Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer.

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

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

OBJECTIVES: Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations. METHODS: We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses. RESULTS: A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90-0.99) and pooled specificity was 0.90 (95% CI: 0.87-0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92-0.99] vs. 0.84 [95% CI: 0.50-0.96]) and lower specificity (0.87 [95% CI: 0.85-0.88] vs. 0.95 (95% CI: 0.92-0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91-0.99] vs. 0.91 [95% CI: 0.67-0.98]) and lower specificity (0.88 [95% CI: 0.85-0.90] vs. 0.93 [95% CI: 0.88-0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83-0.96) and specificity was 0.81 (95% CI: 0.67-0.90). CONCLUSION: Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence. CLINICAL RELEVANCE STATEMENT: Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence. KEY POINTS: • High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0. • In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies. • There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules.

BACKGROUND: During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability. METHODS: In total, 724 IPNs (size 50-500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP). RESULTS: The random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods. CONCLUSION: ML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.

An optimized D-dimer cut-off value to predict pulmonary thromboembolism in COVID-19 patients.

Pulmonary thromboembolism (PTE) is a common complication in coronavirus disease 2019 (COVID-19) patients. Elevated D-dimer levels are observed even in the absence of PTE, reducing its discriminative ability as a screening test. It is unknown whether conventional D-dimer cut-off values, as used in the YEARS algorithm, apply to COVID-19 patients. This study aimed to determine the optimal D-dimer cut-off value to predict PTE in COVID-19 patients. All confirmed COVID-19 patients with a computed tomography pulmonary angiography (CTPA) performed ≤5 days after admission due to suspicion of PTE between March 2020 and February 2021, at Medisch Spectrum Twente, The Netherlands, were retrospectively analyzed. The association between PTE and D-dimer levels prior to CTPA, and other potential predictors, was analyzed using logistic regression analyses. The optimal cut-off value was identified using receiver operating characteristic (ROC) curve analyses. In 142 patients, PTE prevalence was 20.4%. The optimal cut-off value was 750 ng/mL (sensitivity 100%; specificity 19.5%; negative predictive value 100%; positive predictive value 24.2%). In total, 15 of 113 (13%) patients without PTE had a D-dimer level ≥500 and <750 ng/mL. In our population of patients hospitalized with COVID-19, a D-dimer level <750 ng/mL safely excluded PTE. Compared to the YEARS 500 ng/mL cut-off value, 13% fewer patients are in need of a CTPA, with similar sensitivity. Future research is required for external validation.

Predicted versus CT-derived total lung volume in a general population: The ImaLife study.

Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45-65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.

Developing a Pan-European Technical Standard for a Comprehensive High-quality Lung Cancer CT Screening Program. An ERS Technical Standard.

Screening for lung cancer with low radiation dose computed tomography (LDCT) has a strong evidence base. The European Council adopted a recommendation in November 2022 that lung cancer screening be implemented using a stepwise approach. The imperative now is to ensure that implementation follows an evidence-based process that delivers clinical and cost effectiveness. This ERS Taskforce was formed to provide a technical standard for a high-quality lung cancer screening program. METHOD: A collaborative group was convened to include members of multiple European societies (see below). Topics were identified during a scoping review and a systematic review of the literature was conducted. Full text was provided to members of the group for each topic. The final document was approved by all members and the ERS Scientific Advisory Committee. RESULTS: Ten topics were identified representing key components of a screening program. The action on findings from the LDCT were not included as they are addressed by separate international guidelines (nodule management and clinical management of lung cancer) and by a linked taskforce (incidental findings). Other than smoking cessation, other interventions that are not part of the core screening process were not included (e.g. pulmonary function measurement). Fifty-three statements were produced and areas for further research identified. CONCLUSION: This European collaborative group has produced a technical standard that is a timely contribution to implementation of LCS. It will serve as a standard that can be used, as recommended by the European Council, to ensure a high quality and effective program.

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial.

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%.

CT-based emphysema characterization per lobe: A proof of concept.

