Validity of Atherosclerotic Calcified Lesions Observed on Low-Dose Computed Tomography and Cardio-Ankle Vascular Index as Surrogate Markers of Atherosclerosis Progression

The significance of atherosclerotic calcified lesions observed on low-dose computed tomography (LDCT) performed during general checkups was investigated. The coronary arteries (CA), ascending aorta and aortic arch (AAAA), descending thoracic aorta (DTA), and abdominal aorta (AA) were examined. Semiquantitative calcified index analysis of the DTA and AA in terms of atherosclerosis risk factors and cardio-ankle vascular index (CAVI) measurements was also performed. We included 1594 participants (mean age: 59.2 years; range: 31-91 years). The prevalence of calcified lesions was 71.0%, 66.6%, 57.2%, and 37.9% in the AA, CA, AAAA, and DTA, respectively. Age-related advances in calcification among participants with no major risk factors, revealed that calcification appeared earliest in the AA, followed by the CA, AAAA, and DTA. Participants with calcified lesions in all arteries had a significantly greater CAVI than those without calcification. The CAVI was negatively correlated with low-density lipoprotein cholesterol levels, particularly in participants without calcified lesions in the DTA. Calcified lesions on LDCT could indicate the end stage of atherosclerotic lesions. The CAVI can be used to assess atherosclerotic changes at all stages of disease progression. A combination of LDCT and CAVI could be used as a routine non-invasive assessment of atherosclerosis.

Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland

Throughout Europe, computed tomography (CT) screening for lung cancer is in a phase of clinical implementation or reimbursement evaluation. To efficiently select individuals for screening, the use of lung cancer risk models has been suggested, but their incremental (cost-)effectiveness relative to eligibility based on pack-year criteria has not been thoroughly evaluated for a European setting. We evaluate the cost-effectiveness of pack-year and risk-based screening (PLCOm2012 model-based) strategies for Switzerland, which aided in informing the recommendations of the Swiss Cancer Screening Committee (CSC). We use the MISCAN (MIcrosimulation SCreening ANalysis)-Lung model to estimate benefits and harms of screening among individuals born 1940 to 1979 in Switzerland. We evaluate 1512 strategies, differing in the age ranges employed for screening, the screening interval and the strictness of the smoking requirements. We estimate risk-based strategies to be more cost-effective than pack-year-based screening strategies. The most efficient strategy compliant with CSC recommendations is biennial screening for ever-smokers aged 55 to 80 with a 1.6% PLCOm2012 risk. Relative to no screening this strategy is estimated to reduce lung cancer mortality by 11.0%, with estimated costs per Quality-Adjusted Life-Year (QALY) gained of €19 341, and a €1.990 billion 15-year budget impact. Biennial screening ages 55 to 80 for those with 20 pack-years shows a lower mortality reduction (10.5%) and higher cost per QALY gained (€20 869). Despite model uncertainties, our estimates suggest there may be cost-effective screening policies for Switzerland. Risk-based biennial screening ages 55 to 80 for those with ≥1.6% PLCOm2012 risk conforms to CSC recommendations and is estimated to be more efficient than pack-year-based alternatives.

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

BACKGROUND

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

An unsupervised two-step training framework for low-dose computed tomography denoising

BACKGROUND

Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT images tend to have more noise, which impedes accurate diagnosis. Recently, deep neural networks using convolutional neural networks to reduce noise in the reconstructed low-dose CT images have shown considerable improvement. However, they need a large number of paired normal- and low-dose CT images to fully train the network via supervised learning methods.

PURPOSE

To propose an unsupervised two-step training framework for image denoising that uses low-dose CT images of one dataset and unpaired high-dose CT images from another dataset.

METHODS

Our proposed framework trains the denoising network in two steps. In the first training step, we train the network using 3D volumes of CT images and predict the center CT slice from them. This pre-trained network is used in the second training step to train the denoising network and is combined with the memory-efficient denoising generative adversarial network (DenoisingGAN), which further enhances both objective and perceptual quality.

RESULTS

The experimental results on phantom and clinical datasets show superior performance over the existing traditional machine learning and self-supervised deep learning methods, and the results are comparable to the fully supervised learning methods.

CONCLUSIONS

We proposed a new unsupervised learning framework for low-dose CT denoising, convincingly improving noisy CT images from both objective and perceptual quality perspectives. Because our denoising framework does not require physics-based noise models or system-dependent assumptions, our proposed method can be easily reproduced; consequently, it can also be generally applicable to various CT scanners or dose levels.

Screening for lung cancer: 2023 guideline update from the American Cancer Society

Lung cancer is the leading cause of mortality and person-years of life lost from cancer among US men and women. Early detection has been shown to be associated with reduced lung cancer mortality. Our objective was to update the American Cancer Society (ACS) 2013 lung cancer screening (LCS) guideline for adults at high risk for lung cancer. The guideline is intended to provide guidance for screening to health care providers and their patients who are at high risk for lung cancer due to a history of smoking. The ACS Guideline Development Group (GDG) utilized a systematic review of the LCS literature commissioned for the US Preventive Services Task Force 2021 LCS recommendation update; a second systematic review of lung cancer risk associated with years since quitting smoking (YSQ); literature published since 2021; two Cancer Intervention and Surveillance Modeling Network-validated lung cancer models to assess the benefits and harms of screening; an epidemiologic and modeling analysis examining the effect of YSQ and aging on lung cancer risk; and an updated analysis of benefit-to-radiation-risk ratios from LCS and follow-up examinations. The GDG also examined disease burden data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Formulation of recommendations was based on the quality of the evidence and judgment (incorporating values and preferences) about the balance of benefits and harms. The GDG judged that the overall evidence was moderate and sufficient to support a strong recommendation for screening individuals who meet the eligibility criteria. LCS in men and women aged 50-80 years is associated with a reduction in lung cancer deaths across a range of study designs, and inferential evidence supports LCS for men and women older than 80 years who are in good health. The ACS recommends annual LCS with low-dose computed tomography for asymptomatic individuals aged 50-80 years who currently smoke or formerly smoked and have a ≥20 pack-year smoking history (strong recommendation, moderate quality of evidence). Before the decision is made to initiate LCS, individuals should engage in a shared decision-making discussion with a qualified health professional. For individuals who formerly smoked, the number of YSQ is not an eligibility criterion to begin or to stop screening. Individuals who currently smoke should receive counseling to quit and be connected to cessation resources. Individuals with comorbid conditions that substantially limit life expectancy should not be screened. These recommendations should be considered by health care providers and adults at high risk for lung cancer in discussions about LCS. If fully implemented, these recommendations have a high likelihood of significantly reducing death and suffering from lung cancer in the United States.

Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement

RATIONALE AND OBJECTIVES

Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules.

MATERIALS AND METHODS

A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic.

RESULTS

The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05).

CONCLUSION

The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

A multi-institutional assessment of low-dose protocols in chest computed tomography: Dose and image quality

Background

Low-dose CT (LDCT) chest protocols have widespread clinical applications for many indications; as a result, there is a need for protocol assessment prior to standardization. Dalhousie University and Oslo Metropolitan University have a formally established cooperative relationship.

Purpose

The purpose is to assess radiation dose and image quality for LDCT chest protocols in seven different hospital locations in Norway and Canada.

Material and methods

Retrospective dosimetry data, volumetric CT dose index (CTDIvol), and dose length product (DLP) from 240 average-sized patients as well as CT protocol parameters were included in the survey. Effective dose (ED) and size-specific dose estimate (SSDE) were calculated for each examination. For a quantitative image quality analysis, noise, CT number, and signal-to-noise ratio (SNR) were determined for three regions in the chest. The contrast-to-noise ratio (CNR) was calculated for lung parenchyma in comparison to the subcutaneous fat. Differences in dose and image quality were evaluated by a single-factor ANOVA test. A two-sample t-test was performed to determine differences in means between individual scanners.

Results

The ANOVA test revealed significant differences (p < .05) in dose values for all scanners, including identical scanner models. Statistically significant differences (p < .05) were determined in mean values of the SNR distributions between the scanners in all three measured regions in the chest, as well as the CNR values.

Conclusion

The observed variations in dose and image quality measurements, even within the same hospitals and between identical scanner models, indicate a potential for protocol optimization in the involved hospitals in both countries.

