166 (Table). Conclusions: In our study cohort a lower-than-average BMI and also female gender were statistically correlated with an elevated Lung-RADS category ≥3. These findings might be incorporated into a risk stratification model, however further larger studies are needed to validate our findings. [Table: see text]

ACR Lung CT Screening Reporting and Data System, a Systematic Review and Meta-Analysis Before Change in US Preventative Services Taskforce Eligibility Criteria: 2014 to 2021

ObjectiveTo review Lung CT Screening Reporting and Data System (Lung-RADS) scores from 2014 to 2021, before changes in eligibility criteria proposed by the US Preventative Services Taskforce. MethodsA registered systematic review and meta-analysis was conducted in MEDLINE, Embase, CINAHL, and Web of Science in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines; eligible studies examined low-dose CT (LDCT) lung cancer screening at institutions in the United States and reported Lung-RADS from 2014 to 2021. Patient and study characteristics, including age, gender, smoking status, pack-years, screening timeline, number of individual patients, number of unique studies, Lung-RADS scores, and positive predictive value (PPV) were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling. ResultsThe meta-analysis included 24 studies yielding 36,211 LDCT examinations for 32,817 patient encounters. The meta-analysis Lung-RADS 1-2 scores were lower than anticipated by ACR guidelines, at 84.4 (95% confidence interval [CI] 83.3-85.6) versus 90% respectively (P < .001). Lung-RADS 3 and 4 scores were both higher than anticipated by the ACR, at 8.7% (95% CI 7.6-10.1) and 6.5% (95% CI 5.707.4), compared with 5% and 4%, respectively (P < .001). The ACR’s minimum estimate of PPV for Lung-RADS 3 to 4 is 21% or higher; we observed a rate of 13.1% (95% CI 10.1-16.8). However, our estimated PPV rate for Lung-RADS 4 was 28.6% (95% CI 21.6-36.8). ConclusionLung-RADS scores and PPV rates in the literature are not aligned with the ACR’s own estimates, suggesting that perhaps Lung-RADS categorization needs to be reexamined for better concordance with real-world screening populations. In addition to serving as a benchmark before screening guideline broadening, this study provides guidance for future reporting of lung cancer screening and Lung-RADS data.