Low-dose CT Research Articles

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Benchmarking deep learning-based low-dose CT image denoising algorithms.

Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reductionalgorithms. Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reportedresults. In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardizedsetup. Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal atbest. This study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate theiralgorithms.

Assessing Bronchiectasis Progression in Low-dose Screening for Lung Cancer: Frequency and Predictors.

Bronchiectasis is associated with loss of lung function, substantial use of health care resources, and increased morbidity and mortality in people with cardiopulmonary diseases. We assessed the frequency of progression or new development of bronchiectasis and predictors of progression in participants in low-dose computed tomography (CT) screening programs. We reviewed our prospectively enrolled screening cohort in the Early Lung and Cardiac Action Program cohort of smokers, aged 40 to 90, between 2010 and 2019, and medical records to assess the progression of bronchiectasis after five or more years of follow-up after baseline low-dose CT. Logistic and multivariate-analysis-of-covariance regression analyses were used to examine factors associated with bronchiectasis progression. Among 2182 baseline screening participants, we identified 534 (mean age: 65±9y; 53.6% women) with follow-up screening of 5+ years (median follow-up: 103.2mo). Of the 534 participants, 34 (6.4%) participants had progressed (25/126, 19.8%) or newly developed (9/408, 2.2%) bronchiectasis. Significant predictors of progression (progressed+newly developed) were: age (P=0.03), pack-years of smoking (P=0.004), baseline components of the ELCAP Bronchiectasis Score, including the severity of bronchial dilatation (P=0.01), its extent (P=0.01), bronchial wall thickening (P=0.04), and mucoid impaction (P<0.001). Assuming similar progression rates, ~136 out of 2182 participants are expected to progress on follow-up screening. This study sheds light on bronchiectasis progression and its significant predictors in a low-dose CT screening program. We recommend reporting bronchiectasis as participants who have smoked are at increased risk, and continued assessment over the entire period of participation in the low-dose CT screening program would allow for the identification of possible causes, early warning, and even early treatment.

Repeated CT scans on 12,984 Asians to diagnose lung cancer once it is suspected.

In Taiwan, lung cancer remains the leading cause of cancer-related fatalities, resulting in substantial healthcare expenses. This research aims to evaluate both the frequency and the costs of low-dose computed tomography (LDCT) in individuals suspected of having lung cancer until their diagnosis of cancer. LDCT screening was not conducted on a population-wide scale, and asymptomatic participants had to cover the expenses for the screening personally or reimburse from other sources. If the screening results were positive or suspicious, National Health Insurance (NHI) could be utilized for subsequent follow-up examinations. This cohort study utilized the NHI Database and focused on individuals with suspected cases of lung cancer identified between 2010 and 2014.A total of 17,572 suspected new lung cancer cases were initially identified and assigned to the relevant International Classification of Diseases codes. Individuals with suspected lung cancer received a diagnosis following an average follow-up period of 2.24 (95%CI, 2.11-2.37) years, and required the use of 2.36 (95%CI, 2.20-2.51) repeated CT scans. The NHI expenditures incurred by the use of CT scans for monitoring suspected lung cancer cases were relatively modest.

Comparative analysis of body composition using torso CT from PET/CT with bioelectrical impedance and muscle strength in healthy adults

The role of torso computed tomography (CT) in evaluating body composition has been unexplored. This study assessed the potential of low-dose torso CT from positron emission tomography (PET)/CT for analyzing body composition and its relation to muscle strength. We retrospectively recruited 384 healthy Korean adults (231 men, 153 women) who underwent torso 18F-FDG PET/CT, bioelectrical impedance analysis (BIA), and muscle strength tests (handgrip strength [HGS] and knee extension strength [KES]). CT images were segmented into three compartments: torso volumetric, abdominal volumetric, and abdominal areal. Muscle amounts from each compartment were indexed to height (m2). BIA and HGS served as reference standards, with correlation coefficients (r) calculated. Torso muscle volumetric index (TorsoMVI) had the strongest correlations with BIA-derived values (r = 0.80 for men; r = 0.73 for women), surpassing those from the abdominal compartments. TorsoMVI was also correlated significantly with HGS (r = 0.39, p < 0.01) and differentiated between normal and possible sarcopenia in men (n = 225, 5960 ± 785 cm3/m2 vs. n = 6, 5210 ± 487 cm3/m2, p = 0.02). In women, KES correlated more strongly with muscle parameters than HGS. Despite gender-specific variations, torso CT-derived parameters show promise for evaluating body composition and sarcopenia.