Results from Lung Cancer Screening Outreach Utilizing a Mobile CT Scanner in an Urban Area

INTRODUCTION

Lung cancer screening using low-dose (LD) CT reduces lung cancer-specific and all-cause mortality in high-risk individuals, although significant barriers to screening remain. We assessed the outreach of a mobile lung cancer screening program to increase screening accessibility and early detection of lung cancer.

METHODS

We placed a mobile CT unit in a high-traffic area in New York City and offered free screening to all eligible patients. Characteristics of the mobile screening cohort were compared with those of our hospital-based screening cohort.

RESULTS

Between December 9, 2019, and January 30, 2020, a total of 216 patients underwent mobile LDCT screening. Compared with the hospital-based screening cohort, mobile screening participants were significantly more likely to be younger, be uninsured, and have lower smoking intensity and were less likely to meet 2013 US Preventive Services Task Force guidelines (but would meet their 2021 guidelines) and self-identify as White race and Hispanic ethnicity. Asian New Yorkers were substantially underrepresented in both hospital and mobile screening cohorts, compared with their level of representation in New York City. Two patients were diagnosed with lung cancer and were treated. Potentially clinically significant non-lung cancer findings were identified in 28.2%, most commonly moderate-severe coronary artery calcification and emphysema.

CONCLUSIONS

Mobile LDCT screening is useful and effective in detecting lung cancer and other significant findings and may engage a distinct high-risk patient demographic. Disproportionately low screening rates among certain high-risk populations highlight the imperative of implementing strategies aimed at understanding health behaviors and access barriers for diverse populations. Effective care-navigation services, facilitating high-quality care for all patients, are critical.

Lung cancer screening utilization rate varies based on patient, provider, and hospital factors

OBJECTIVE

Low-dose computed tomography has been proven to reduce mortality, yet utilization remains low. The purpose of this study is to identify factors that impact the utilization of lung cancer screening.

METHODS

We performed a retrospective review of our institution's primary care network from November 2012 to June 2022 to identify patients who were eligible for lung cancer screening. Eligible patients were 55 to 80 years of age and current or former smokers with at least a 30 pack-year history. Analyses were performed on the screened populations and patients who met eligibility criteria but were not screened.

RESULTS

A total of 35,279 patients in our primary care network were current/former smokers aged 55 to 80 years. A total of 6731 patients (19%) had a 30 pack-year or more cigarette history, and 11,602 patients (33%) had an unknown pack-year history. A total of 1218 patients received low-dose computed tomography. The utilization rate of low-dose computed tomography was 18%. The utilization rate was significantly lower (9%) if patients with unknown pack-year history were included (P < .001). The utilization rates between primary care clinic locations were significantly different (range, 18% vs 41%, P < .05). Utilization of low-dose computed tomography on multivariate analysis was associated with Black race, former smoker, chronic obstructive pulmonary disease, bronchitis, family history of lung cancer, and number of primary care visits (all P < .05).

CONCLUSIONS

Lung cancer screening utilization rates are low and vary significantly on the basis of patient comorbidities, family history of lung cancer, primary care clinic location, and accurate documentation of pack-year cigarette history. The development of programs to address patient, provider, and hospital-level factors is needed to ensure appropriate lung cancer screening.

An Intramyocardial Lipoma Mimicking Post-infarction Fatty Changes: Discussion of Key Distinguishing Imaging Findings and Clinical Implications

Cardiac lipomas are benign primary cardiac tumors that are most often asymptomatic and diagnosed incidentally. Cardiac magnetic resonance imaging (MRI) is the imaging modality of choice when aiming to characterize these tumors. A minority of cardiac lipomas are intramyocardial, which, when combined with the much more common post-infarction fatty metaplasia, makes diagnosing these lipomas very challenging. We review a case of intramyocardial lipoma in the distal interventricular septum that was initially detected on a low-dose computed tomography for lung cancer screening and the subsequent findings on cardiac MRI that made the diagnosis. Additionally, this case also helps to support the conservative management of intramyocardial lipomas that are more distal in the left ventricle and subsequently at lower risk for conduction arrhythmias.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study

PURPOSE

To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.

METHODS

A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.

RESULTS

With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs.

CONCLUSION

Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Impact of low-dose computed tomography screening on lung cancer incidence and outcomes

PURPOSE OF REVIEW

To review findings from clinical trials of lung cancer screening (LCS), assess contemporary issues with implementation in clinical practice, and review emerging strategies to increase the uptake and efficiency of LCS.

RECENT FINDINGS

In 2013, the USPSTF recommended annual screening for individuals aged 55-80 years and currently smoke or quit within the past 15 years based on reduced mortality from lung cancer with annual low-dose computed tomography (LDCT) screening in the National Lung Screening Trial. Subsequent trials have demonstrated similar mortality outcomes in individuals with lower pack-year smoking histories. These findings, coupled with evidence for disparities in screening eligibility by race, resulted in updated guidelines by USPSTF to broaden eligibility criteria for screening. Despite this body of evidence, implementation in the United States has been suboptimal with fewer than 20% of eligible individuals receiving a screen. Barriers to efficient implementation are multifactorial and include patient, clinician, and system-level factors.

SUMMARY

Multiple randomized trials have established that annual LCS reduces mortality from lung cancer; however, several areas of uncertainty exist on the effectiveness of annual LDCT. Ongoing research is examining approaches to improve the uptake and efficiency of LCS, such as the use of risk-prediction models and biomarkers for identification of high-risk individuals.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

BACKGROUND

Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology.

METHODS

The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model.

RESULTS

A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively.

CONCLUSIONS

It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.

Initial low-dose computed tomography screening results and summary of participant characteristics: based on the latest Chinese guideline

Background

Low-dose computed tomography (LDCT) has been promoted as a promising screening strategy for early detection of lung cancer. China released the latest lung cancer screening guideline in 2021. The compliance of the individuals who received LDCT for lung cancer screening with the guideline is unknown yet. It is necessary to summarize the distribution of guideline-defined lung cancer-related risk factors in the Chinese population so as to inform the selection of target population for the future lung cancer screening.

Methods

A single-center, cross-sectional study design was adopted. All participants were individuals who underwent LDCT at a tertiary teaching hospital in Hunan, China, between 1 January and 31 December 2021. LDCT results were derived along with guideline-based characteristics for descriptive analysis.

Results

A total of 5,486 participants were included. Over one-quarter (1,426, 26.0%) of the participants who received screening did not meet the guideline-defined high-risk population, even among non-smokers (36.4%). Most of the participants (4,622, 84.3%) were found to have lung nodules, while no clinical intervention was required basically. The detection rate of positive nodules varied from 46.8% to 71.2% when using different cut-off values for positive nodules. Among non-smoking women, ground glass opacity appeared to be more significantly common compared with non-smoking men (26.7% vs. 21.8%).

Conclusion

Over one-quarter of individuals who received LDCT screening did not meet the guideline-defined high-risk populations. Appropriate cut-off values for positive nodules need to be continuously explored. More precise and localized criteria for high-risk individuals are needed, especially for non-smoking women.

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuses on the optimization of the prospectively ECG-triggered scan technique, which delivers the radiation efficiently only during a fraction of the R-R interval, matching the aim of reducing radiation dose in this increasingly used radiological examination. In this work, we analyzed how the median DLP (Dose-Length Product) values for CCTA of our Center decreased significantly in recent times mainly due to a notable change in the technology used. We passed from a median DLP value of 1158 mGy·cm to 221 mGy·cm for the whole exam and from a value of 1140 mGy·cm to 204 mGy·cm if considering CCTA scanning only. The result was obtained through the association of important factors during the dose imaging optimization: technological improvement, acquisition technique, and image reconstruction algorithm intervention. The combination of these three factors allows us to perform a faster and more accurate prospective CCTA with a lower radiation dose. Our future aim is to tune the image quality through a detectability-based study, combining algorithm strength with automatic dose settings.

Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis

BACKGROUND

Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.

METHODS

MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test.

RESULTS

A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies.

CONCLUSIONS

Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.