PURPOSE: The Fleischner society criteria are global criteria to visually evaluate and classify pulmonary emphysema on CT. It may group heterogeneous disease severity within the same category, potentially obscuring clinically relevant differences in emphysema severity. This proof-of-concept study proposes to split emphysema into more categories and to assess each lobe separately, and applies this to two general population-based cohort samples to assess what information such an extension adds. METHOD: From a consecutive sample in two general population-based cohorts with low-dose chest CT, 117 participants with more than a trace of emphysema were included. Two independent readers performed an extended per-lobe classification and assessed overall severity semi-quantitatively. An emphysema sum score was determined by adding the severity score of all lobes. Inter-reader agreement was quantified with Krippendorff Alpha. RESULTS: Based on Fleischner society criteria, 69 cases had mild to severe centrilobular emphysema, and 90 cases had mild or moderate paraseptal emphysema (42 had both types of emphysema). The emphysema sum score was significantly different between mild (10.7 ± 4.3, range 2-22), moderate (20.1 ± 3.1, range: 15-24), and severe emphysema (23.6 ± 3.4, range: 17-28, p < 0.001), but ranges showed significant overlap. Inter-reader agreement for the extended classification and sum score was substantial (alpha 0.79 and 0.85, respectively). Distribution was homogenous across lobes in never-smokers, yet heterogenous in current smokers, with upper-lobe predominance. CONCLUSIONS: The proposed emphysema evaluation method adds information to the original Fleischner society classification. Individuals in the same Fleischner category have diverse emphysema sum scores, and lobar emphysema distribution differs between smoking groups.

Detection and size quantification of pulmonary nodules in ultralow-dose versus regular-dose CT: a comparative study in COPD patients.

OBJECTIVE: To evaluate detectability and semi-automatic diameter and volume measurements of pulmonary nodules in ultralow-dose CT (ULDCT) vs regular-dose CT (RDCT). METHODS: Fifty patients with chronic obstructive pulmonary disease (COPD) underwent RDCT on 64-multidetector CT (120 kV, filtered back projection), and ULDCT on third-generation dual source CT (100 kV with tin filter, advanced modeled iterative reconstruction). One radiologist evaluated the presence of nodules on both scans in random order, with discrepancies judged by two independent radiologists and consensus reading. Sensitivity of nodule detection on RDCT and ULDCT was compared to reader consensus. Systematic error in semi-automatically derived diameter and volume, and 95% limits of agreement (LoA) were evaluated. Nodule classification was compared by κ statistics. RESULTS: ULDCT resulted in 83.1% (95% CI: 81.0-85.2) dose reduction compared to RDCT (p < 0.001). 45 nodules were present, with diameter range 4.0-25.3 mm and volume range 16.0-4483.0 mm3. Detection sensitivity was non-significant (p = 0.503) between RDCT 88.8% (95% CI: 76.0-96.3) and ULDCT 95.5% (95% CI: 84.9-99.5). No systematic bias in diameter measurements (median difference: -0.2 mm) or volumetry (median difference: -6 mm3) was found for ULDCT compared to RDCT. The 95% LoA for diameter and volume measurements were ±3.0 mm and ±33.5%, respectively. κ value for nodule classification was 0.852 for diameter measurements and 0.930 for volumetry. CONCLUSION: ULDCT based on Sn100 kV enables comparable detectability of solid pulmonary nodules in COPD patients, at 83% reduced radiation dose compared to RDCT, without relevant difference in nodule measurement and size classification. ADVANCES IN KNOWLEDGE: Pulmonary nodule detectability and measurements in ULDCT are comparable to RDCT.

Contextualizing the Role of Volumetric Analysis in Pulmonary Nodule Assessment: AJR Expert Panel Narrative Review.

Pulmonary nodules are managed on the basis of their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by measuring diameter using manually deployed electronic calipers. Size may also be assessed with 3D volumetric measurements (referred to as volumetry) obtained with software. Nodule size and growth are more accurately assessed with volumetry than on the basis of diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions and volumetry software packages can lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European lung cancer screening position statement and British Thoracic Society recommendations. The purposes of this article are to review technical aspects, advantages, and limitations of volumetry and, by considering specific scenarios, to contextualize the use of volumetry with respect to its importance in morphologic evaluation, its role in predicting malignancy in risk models, and its practical impact on nodule management. Implementation challenges and areas requiring further evidence are also highlighted.

CT characteristics of solid pulmonary nodules of never smokers versus smokers: A population-based study.