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.

Radiation dose should be as low as reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), the radiation dose may be considerably reduced. To evaluate the potential of PCD-CT for dose reduction in pulmonary nodule visualization for human readers as well as for computer-aided detection (CAD) studies. A chest phantom containing pulmonary nodules of different sizes/densities (range 3-12 mm and -800-100 HU) was scanned on a PCD-CT with standard low-dose protocol as well as with half, quarter, and 1/40 dose (CTDIvol 0.4-0.03 mGy). Dose-matched scans were performed on a third-generation energy-integrating detector CT (EID-CT). Evaluation of nodule visualization and detectability was performed by two blinded radiologists. Subjective image quality was rated on a 5-point Likert scale. Artificial intelligence (AI)-based nodule detection was performed using commercially available software. Highest image noise was found at the lowest dose setting of 1/40 radiation dose (eff. dose = 0.01mSv) with 166.1 ± 18.5 HU for PCD-CT and 351.8 ± 53.0 HU for EID-CT. Overall sensitivity was 100% versus 93% at standard low-dose protocol (eff. dose = 0.2 mSv) for PCD-CT and EID-CT, respectively. At the half radiation dose, sensitivity remained 100% for human reader and CAD studies in PCD-CT. At the quarter radiation dose, PCD-CT achieved the same results as EID-CT at the standard radiation dose setting (93%, P = 1.00) in human reading studies. The AI-CAD system delivered a sensitivity of 93% at the lowest radiation dose level in PCD-CT. At half dose, PCD CT showed pulmonary nodules similar to full-dose PCD, and at quarter dose, PCD CT performed comparably to standard low-dose EID CT. The CAD algorithm is effective even at ultra-low doses.

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

The Azygos Esophageal Recess Is Not to Be Missed in Screening Lung Cancer With LDCT.

Lesion overlooking and late diagnostic workup can compromise the efficacy of low-dose CT (LDCT) screening of lung cancer (LC), implying more advanced and less curable disease stages. We hypothesized that the azygos esophageal recess (AER) of the right lower lobe (RLL) might be an area prone to lesion overlooking in LC screening. Two radiologists reviewed the LDCT examinations of all the screen-detected incident LCs observed in the active arm of 2 randomized clinical trials: ITALUNG and national lung screening trial. Those in the AER were compared with those in the remainder of the RLL for possible differences in diagnostic lag according to the Lung-RADS 1.1 recommendations, size, stage, and mortality. Six (11.7%) of 51 screen-detected incident LCs of the RLL were located in the AER. The diagnostic lag time was significantly longer (P=0.046) in the AER LC (mean 14±9mo) than in the LC in the remaining RLL (mean 7.3±1mo). Size and stage at diagnosis were not significantly different. All 6 subjects with LC in the AER and 16 (35.5%) of 45 subjects with LC in the remaining RLL (P=0.004) died of LC after a median follow-up of 12 years. Our retrospective study indicates that AER might represent a lung region of the RLL prone to have early LC overlooked due to detection or interpretation errors with possible detrimental consequences for the subject undergoing LC screening.