Peripheral interstitial lung abnormalities on LDCT in an asymptomatic, nonsmoking Chinese urban cohort

To retrospectively investigate the imaging features and the related influencing factors of peripheral interstitial lung abnormalities (PILA) that caused "normal aging" by low-dose computed tomography (LDCT) in an nonsmoking, asymptomatic Chinese urban cohort. The clinical data of 733 subjects who underwent chest LDCT were retrospectively collected. The computed tomography (CT) signs of PILA (interlobular septal thickening [ILST], intralobular interstitial thickening [ILIT], ground-glass opacity [GGO], reticular shadow [RS], subpleural line [SL]) were evaluated at 6 levels and statistically analyzed. The effects of age, sex, body mass index (BMI), blood pressure (BP), and blood biochemistry parameters on ILST, ILIT, and RS were analyzed by Binary Logistic regression analysis. Significant age differences in PILA were found. None of the 5 PILA CT signs (GGO, ILST, ILIT, RS, and SL) was observed in subjects under 40 years old, while in subjects over 40 years old, the incidence of PILA increased with age. All 5 CT signs of PILA were significantly different among the subjects aged 18 to 49, 50 to 69, and 70 to 79 (P < .05). There was no significant sex difference in PILA. Among age, sex, BMI, BP, and laboratory biochemistry parameters, only age had a significant effect on ILST, ILIT, and RS. LDCT can be used as a noninvasive method to evaluate the PILA. PILA were mainly affected by age, while sex, BMI, BP, and laboratory biochemistry parameters had little effect on PILA. PILA observed before the age of 40 years should be considered an abnormal finding, whereas it is common in individuals over 70.

Development of a Novel Circulating Autoantibody Biomarker Panel for the Identification of Patients with 'Actionable' Pulmonary Nodules

Due to poor compliance and uptake of LDCT screening among high-risk populations, lung cancer is often diagnosed in advanced stages where treatment is rarely curative. Based upon the American College of Radiology's Lung Imaging and Reporting Data System (Lung-RADS) 80-90% of patients screened will have clinically "non-actionable" nodules (Lung-RADS 1 or 2), and those harboring larger, clinically "actionable" nodules (Lung-RADS 3 or 4) have a significantly greater risk of lung cancer. The development of a companion diagnostic method capable of identifying patients likely to have a clinically actionable nodule identified during LDCT is anticipated to improve accessibility and uptake of the paradigm and improve early detection rates. Using protein microarrays, we identified 501 circulating targets with differential immunoreactivities against cohorts characterized as possessing either actionable (n = 42) or non-actionable (n = 20) solid pulmonary nodules, per Lung-RADS guidelines. Quantitative assays were assembled on the Luminex platform for the 26 most promising targets. These assays were used to measure serum autoantibody levels in 841 patients, consisting of benign (BN; n = 101), early-stage non-small cell lung cancer (NSCLC; n = 245), other early-stage malignancies within the lung (n = 29), and individuals meeting United States Preventative Screening Task Force (USPSTF) screening inclusion criteria with both actionable (n = 87) and non-actionable radiologic findings (n = 379). These 841 patients were randomly split into three cohorts: Training, Validation 1, and Validation 2. Of the 26 candidate biomarkers tested, 17 differentiated patients with actionable nodules from those with non-actionable nodules. A random forest model consisting of six autoantibody (Annexin 2, DCD, MID1IP1, PNMA1, TAF10, ZNF696) biomarkers was developed to optimize our classification performance; it possessed a positive predictive value (PPV) of 61.4%/61.0% and negative predictive value (NPV) of 95.7%/83.9% against Validation cohorts 1 and 2, respectively. This panel may improve patient selection methods for lung cancer screening, serving to greatly reduce the futile screening rate while also improving accessibility to the paradigm for underserved populations.

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Background

Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images.

Methods

In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body.

Results

The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and - 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = -6.15% ± 2.24%.

Conclusions

The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.

Multidimensional analysis using low-dose computed tomography to evaluate the severity of Mycoplasma pneumoniae pneumonia in children

Background

It is unclear whether local pathological pulmonary changes truly reflect the severity of childhood Mycoplasma pneumoniae infection, which is characterized by rapid progress and potential mortality. This study multi-dimensionally analyzed low-dose computed tomography findings to assess the severity of Mycoplasma pneumoniae infection and predict its progress in such patients.

Methods

In all, 752 children with Mycoplasma pneumoniae pneumonia (MPP) who underwent low-dose computed tomography examinations from February 2016 to July 2020 were retrospectively enrolled to conduct a cohort study. Clinical and radiological variables were analyzed using univariate analysis, and radiological variables were further analyzed using multivariable logistic regression in severe cases. Then, the correlation between the key computed tomography features and clinical symptoms, laboratory indicators, and medical costs were assessed using the chi-squared and Kruskal-Wallis H tests. Kaplan-Meier curves and Cox regression models were created to evaluate the correlations between the key computed tomography features, fever duration, and the length of hospital stay.

Results

Of the 752 included patients, 16.2% (122/752) developed severe MPP. Atelectasis, pleural effusion, and lung consolidation occurred in 9.7% (73/752), 15.8% (119/752), and 90.3% (679/752) of patients, respectively. In addition to pleural effusion, the number of lobes of lung consolidation was the highest risk feature of severe MPP. Patients with consolidation in 2, 3, and 4 lobes had a 1.0-, 3.1-, and 7.5-fold increased risk of severe MPP, compared with patients with consolidation in fewer than 1 lobe. The duration of fever prior to admission had no effect on the proportions of the lobar consolidation (P=0.14) but did have significant effect on the incidence of pleural effusion (P=0.004). Levels of inflammatory markers and medical costs rose consistently with the increase in the number of lobar consolidations (P<0.001). After adjustments for pleural effusion, 1, 2, 3, and 4 lobes of consolidation remained positively associated with fever duration [1 lobe: hazard ratio (HR) =1.55, 95% CI: 1.10-2.18; 2 lobes: HR =1.65, 95% CI: 1.13-2.42l; 3 lobes: HR =1.82, 95% CI: 1.11-2.98; 4 lobes: HR =2.87, 95% CI: 1.25-6.61] compared to 0 lobes of consolidation. Compared to 0 lobes of consolidation, 1, 2, 3, and 4 lobes of consolidation were also positively correlated with the length of hospital stay (1 lobe: HR =2.24, 95% CI: 1.73-2.89; 2 lobes: HR =2.56, 95% CI: 1.91-3.43; 3 lobes: HR =2.87, 95% CI: 1.90-4.32; 4 lobes: HR =4.12, 95% CI: 2.01-8.46).

Conclusions

Lobar consolidation is a stable and reliable computed tomography feature that can be used to assess the severity of MPP in children. Quantitative analysis of lobar consolidation can comprehensively and accurately predict the progression of Mycoplasma pneumoniae. Low-dose computed tomography is recommended for children with severe MPP with complicated courses.

The prevalence of comorbidity in the lung cancer screening population: A systematic review and meta-analysis

OBJECTIVE

Comorbidity is associated with adverse outcomes for all lung cancer patients, but its burden is less understood in the context of screening. This review synthesises the prevalence of comorbidities among lung cancer screening (LCS) candidates and summarises the clinical recommendations for screening comorbid individuals.

METHODS

We searched MEDLINE, EMBASE, EBM Reviews, and CINAHL databases from January 1990 to February 2021. We included LCS studies that reported a prevalence of comorbidity, as a prevalence of a particular condition, or as a summary score. We also summarised LCS clinical guidelines that addressed comorbidity or frailty for LCS as a secondary objective for this review. Meta-analysis was used with inverse-variance weights obtained from a random-effects model to estimate the prevalence of selected comorbidities.

RESULTS

We included 69 studies in the review; seven reported comorbidity summary scores, two reported performance status, 48 reported individual comorbidities, and 12 were clinical guideline papers. The meta-analysis of individual comorbidities resulted in an estimated prevalence of 35.2% for hypertension, 23.5% for history of chronic obstructive pulmonary disease (COPD) (10.7% for severe COPD), 16.6% for ischaemic heart disease (IHD), 13.1% for peripheral vascular disease (PVD), 12.9% for asthma, 12.5% for diabetes, 4.5% for bronchiectasis, 2.2% for stroke, and 0.5% for pulmonary fibrosis.