PURPOSE: Aim was to assess CT characteristics of lung nodules in never and former smokers compared to current smokers in a population-based setting. METHOD: We included individuals aged 45-60 years taking part in the ImaLife (Imaging in Lifelines) study, with at least one solid lung nodule (≥30 mm3) on low-dose chest CT. Qualitative (location, shape, margin, nodule type, attached structures) and quantitative (count, diameter, volume) nodule characteristics were evaluated. Based on Fleischner criteria, 'high risk' nodules were defined. To examine the association between smoking status and nodule CT characteristics of participants, multi-level multinomial logistic regression corrected for clustering of nodules within participants was performed, where all odds ratios (aORs) were adjusted for age and sex. RESULTS: Overall, 1,639 individuals (median age: 55.0, IQR:50.5-58.5, 50.5% men) were included, with 42.1% never smokers, 35.3% former smokers and 22.6% current smokers. A total of 3,222 solid nodules were identified; 39.7% of individuals had multiple nodules. Nodule size, location, type and attachment were similar for never compared to current smokers. The odds of nodules with an irregular shape and irregular margin was lower in never smokers (aOR:0.64, 95 %CI:0.44-0.93; aOR:0.60, 95 %CI:0.41-0.88, respectively) and former smokers (aOR:0.61, 95 %CI:0.41-0.90; aOR:0.57, 95 %CI:0.38-0.85, respectively) compared to current smokers. The odds of a detected nodule being 'high risk' was similar for never versus current smokers (never smokers: aOR = 0.90; 95% CI:0.73-1.11). CONCLUSIONS: CT-based characteristics of solid lung nodules in never and former smokers differed only slightly from current smokers. Among individuals with solid nodules, 'high-risk' nodules were equally common in never smokers and current smokers.

Association between visual emphysema and lung nodules on low-dose CT scan in a Chinese Lung Cancer Screening Program (Nelcin-B3).

OBJECTIVES: This study aimed to evaluate the association between visual emphysema and the presence of lung nodules, and Lung-RADS category with low-dose CT (LDCT). METHODS: Baseline LDCT scans of 1162 participants from a lung cancer screening study (Nelcin-B3) performed in a Chinese general population were included. The presence, subtypes, and severity of emphysema (at least trace) were visually assessed by one radiologist. The presence, size, and classification of non-calcified lung nodules (≥ 30 mm3) and Lung-RADS category were independently assessed by another two radiologists. Multivariable logistic regression and stratified analyses were performed to estimate the association between emphysema and lung nodules, Lung-RADS category, after adjusting for age, sex, BMI, smoking status, pack-years, and passive smoking. RESULTS: Emphysema and lung nodules were observed in 674 (58.0%) and 424 (36.5%) participants, respectively. Participants with emphysema had a 71% increased risk of having lung nodules (adjusted odds ratios, aOR: 1.71, 95% CI: 1.26-2.31) and 70% increased risk of positive Lung-RADS category (aOR: 1.70, 95% CI: 1.09-2.66) than those without emphysema. Participants with paraseptal emphysema (n = 47, 4.0%) were at a higher risk for lung nodules than those with centrilobular emphysema (CLE) (aOR: 2.43, 95% CI: 1.32-4.50 and aOR: 1.60, 95% CI: 1.23-2.09, respectively). Only CLE was associated with positive Lung-RADS category (p = 0.02). CLE severity was related to a higher risk of lung nodules (ranges aOR: 1.44-2.61, overall p < 0.01). CONCLUSION: In a Chinese general population, visual emphysema based on LDCT is independently related to the presence of lung nodules (≥ 30 mm3) and specifically CLE subtype is related to positive Lung-RADS category. The risk of lung nodules increases with CLE severity. KEY POINTS: • Participants with emphysema had an increased risk of having lung nodules, especially smokers. • Participants with PSE were at a higher risk for lung nodules than those with CLE, but nodules in participants with CLE had a higher risk of positive Lung-RADS category. • The risk of lung nodules increases with CLE severity.

Airflow Limitation Increases Lung Cancer Risk in Smokers: The Lifelines Cohort Study.

BACKGROUND: The relationship between smoking, airflow limitation, and lung cancer occurrence is unclear. This study aims to evaluate the relationship between airflow limitation and lung cancer, and the effect modification by smoking status. METHODS: We included participants with spirometry data from Lifelines, a population-based cohort study from the Northern Netherlands. Airflow limitation was defined as FEV1/FVC ratio < 0.7. The presence of pathology-confirmed primary lung cancer during a median follow-up of 9.5 years was collected. The Cox regression model was used and hazard ratios (HR) with 95% confidence interval (95% CI) were reported. Adjusted confounders included age, sex, educational level, smoking, passive smoking, asthma status and asbestos exposure. The effect modification by smoking status was investigated by estimating the relative excess risk due to interaction (RERI) and the ratio of HRs with 95% CI. RESULTS: Out of 98,630 participants, 14,200 (14.4%) had airflow limitation. In participants with and without airflow limitation, lung cancer incidence was 0.8% and 0.2%, respectively. The adjusted HR between airflow limitation and lung cancer risk was 1.7 (1.4-2.3). The association between airflow limitation and lung cancer differed by smoking status [former smokers: 2.1 (1.4-3.2), current smokers: 2.2 (1.5-3.2)] and never smokers [0.9 (0.4-2.1)]. The RERI and ratio of HRs was 2.1 (0.7-3.4) and 2.5 (1.0-6.5) for former smokers, and 4.6 (95% CI, 1.8-7.4) and 2.5 (95% CI, 1.0-6.3) for current smokers, respectively. CONCLUSIONS: Airflow limitation increases lung cancer risk and this association is modified by smoking status. IMPACT: Ever smokers with airflow limitation are an important target group for the prevention of lung cancer.