CONCLUSIONS

Comorbidities were highly prevalent in LCS populations and likely to be more prevalent than in other cancer screening programmes. Further research on the burden of comorbid disease and its impact on screening uptake and outcomes is needed. Identifying individuals with frailty and comorbidities who might not benefit from screening should become a priority in LCS research.

The impact of low-dose CT on smoking behavior among non-smokers, former-smokers, and smokers: A population-based screening cohort in rural China

BACKGROUND

Lung cancer screening may provide a "teachable moment" for the smoking cessation and relapse prevention. However, the impact of lung cancer screening on smoking initiation in non-smokers has not been reported.

METHODS

A baseline smoking behavior survey was conducted in 2000 participants who were screened by low-dose computed tomography (LDCT) from 2014 to 2018. All participants were re-surveyed on their smoking behavior in 2019. Of these, 312 participants were excluded, leaving 1688 participants in the final analysis. The smoking initiation rate in baseline non-smokers, the relapse rate in baseline former smokers, and the abstinence rate in baseline current smokers were calculated, respectively. The associations between screening results, demographic characteristics, and smoking behavior change were analyzed using multivariable logistic regression.

RESULTS

From 2014 to 2019, smoking prevalence significantly decreased from 52.6% to 49.1%. The prevalence of smoking initiation, relapse, and abstinence in baseline non-smokers, former, and current smokers was 16.8%, 22.9%, and 23.7%, respectively. The risk of smoking initiation in baseline non-smokers was significantly higher in those with negative screening result (adjusted OR = 2.97, 95% CI: 1.27-6.94). Compared to smokers who only received baseline screening, the chance of smoking abstinence in baseline current smokers was reduced by over 80% in those who attended 5 rounds of screening (adjusted OR = 0.15, 95% CI:0.08-0.27). No significant associations were found between smoking relapse and prior screening frequency, with at least one positive screening result. Age, gender, occupational exposure, income, and smoking pack years were also associated with smoking behavior changes.

CONCLUSIONS

The overall decreased smoking prevalence indicated an overwhelming effect of "teachable moment" on "license to smoke." A tailored smoking cessation strategy should be integrated into lung cancer screening.

Assessment of Calcium Score Cutoff Point for Clinically Significant Aortic Stenosis on Lung Cancer Screening Program Low-Dose Computed Tomography-A Cross-Sectional Analysis

Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected in lung cancer screenees. Therefore, initial aortic valve calcification evaluation should be additionally performed on LDCT. The aim of this study was to estimate a calcium score (CS) cutoff point for clinically significant AS diagnosis based on LDCT, confirmed by echocardiographic examination. The study included 6631 heavy smokers who participated in a lung cancer screening program (MOLTEST BIS). LDCTs were performed on all individuals and were additionally assessed for aortic valve calcification with the use of CS according to the Agatston method. Patients with CS ≥ 900 were referred for echocardiography to confirm the diagnosis of AS and to evaluate its severity. Of 6631 individuals, 54 met the inclusion criteria and underwent echocardiography for confirmation and assessment of AS. Based on that data, receiver operating characteristic (ROC) curves of CS were plotted, and cutoff points for clinically significant AS diagnosis were established: A CS of 1758 for at least moderate AS had 85.71% (CI 65.36-95.02%) sensitivity and 75.76% (CI 58.98-87.17%) specificity; a CS of 2665 for severe AS had 87.5% (CI 73.89-94.54%) sensitivity and 76.92% (CI 49.74-91.82%) specificity. This is the first study to assess possible CS cutoff points for diagnosing clinically significant AS detected by LDCT in lung cancer screening participants. LDCT with CS assessment could enable early detection of patients with clinically significant AS and therefore identify patients who require appropriate treatment.

Correlation between lung cancer probability and number of pulmonary nodules in baseline computed tomography lung cancer screening: A retrospective study based on the Chinese population

Background

Screening for lung cancer with LDCT detects a large number of nodules. However, it is unclear whether nodule number influences lung cancer probability. This study aimed to acquire deeply insight into the distribution characteristics of nodule number in the Chinese population and to reveal the association between the nodule number and the probability of lung cancer (LC).

Methods

10,167 asymptomatic participants who underwent LDCT LC screening were collected. Noncalcified nodules larger than 4 mm were included. The nodule number per participant was determined. We defined five categories according to the number of nodules (based on nodule type and size): one, two, three, four, and more than four nodules. We stratified the nodules as groups A, B, and C and participants as Amax, Bmax, and Cmax groups, and explored the association between nodule number and the probability of LC on nodule and participant levels.

Results

97 participants were confirmed to have LC. The probabilities of LC were 49/1719, 22/689, 11/327, 6/166, and 9/175 in participants with one, two, three, four, and more than four nodules (p>0.05), respectively. In the Bmax group, the probability of LC was significantly higher in participants with one nodule than those with >4 nodules (p<0.05), and the probability of LC showed a negative linear trend with increasing nodule numbers (p<0.05). Based on the nodule-level analyses, in Group B, LC probability was significantly higher when participants had a solitary nodule than when they had >4 nodules (p<0.05).

Conclusion

LC probability does not significantly change with the number of nodules. However, when stratified by the nodule size, the effect of nodule number on LC probability was nodule-size dependent, and greater attention and active follow-up are required for solitary nodules especially SNs/solid component of PSNs measuring 6-15 mm or NSNs measuring 8-15 mm. Assessing the nodule number in conjunction with nodule size in baseline LDCT LC screening is considered beneficial.

Special issue "The advance of solid tumor research in China": Participants with a family history of cancer have a higher participation rate in low-dose computed tomography for lung cancer screening

We aimed to determine participation in low-dose computed tomography (LDCT) of individuals with a family history of common cancers in a population-based screening program to provide timely evidence in high-risk populations in China. The analysis was conducted using data from the Cancer Screening Program in Urban China (CanSPUC), which recruited 282 377 participants aged 40 to 74 years from eight cities in the Henan province. Using the CanSPUC risk score system, 55 428 participants were evaluated to have high risk for lung cancer and were recommended for LDCT. We calculated the overall and group-specific participation rates using family history of common cancers and compared differences in participation rates between different groups. Odds ratios (ORs) and 95% confidence intervals were derived by multivariable logistic regression. Of the 55 428 participants, 22 260 underwent LDCT (participation rate, 40.16%). Family history of lung, esophageal, stomach, liver and colorectal cancer was associated with increased participation in LDCT screening. The odds of participants with a family history of one, two, three and four or more cancer cases undergoing LDCT screening were 1.9, 2.7, 2.8 and 3.5 times, respectively, than those without a family history of cancer. Compared to those without a history of cancer, participation in LDCT gradually increased as the number of cancer cases in the family increased (P < .001). Our findings suggest that there is room for improvement in lung cancer screening given the relatively low participation rate. Lung cancer screening in populations with a family history of cancer may improve efficiency and cost-effectiveness; however, this requires further verification.

Proactive Recruitment Strategy for Patient Identification for Lung Cancer Screening

PURPOSE

We assessed low-dose computed tomography (LDCT) screening for lung cancer using a proactive patient education/recruitment program.

METHODS

We identified patients aged 55-80 years from a family medicine group. In the retrospective phase (March-August, 2019), patients were categorized as current/former/never smokers, and screening eligibility was determined. Patients who underwent LDCT in the past year, along with outcomes, were documented. In the prospective phase (2020), patients in the same cohort who did not undergo LDCT were proactively contacted by a nurse navigator to discuss eligibility and prescreening. Eligible and willing patients were referred to their primary care physician.

RESULTS

In the retrospective phase, of 451 current/former smokers, 184 (40.8%) were eligible for LDCT, 104 (23.1%) were ineligible, and 163 (36.1%) had an incomplete smoking history. Of those eligible, 34 (18.5%) had LDCT ordered. In the prospective phase, 189 (41.9%) were eligible for LDCT (150 [79.4%] of whom had no prior LDCT or diagnostic CT), 106 (23.5%) were ineligible, and 156 (34.6%) had an incomplete smoking history. The nurse navigator identified an additional 56/451 (12.4%) patients as eligible after contacting patients with incomplete smoking history. In total, 206 patients (45.7%) were eligible, an increase of 37.3% compared with the retrospective phase (150). Of these, 122 (59.2%) verbally agreed to screening, 94 (45.6%) met with their physician, and 42 (20.4%) were prescribed LDCT.

CONCLUSIONS

A proactive education/recruitment model increased eligible patients for LDCT by 37.3%. Proactive identification/education of patients desiring to pursue LDCT was 59.2%. It is essential to identify strategies that will increase and deliver LDCT screening among eligible and willing patients.

Low-dose computed tomography for lung cancer screening in Anhui, China: A randomized controlled trial

Background

Lung cancer is the leading cause of cancer-related death worldwide, with risk factors such as age and smoking. Low-dose computed tomography screening can reduce lung cancer mortality. However, its effectiveness in Asian populations remains unclear. Most Asian women with lung cancer are non-smokers who have not been screened. We conducted a randomized controlled trial to evaluate the performance of low-dose computed tomography screening in a Chinese population, including high-risk smokers and non-smokers exposed to passive smoking. The baseline data are reported in this study.

Methods

Between May and December 2019, eligible participants were randomized in a ratio of 1:1:1 to a screening (two arms) or control cohort. Non-calcified nodules/masses with a diameter >4 mm on low-dose computed tomography were considered positive findings.

Results

In total, 600 patients (mean age, 59.1 ± 6.9 years) underwent low-dose computed tomography. Women accounted for 31.5% (189/600) of patients; 89.9% (170/189) were non-smokers/passive smokers. At baseline, the incidence of lung cancer was 1.8% (11/600). The incidence of lung cancer was significantly lower in smokers than in female non-smokers/passive smokers (1.0% [4/415] vs. 4.1% [7/170], respectively; P=0.017). Stage 0-I lung cancer accounted for 90.9% (10/11) of cases.

Conclusions

We demonstrate the importance of including active smokers and female non-smokers/passive smokers in lung cancer screening programs. Further studies are needed to explore the risk factors, and long-term cost-benefit of screening Asian non-smoking women.

Clinical trial registration

http://chictr.org.cn/showproj.aspx?proj=39003, identifier ChiCTR1900023197.

Structure-preserved meta-learning uniting network for improving low-dose CT quality

Objective.Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are usually designed based on simple statistical models which deviate from the real clinical scenarios, which could lead to issues of overfitting, instability and poor robustness. To address these issues, in this work, we present a structure-preserved meta-learning uniting network (shorten as 'SMU-Net') to suppress noise-induced artifacts and preserve structure details in the unlabeled LDCT imaging task in real scenarios.Approach.Specifically, the presented SMU-Net contains two networks, i.e., teacher network and student network. The teacher network is trained on simulated labeled dataset and then helps the student network train with the unlabeled LDCT images via the meta-learning strategy. The student network is trained on real LDCT dataset with the pseudo-labels generated by the teacher network. Moreover, the student network adopts the Co-teaching strategy to improve the robustness of the presented SMU-Net.Main results.We validate the proposed SMU-Net method on three public datasets and one real low-dose dataset. The visual image results indicate that the proposed SMU-Net has superior performance on reducing noise-induced artifacts and preserving structure details. And the quantitative results exhibit that the presented SMU-Net method generally obtains the highest signal-to-noise ratio (PSNR), the highest structural similarity index measurement (SSIM), and the lowest root-mean-square error (RMSE) values or the lowest natural image quality evaluator (NIQE) scores.Significance.We propose a meta learning strategy to obtain high-quality CT images in the LDCT imaging task, which is designed to take advantage of unlabeled CT images to promote the reconstruction performance in the LDCT environments.

Evaluation of a lung cancer screening programme attracting a cohort to actively participate in screening: Honghe Lung Cancer Medical Center Programme

Background

A lung cancer screening project was conducted by attracting active participation to evaluate its feasibility and effectiveness in areas with poor basic medical education.

Methods

This project entailed a prospective, single-arm study which was conducted by means of delivering a lecture on lung cancer at the Honghe Lung Cancer Medical Center to attract public attention and attendance from 28 November 2020 to 21 December 2021. A questionnaire comprising 7 high-risk factors was completed by participants to identify high-risk individuals for further chest low-dose computed tomography examination. Non calcified nodules with a diameter ≥5 mm were deemed positive nodules. The positive nodules were discussed by a multidisciplinary team and treatment suggestions were given. Finally, we analyzed participant information, examination adherence, lung cancer detection rate, and staging.

Results

A total of 6,121 individuals were attracted to the project, and 5,925 (96.8%) agreed to participate. Of these, 5,889 (99.4%) completed the survey, with 4,627 (78.6%) in the high-risk group and 1,262 (21.4%) in the non-high-risk group. The proportion of males in the high-risk group was higher than that in the non-high-risk group, and the difference was statistically significant among those aged 40-49 years, 50-59, years and 60-69 years; P<0.01. In the high-risk population, 4,536 (98.0%) of participants adhered to examination, among whom 2,007 (44.2%) with positive nodules, 1,220 (26.9%) with negative nodules, and 1,309 (28.9%) without nodules showed statistical differences in age; P<0.01. The detection rate of lung cancer was 2.2% (99/4,536); 94.0% (93/99) of whom were stage 0-I patients.

Conclusions

A health lecture-based approach to improving public participation in regions with poor health education is likely to be effective in promoting the early detection of lung cancer.

Lung Cancer Screening in Greece: A Modelling Study to Estimate the Impact on Lung Cancer Life Years

(1) Background: Lung cancer causes a substantial epidemiological burden in Greece. Yet, no formal national lung cancer screening program has been introduced to date. This study modeled the impact on lung cancer life years (LCLY) of a hypothetical scenario of comprehensive screening for lung cancer with low-dose computed tomography (LDCT) of the high-risk population in Greece, as defined by the US Preventive Services Taskforce, would be screened and linked to care (SLTC) for lung cancer versus the current scenario of background (opportunistic) screening only; (2) Methods: A stochastic model was built to monitor a hypothetical cohort of 100,000 high-risk men and women as they transitioned between health states (without cancer, with cancer, alive, dead) over 5 years. Transition probabilities were based on clinical expert opinion. Cancer cases, cancer-related deaths, and LCLYs lost were modeled in current and hypothetical scenarios. The difference in outcomes between the two scenarios was calculated. 150 iterations of simulation scenarios were conducted for 100,000 persons; (3) Results: Increasing SLTC to a hypothetical 100% of eligible high-risk people in Greece leads to a statistically significant reduction in deaths and in total years lost due to lung cancer, when compared with the current SLTC paradigm. Over 5 years, the model predicted a difference of 339 deaths and 944 lost years between the hypothetical and current scenario. More specifically, the hypothetical scenario led to fewer deaths (−24.56%, p < 0.001) and fewer life years lost (−31.01%, p < 0.001). It also led to a shift to lower-stage cancers at the time of diagnosis; (4) Conclusions: Our study suggests that applying a 100% screening strategy amongst high-risk adults aged 50−80, would result in additional averted deaths and LCLYs gained over 5 years in Greece.

The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

Development of Deep Learning-based Automatic Scan Range Setting Model for Lung Cancer Screening Low-dose CT Imaging

RATIONALE AND OBJECTIVES

To develop an automatic setting of a deep learning-based system for detecting low-dose computed tomography (CT) lung cancer screening scan range and compare its efficiency with the radiographer's performance.

MATERIALS AND METHODS

This retrospective study was performed using 1984 lung cancer screening low-dose CT scans obtained between November 2019 and May 2020. Among 1984 CT scans, 600 CT scans were considered suitable for an observational study to explore the relationship between the scout landmarks and the actual lung boundaries. Further, 1144 CT scans data set was used for the development of a deep learning-based algorithm. This data set was split into an 8:2 ratio divided into a training set (80%, n = 915) and a validation set (20%, n = 229). The performance of the deep learning algorithm was evaluated in the test set (n = 240) using actual lung boundaries and radiographers' scan ranges.

RESULTS

The mean differences between the upper and lower boundaries of the deep learning-based algorithm and the actual lung boundaries were 4.72 ± 3.15 mm and 16.50 ± 14.06 mm, respectively. The accuracy and over-scanning of the scan ranges generated by the system were 97.08% (233/240) and 0% (0/240) for the upper boundary, and 96.25% (231/240) and 29.58% (71/240) for the lower boundary.

CONCLUSION

The developed deep learning-based algorithm system can effectively predict lung cancer screening low-dose CT scan range with high accuracy using only the frontal scout.