Association between Chest CT-defined Emphysema and Lung Cancer: A Systematic Review and Meta-Analysis.

Background Given the different methods of assessing emphysema, controversy exists as to whether it is associated with lung cancer. Purpose To perform a systematic review and meta-analysis of the association between chest CT-defined emphysema and the presence of lung cancer. Materials and Methods The PubMed, Embase, and Cochrane databases were searched up to July 15, 2021, to identify studies on the association between emphysema assessed visually or quantitatively with CT and lung cancer. Associations were determined by emphysema severity (trace, mild, or moderate to severe, assessed visually and quantitatively) and subtype (centrilobular and paraseptal, assessed visually). Overall and stratified pooled odds ratios (ORs) with their 95% CIs were obtained. Results Of the 3343 screened studies, 21 studies (107 082 patients) with 26 subsets were included. The overall pooled ORs for lung cancer given the presence of emphysema were 2.3 (95% CI: 2.0, 2.6; I2 = 35%; 19 subsets) and 1.02 (95% CI: 1.01, 1.02; six subsets) per 1% increase in low attenuation area. Studies with visual (pooled OR, 2.3; 95% CI: 1.9, 2.6; I2 = 48%; 12 subsets) and quantitative (pooled OR, 2.2; 95% CI: 1.8, 2.8; I2 = 3.7%; eight subsets) assessments yielded comparable results for the dichotomous assessment. Based on six studies (1716 patients), the pooled ORs for lung cancer increased with emphysema severity and were higher for visual assessment (2.5, 3.7, and 4.5 for trace, mild, and moderate to severe, respectively) than for quantitative assessment (1.9, 2.2, and 2.5) based on point estimates. Compared with no emphysema, only centrilobular emphysema (three studies) was associated with lung cancer (pooled OR, 2.2; 95% CI: 1.5, 3.2; P < .001). Conclusion Both visual and quantitative CT assessments of emphysema were associated with a higher odds of lung cancer, which also increased with emphysema severity. Regarding subtype, only centrilobular emphysema was significantly associated with lung cancer. Clinical trial registration no. CRD42021262163 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hunsaker in this issue.

Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research.

Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Successful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification.

OBJECTIVE: To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists. METHODS: 283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3. RESULTS: 1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively. CONCLUSION: Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.

Lung cancer screening with low-dose CT: Simulating the effect of starting screening at a younger age in women.

BACKGROUND: The US has recently lowered the entry age for lung cancer screening with low-dose computed tomography (LDCT) from 55 to 50 years. The effect of the younger age for starting screening on the rates of screen-detected and radiation-induced lung cancers in women remains unclear. METHODS: A modeling study was conducted. A static cohort of 100,000 heavy female smokers was simulated to undergo annual lung cancer screening with LDCT. The number of screen-detected lung cancers (benefit) and radiation-induced lung cancers (harm) per 1000 screenees were calculated for scenarios with two starting ages (55-50 years) and fixed stopping age (75 years). The benefit-harm ratio and incremental benefit-harm ratio (IBHR) were calculated for each scenario. RESULTS: For annual screening from 55 to 75 years, the number of screen-detected and radiation-induced lung cancers was 112.4 and 2.2, respectively. For annual screening from 50 to 75 years, those numbers were 117.0 and 3.4, respectively. The benefit-harm ratio decreased from 51 to 35 and the IBHR decreased from 6.3 to 4.0 when lowering the screening starting age from 55 to 50 years. CONCLUSIONS: The risk of radiation induced lung cancers increased by 50% when lowering the screening starting age by 5 years in women. However, the benefits of LDCT lung cancer screening still outweigh the assumed radiation harm.