Analysis of pulmonary nodules detected by annual low-dose computed tomography in the elderly during a 10-year follow-up

AIM

To describe pulmonary nodules detected by annual low-dose computed tomography (LDCT) in the elderly during a 10-year follow-up, and to provide a basis for clinical decision-making in the elderly.

METHODS

In this retrospective study, patients who completed at least a 3-year follow-up visit with annual LDCT imaging data were eligible for inclusion. The evolution of pulmonary nodules was evaluated, including malignant, suspicious malignant, benign and undetermined nodules. Additionally, the nature and outcome of new nodules during the follow-up were analyzed.

RESULTS

For the 365 subjects included, 899 positive pulmonary nodules were detected in 286 patients. Among these there were 788 solid nodules, 20 part-solid nodules and 91 nonsolid nodules. The detection rate of positive nodules and of lung cancer was 78.4% and 5.5%, respectively. 99.7% (786/788) of solid nodules were benign, and 75% (15/20) of part-solid nodules and 28.6% (26/91) of nonsolid nodules were malignant or suspected malignant. 124 new positive nodules appeared during the annual follow-up, but 58.9% of them subsequently disappeared. Significant higher detection rates of 10-20-mm nodules (P = 0.0485) and suspicious malignant nodules (P = 0.017) were observed in subjects over 75 years old as compared with those under 75 years old.

CONCLUSIONS

Solid nodules accounted for the highest proportion of lung nodules screened at baseline, and most of them were benign. The malignant probability of part-solid nodules was the highest. Most newly appeared nodules disappeared during subsequent follow-up. The proportions of suspicious malignant nodules and 10-20-mm nodules in subjects over 75 years old were higher than in those under 75 years old. Geriatr Gerontol Int 2022; ••: ••-••.

Lung cancer screening decisional needs among African American smokers of lower socioeconomic status

OBJECTIVES

Adherence to most evidence-based cancer screenings is lower among African Americans due to system- and individual-level factors that contribute to persistent disparities. Given the recommendation for low-dose computed tomography (LDCT) screening among individuals at high risk for lung cancer, we sought to describe aspects of decision-making for LDCT among African Americans and to examine associations between select components of decision-making and screening-related intentions.

DESIGN

African Americans (N = 119) with a long-term smoking history, aged 55-80 years, and without lung cancer were recruited to participate in a cross-sectional survey. We measured knowledge, awareness, decisional conflict, preferences, and values related to lung cancer screening.

RESULTS

The majority of the study population was of lower socioeconomic status (67.2% had an annual income of ≤$20,000) and long-term current (79%) smokers. Participants had a median 20 pack-years smoking history. Most participants (65.8%) had not heard of LDCT and the total lung cancer screening knowledge score was M = 7.1/15.0 (SD = 1.8). Participants with higher scores on the importance of the pros and cons of screening expressed greater likelihood of talking with a doctor, family, and friends about screening (p's < .10).

CONCLUSIONS

Findings have implications for addressing the decisional needs of lower socioeconomic African American current and former smokers to promote informed decision-making for LDCT.

Variation in Model-Based Economic Evaluations of Low-Dose Computed Tomography Screening for Lung Cancer: A Methodological Review

OBJECTIVES

There is significant heterogeneity in the results of published model-based economic evaluations of low-dose computed tomography (LDCT) screening for lung cancer. We sought to understand and demonstrate how these models differ.

METHODS

An expansion and update of a previous systematic review (N = 19). Databases (including MEDLINE and Embase) were searched. Studies were included if strategies involving (single or multiple) LDCT screening were compared with no screening or other imaging modalities, in a population at risk of lung cancer. More detailed data extraction of studies from the previous review was conducted. Studies were critically appraised using the Consensus Health Economic Criteria list.

RESULTS

A total of 16 new studies met the inclusion criteria, giving a total of 35 studies. There are geographic and temporal differences and differences in screening intervals and eligible populations. Studies varied in the types of models used, for example, decision tree, Markov, and microsimulation models. Most conducted a cost-effectiveness analysis (using life-years gained) or cost-utility analysis. The potential for overdiagnosis was considered in many models, unlike with other potential consequences of screening. Some studies report considering lead-time bias, but fewer mention length bias. Generally, the more recent studies, involving more complex modeling, tended to meet more of the critical appraisal criteria, with notable exceptions.

CONCLUSIONS

There are many differences across the economic evaluations contributing to variation in estimates of the cost-effectiveness of LDCT screening for lung cancer. Several methodological factors and evidence needs have been highlighted that will require consideration in future economic evaluations to achieve better agreement.

Preliminary results of low-dose computed tomography screening for lung cancer in asymptomatic participants

OBJECTIVES

Low dose computed tomography (LDCT) is the best method for early diagnosis of lung cancer. Even though it has been widely used in clinic, the selection of screening objects and the management scheme of pulmonary nodules are still not unified among research institutions. This study aims to evaluate the effect of LDCT in detection effect and follow-up process for pulmonary nodules in asymptomatic participants.

METHODS

A total of 1 600 asymptomatic participants (37 to 82 years old), who came from Yantian District People's Hospital, Southern University of Science and Technology, received LDCT. The lung nodules were categorized into positive nodules and semi-positive nodules, and according to the density of positive nodules they were categorized into 4 types: solid nodules (SN), partial solid nodules (pSN), pure ground glass nodules (pGGN), and pleural nodules (PN). The number, detection rate, imaging findings, follow-up change of lung nodules, and the postoperative pathological results of positive nodules were recorded and analyzed.

RESULTS

Lung nodules were found in 221 cases by LDCT. The total detection rate of lung nodule was 13.8% (221/1 600), and the detection rate in positive nodules was 4.9% (79/1 600). The detected nodules were mainly single (173 cases), solid (133 cases) and semi-positive nodules (142 cases). Most of nodules (177 cases) had no change in the follow-up process. The enlargement and/or increased density of nodules (5 cases) were lung cancer. Pathological results were obtained in 10 cases, 8 cases were malignant (1 small cell lung cancer and 7 adenocarcinomas), 2 cases were benign (cryptococcal infection and alveolar epithelial dysplasia). The detection rate of lung cancer was 0.5% (8/1 600), and the proportion of early lung cancer was 75% (6/8).

CONCLUSIONS

LDCT screening can identify and increase the detection rate in the early lung cancer, which is an effective screening method. It is safe and feasible to take regular follow-up and re-examination for nodules with diameter less than 5 mm. When the size and or density of nodule increases, it indicates the malignant prognosis of the nodule and timely clinical intervention is needed.

Evaluation and comparison of performance of low-dose 128-slice CT scanner with different mAs values: A phantom study

OBJECTIVE:

Radiation dose in computed tomography (CT) has been the concern of physicists ever since the introduction of CT scan. The objective of this study was to evaluate the performance of low-dose 128-slice CT scanner with different mAs values.

MATERIALS AND METHODS:

Quantitative study was carried out at different values of mAs. Philips brilliance CT phantom with Philips ingenuity 128-slice low-dose CT scanner was chosen for this study. CT number linearity, CT number accuracy, slice thickness accuracy, high-contrast resolution, and low-contrast resolution were calculated and estimated computed tomography dose index volume (CTDI_vol) for all the mAs values were recorded. Noise was calculated for all mAs values for comparison.

RESULTS:

Data analysis shows that image quality was acceptable for all protocols. High-contrast resolution for all protocols was 20 line pairs per centimeter. Low-contrast resolution for 50 mAs images was 4 mm and 3 mm for other mAs protocols. Images acquired using 100 mAs revealed ring artifacts. CTDI_vol using 50 mAs was 33% of the CTDI_vol using 150 mAs. The dose–length product at 100 mAs was reduced to 66% of the dose–length product at 150 mAs, and the same at 50 mAs was reduced to 33%.

CONCLUSION:

It is evident here that mAs has direct impact on the radiation dose to patient. With iDose4, mAs can be reduced to 50 mAs in multislice low-dose CT scan to reduce the radiation dose with minimal effect on image quality for slice thickness 4 mm. However, noise would dominate at tube current lower than 50 mAs for 120 kVp.

Comparing the downstream costs and healthcare utilization associated with the use of low-dose computed tomography (LDCT) in lung cancer screening in patients with and without alzheimer's disease and related dementias (ADRD)

OBJECTIVE

This study aims to compare the downstream costs and healthcare utilization associated with using low-dose computed tomography (LDCT) for lung cancer screening in patients with and without Alzheimer's disease and related dementias (ADRD).

METHODS

Based on data from IBM MarketScan Commercial Claims Databases (2014-2018), we have identified four study cohorts: ADRD and non-ADRD patients who went through LDCT screening; ADRD and non-ADRD patients without LDCT screening. Annually healthcare utilization and cost were grouped into outpatient, inpatient, and pharmacy. We used difference-in-differences (DID) models to estimate the downstream healthcare utilization and cost associated with LDCT screening in both ADRD and non-ADRD population. We used a difference-in-difference-in-differences (DDD) model to explore whether LDCT screening was associated with higher downstream cost and healthcare utilization in ADRD population than non-ADRD population.

RESULT

Compared to individuals without LDCT screening, LDCT screening was associated with increased outpatient visits (2.1, 95% CI 0.7, 3.4) and outpatient cost ($2301.0, 95% CI 296.2, 4305.8) in the ADRD population and increased outpatient visits (0.6, 95% CI 0.1, 1.1) in the non-ADRD population within 1 year after screening. Compared with the non-ADRD population, LDCT screening was found to be associated with an additional 1.5 (95% CI 0.2, 2.8) outpatient visits, 0.7 (95% CI 0.1, 1.3) days of inpatient stays, and $4,960.4 (95% CI 532.7, 9388.0) in overall healthcare costs within 1-year after LDCT in the ADRD population (all p < .5).

CONCLUSION

The downstream cost and healthcare utilization associated with LDCT screening were found to be higher in the ADRD population compared to the average population.

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.

Examination of the Association Between Access to Care and Lung Cancer Screening Among High-Risk Smokers

Objective: The purpose of this study was to examine the influence of access to care on the uptake of low-dose computed tomography (LDCT) lung cancer screening among a diverse sample of screening-eligible patients. Methods: We utilized a cross-sectional study design. Our sample included patients evaluated for lung cancer screening at a large academic medical center (AMC) between 2015 and 2017 who met 2013 USPSTF guidelines for LDCT screening eligibility. The completion of LDCT screening (yes, no) was the primary dependent variable. The independent variable was access to care (insurance type, living within the AMC service area). We utilized binary logistic regression analyses to examine the influence of access to care on screening completion after adjusting for demographic factors (age, sex, race) and smoking history (current smoking status, smoking pack-year history). Results: A total of 1,355 individuals met LDCT eligibility criteria, and of those, 29.8% (n = 404) completed screening. Regression analysis results showed individuals with Medicaid insurance (OR, 1.51; 95% CI, 1.03-2.22), individuals living within the AMC service area (OR, 1.71; 95% CI, 1.21-2.40), and those aged 65-74 years (OR, 1.49; 95% CI, 1.12-1.98) had higher odds of receiving LDCT lung cancer screening. Lower odds of screening were associated with having Medicare insurance (OR, 0.30; 95% CI, 0.22-0.41) and out-of-pocket (OR, 0.27; 95% CI, 0.15-0.47). Conclusion: Access to care was independently associated with lowered screening rates. Study results are consistent with prior research identifying the importance of access factors on uptake of cancer early detection screening behaviors.

Impact of low-dose computed tomography for lung cancer screening on lung cancer surgical volume: The urgent need in health workforce education and training

This study aimed to investigate the time trend variation in the surgical volume and prognostic outcome of patients with lung cancer after the gradual prolonged implementation of a low-dose computed tomography (LDCT) lung cancer screening program.Using the hospital-based cancer registry data on number of patients with lung cancer and deaths from 2008 to 2017, we conducted a retrospective study using a hospital-based cohort to investigate the relationship between changes in lung cancer surgical volume, the proportion of lung-sparing surgery, and prolonged prognostic outcomes after the gradual implementation of the LDCT lung cancer screening program in recent years.From 2008 to 2017, 3251 patients were diagnosed with lung cancer according to the hospital-based cancer registry. The 5-year mortality rate decreased gradually from 83.54% to 69.44% between 2008 and 2017. The volume of total lung cancer surgical procedures and proportion of lung-sparing surgery performed gradually increased significantly from 2008 to 2017, especially from 2014 to 2017 after implementation of a large volume of LDCT lung cancer screening examinations. In conclusion, our real-world data suggest that there will be an increase in cases of operable early-stage lung cancers, which in turn will increase the surgical volume and proportion of lung-sparing surgery, after the gradual implementation of the LDCT lung cancer screening program in recent years. These findings suggest the importance of a successful national policy regarding LDCT screening programs, regulation of shortage of thoracic surgeons, thoracic radiologist workforce training positions, and education programs.

A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings

BACKGROUND

Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time.

METHODS

ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms.

RESULTS

Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%.

CONCLUSIONS

ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening.

LAY SUMMARY

A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.

The budget impact of lung cancer screening with low-dose computed tomography

Összefoglaló. Bevezetés: Korábbi vizsgálatunk szerint a kis dózisú komputertomográfiával évente végzett tüdőrákszűrés 50-74 éves dohányzók körében költséghatékony, és az 55-74 évesek körében költségmegtakarító. Célkitűzés: Ennek a vizsgálatnak a célja a korábbi hosszú távú költséghatékonysági elemzés kiegészítése egy finanszírozó szempontú, rövid és középtávú költségvetési hatásvizsgálattal. Módszer: Egészség-gazdaságtani modellünk az 50-74 éves, naponta dohányzó lakosság tüdőrákszűrésének költségét hasonlítja össze a szervezett szűrésben nem részesülő, naponta dohányzó lakosság költségével. Ehhez megvizsgáljuk a célpopuláció létszámának alakulását, az eredményes elérés és felfedezés valószínűségét, továbbá a szűrés nyomán felmerülő terápiás költségeket és megtakarításokat. A szűrés és a kivizsgálások után diagnosztizált betegek útját az érvényben lévő hazai ellátási protokollnak megfelelően követjük. A kezelések eredményességét a HUNCHEST-felmérés adatai alapján, a kezelésekhez tartozó beavatkozások költségét közfinanszírozási adatok alapján számoljuk. Eredmények: A kis dózisú komputertomográfiával történő tüdőrákszűrés az érintett lakosság 10%-ának várható részvétele mellett a kezdeti évben mintegy 3,3 milliárd, az 5. évben 1,9 milliárd Ft éves többletkiadással jár. A 3. évig szűréssel felfedezett betegek terápiája többe kerül, mint a szűrés nélkülieké, ugyanakkor a 4. és 5. évben a szűrés nélküli csoportban a későbbi stádiumban felismert betegek kezelési költsége már meghaladja a szűrt betegek terápiás költségét. A 3. évtől folyamatosan növekvő terápiás megtakarítás a teljes szűrés költségét a 10. évre az 1. év kiadásának 20%-ára csökkenti. Következtetések: A kis dózisú komputertomográfiával történő tüdőrákszűrés bevezetése évi 2,6 milliárd Ft többletforrást igényelne, és folyamatos kiadáscsökkenés mellett hosszú távon akár nettó megtakarítást is eredményezhet a nem szervezett szűréshez képest. A kockázati csoportok pontosítása, például kiemelt földrajzi területeken végzett célzott szűrés tovább javíthatja az eredményeket. Orv Hetil. 2021; 162(24): 952-959.

SUMMARY

INTRODUCTION

Our earlier analysis indicated that screening lung cancer patients with low-dose computed tomography amongst smokers between age of 50-74 and between age of 55-74 is cost-effective and cost-saving, respectively.

OBJECTIVE

This study aims to extend the long-term cost-effectiveness analysis with short- and mid-term budget impact analysis.

METHOD

The health economic model compares the cost of nationwide screening amongst smokers between 50-74 years to the current occasional screening policy. The analysis determines the size of the target population, recruitment rates and market uptake. Health care finance costs associated with the patient pathways are determined by national guidelines and clinical practice. Screening and treatment effectiveness are based on the HUNCHEST survey and international scientific literature, while the cost of health states and events are determined using national tariffs.

RESULTS

Assuming 10% uptake of low-dose computed tomography screening for the target population will cost an additional 3.3 billion HUF and 1.9 billion HUF in the 1st and 5th years, respectively. Until the 3rd year, new patients' treatment costs exceed costs due to late discovery and delay in treatment. This pattern is changing from the 4th year on. Due to timely care savings by the 10th year in the screened population will reduce total costs to the 20% of the first year costs.

CONCLUSIONS

Introduction of national screening for lung cancer patients with low-dose computed tomography is estimated to cost around additional 2.6 billion HUF/year and could end up in net savings in the long run. Identification of risk groups according to regional or other strata could increase the effectiveness and efficiency of the program. Orv Hetil. 2021; 162(24): 952-959.

Characteristics of Ground-Glass Nodules Detected by Low-Dose Computed Tomography as a Regular Health Examination Among Chinese Hospital Employees and Their Parents

Introduction

Annual LDCT has been offered as a regular examination among many unit staff in China. Along with the wide application of LDCT, more and more ground-glass nodules were found. We focused on characteristics and relationship of ground-glass nodules detected by LDCT as a regular health examination among Chinese hospital employees and their parents.

Methods

We recorded LDCT-detected ground-glass nodules (GGNs) in the hospital employees and parents between 2019 and 2020. Clinical information, including age, gender, smoking status was collected and analyzed.

Results

A total of 5,574 employees and 2,686 employs’ parents ≥60 years in Xiangya hospital performed annual physical examination. In total, LDCT incidentally detected ground-glass nodules 392 (24.78%, 392/1,582) in hospital employees and 254 in parents (10.80%, 254/2,352). The GGN-detection rate was significantly greater in employee group than parent group and more non-smokers in former (P <0.001). The detection rate was significantly greater in female than male both in employees group and parents group, and the proportion of female was bigger in employees group (P <0.001). There were more pure-GGNs both in employees group and parents group. There were less participants with solitary GGN in employee group than parent group (P = 0.033). Besides, there were more large GGNs (≥10 mm) (P <0.001), LU-RADS 4 GGNs (P <0.001) and LU-RADS 4B GGNs (P = 0.003), LU-RADS 4C-5 GGNs (P = 0.001) in parent group than employee group. There were 36 employee–parent pairs (27.07%) both had GGNs among 133 pairs who both performed LDCT. GGNs in employees were smaller and lower-grade than their parents (P < 0.001, P = 0.001).

Conclusions

Among the employees and parents who had ground glass nodules, 1/4 of them both detected GGNs. Although the detection rate of GGNs in the parent group was lower than that in the employee group, the grade of nodules was significantly higher. All these suggest that the occurrence and development of ground glass nodules may be related to genetic factors.

Downstream Complications and Healthcare Expenditure after Invasive Procedures for Lung Lesions in Taiwan

This study aimed to estimate the downstream complications and healthcare expenditure after invasive procedures for lung lesions, which in turn could be used for future cost-effectiveness analyses of lung cancer screening in Taiwan. We interlinked the Taiwan National Beneficiary Registry with the National Health Insurance Reimbursement databases to identify non-lung cancer individuals aged 50–80 years who underwent invasive lung procedures within one month after non-contrast chest computed tomography between 2014 and 2016. We directly matched one individual with 10 controls by age, gender, calendar year, residence area, comorbidities, and the past one-year healthcare expenditure to calculate incremental one-month complication rates and attributable costs. A total of 5805 individuals who underwent invasive lung procedures were identified and matched with 58,050 controls. The incremental one-month complication rates were 13.4% (95% CI: 10.9% to 15.8%), 10.7% (95% CI: 9.2% to 12.1%), and 4.4% (95% CI: 2.0% to 6.7%) for thoracic surgery, bronchoscopy, and needle biopsy, respectively. The incremental one-month healthcare expenditure for minor, intermediate, and major complications were NT$1493 (95% CI: NT$-3107 to NT$6092), NT$18,422 (95% CI: NT$13,755 to NT$23,089), and NT$58,021 (95% CI: NT$46,114 to NT$69,929), respectively. Individuals aged 60–64 years incurred the highest incremental costs. Downstream complications and the healthcare expenditure after invasive procedures for lung lesions would be substantial for non-lung cancer individuals 50–80 years of age. These estimates could be used in modeling the cost-effectiveness of the national lung screening program in Taiwan.

Primary care and pulmonary physicians' knowledge and practice concerning screening for lung cancer in Lebanon, a middle-income country

Background

Screening for lung cancer with low‐dose computed tomography (LDCT) was shown to reduce lung cancer incidence and overall mortality, and it has been recently included in international guidelines. Despite the rising burden of lung cancer in low and middle‐income countries (LMICs) such as Lebanon, little is known about what primary care physicians or pulmonologists know and think about LDCT as a screening procedure for lung cancer, and if they recommend it.

Objectives

Evaluate the knowledge about LDCT and implementation of international guidelines for lung cancer screening among Lebanese primary care physicians (PCPs) and pulmonary specialists.

Methodology

PCPs and PUs based in Lebanon were surveyed concerning knowledge and practices related to lung cancer screening by self‐administered paper questionnaires.

Results

73.8% of PCPs and 60.7% of pulmonary specialists recognized LDCT as an effective tool for lung cancer screening, with 63.6% of PCPs and 71% of pulmonary specialists having used it for screening. However, only 23.4% of PCPs and 14.5% of pulmonary specialists recognized the eligibility criteria for screening. Chest X‐ray was recognized as ineffective by only 55.8% of PCPs and 40.7% of pulmonary specialists; indeed, 30.2% of PCPs and 46% of pulmonary specialists continue using it for screening. The majority have initiated a discussion about the risks and benefits of lung cancer screening.

Conclusion

PCPs and pulmonary specialists are initiating discussions and ordering LDCT for lung cancer screening. However, a significant proportion of both specialties are still using a non‐recommended screening tool (chest x‐ray); only few PCPs and pulmonary specialists recognized the population at risk for which screening is recommended. Targeted provider education is needed to close the knowledge gap and promote proper implementation of guidelines for lung cancer screening.

Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules

Objectives

This study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated.

Methods

A total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.

Results

Shape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models.

Conclusions

The diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.

Performance of various risk prediction models in a large lung cancer screening cohort in Gdańsk, Poland-a comparative study

Background

Optimal selection criteria for the lung cancer screening programme remain a matter of an open debate. We performed a validation study of the three most promising lung cancer risk prediction models in a large lung cancer screening cohort of 6,631 individuals from a single European centre.

Methods

A total of 6,631 healthy volunteers (aged 50-79, smoking history ≥30 pack-years) were enrolled in the MOLTEST BIS programme between 2016 and 2018. Each participant underwent a low-dose computed chest tomography scan, and selected participants underwent a further diagnostic work-up. Various lung cancer prediction models were applied to the recruited screenees, i.e., (I) Tammemagi's Prostate, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCO_m2012), (II) Liverpool Lung Project (LLP) model, and (III) Bach's lung cancer risk model. Patients (I) with 6-year lung cancer probability ≥1.3% were considered as high risk in PLCO_m2012 model, (II) in LLP model with 5-year lung cancer probability ≥5.0%, and (III) in Bach's model with 5-year lung cancer probability ≥2.0%. The particular model cut-off values were employed to the cohort to evaluate each model's performance in the screened population.

Results

Lung cancer was diagnosed in 154 (2.3%) participants. Based on the risk estimates by PLCO_m2012, LLP and Bach's models there were 82.4%, 50.3% and 19.8% of the MOLTEST BIS participants, respectively, who fulfilled the above-mentioned threshold criteria of a lung cancer development probability. Of those detected with lung cancer, 97.4%, 74.0% and 44.8% were eligible for screening by PLCO_m2012, LLP and Bach's model criteria, respectively. In Tammemagi's risk prediction model only four cases (2.6%) would have been missed from the group of 154 lung cancer patients primarily detected in the MOLTEST BIS.

Conclusions

Lung cancer screening enrollment based on the risk prediction models is superior to NCCN Group 1 selection criteria and offers a clinically significant reduction of screenees with a comparable proportion of detected lung cancer cases. Tammemagi's risk prediction model reduces the proportion of patients eligible for inclusion to a screening programme with a minimal loss of detected lung cancer cases.