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Cost-effectiveness analysis of a lung cancer screening program in the Netherlands: a simulation based on NELSON and NLST study outcomes

Background In the Netherlands, lung cancer is the leading cause of cancer-related death, accounting for more than 10,000 annual deaths. Lung cancer screening (LCS) studies using low-dose computed tomography (LDCT) have demonstrated that early detection reduces lung cancer mortality. However, no LCS program has been implemented yet in the Netherlands. A national LCS program has the potential to enhance the health outcomes for lung cancer patients in the Netherlands. Objective and Methods This study evaluates the cost-effectiveness of LCS compared to no-screening in the Netherlands, by simulating the screening outcomes based on data from NEderlands–Leuvens Longkanker Screenings ONderzoek (NELSON) and National Lung Screening Trial (NLST). We simulated annual screening up to 74 years of age, using inclusion criteria from the respective studies. A decision tree and Markov model was used to predict the incremental costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICERs) for the screening population. The analysis used a lifetime horizon and a societal perspective. Results Compared to no-screening, LCS resulted in an ICER of €5,169 per QALY for the NELSON simulation, and an ICER of €17,119 per QALY for the NLST simulation. The screening costs were highly impactful for the cost-effectiveness. The most influential parameter was the CT scan cost. Cost reduction for CT from €201 to €101 per scan would reduce the ICER to €2,335 using NELSON criteria. Additionally, LCS could prevent 15,115 and 12,611 premature lung cancer deaths, accompanied by 1.66 and 1.31 QALYs gained per lung cancer case for the NELSON and NLST simulations, respectively. Conclusion LCS was estimated to be cost-effective in the Netherlands for both simulations at a willingness-to-pay threshold of €20,000 per QALY. Using the NELSON criteria, less than €5,500 per QALY had to be spent. Lowering the cost per CT exam would lead to a further reduction of this amount.

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

Lung cancer screening in never smokers.

Low-dose computed tomography (LDCT) lung cancer screening has been established in smokers, but its role in never smokers remains unclear. The differences in lung cancer biology between smokers and nonsmokers highlight the importance of a discriminated approach. This overview focuses on the emerging data and implementation challenges for LDCT screening in nonsmokers. The first LDCT screening study in nonsmokers enriched with risk factors demonstrated a lung cancer detection rate double that of the phase 3 trials in smokers. The relative risk of lung cancer detected by LDCT has also been found to be similar amongst female never smokers and male ever smokers in Asia. Majority of lung cancers detected through LDCT screening are stage 0/1, leading to concerns of overdiagnosis. Risk prediction models to enhance individual selection and nodule management could be useful to enhance the utility of LDCT screening in never smokers. With appropriate risk stratification, LDCT screening in never smokers may attain similar efficacy as compared to smokers. A global effort is needed to generate evidence surrounding optimal screening strategies, as well as health and economic benefits to determine the suitability of widespread implementation.

Beyond lung cancer screening, an opportunity for early detection of chronic obstructive pulmonary disease and cardiovascular diseases.

Lung cancer screening programs concern smokers at risk for cardiovascular diseases (CVDs) and chronic obstructive pulmonary disease (COPD). The LUMASCAN (LUng Cancer Screening, MArkers and low-dose computed tomography SCANner) study aimed to evaluate the acceptability and feasibility of screening for these 3 diseases in a community population with centralized organization and to determine low-dose computed tomography (CT) markers associated with each disease. This cohort enrolled participants meeting National Comprehensive Cancer Network criteria (v1.2014) in an organized lung cancer-screening program including low-dose CT scans; spirometry; evaluations of coronary artery calcifications (CACs); and a smoking cessation plan at inclusion, 1, and 2 years; then telephone follow-up. Outcomes were the participation rate and the proportion of participants affected by lung cancer, obstructive lung disease, or CVD events. Logistic-regression models were used to identify radiological factors associated with each disease. Between 2016 and 2019, a total of 302 participants were enrolled: 61% men; median age 58.8 years; 77% active smoker; 11% diabetes; 38% hypertension; and 27% taking lipid-lowering agents. Inclusion, 1-year, and 2-year participation rates were 99%, 81%, 79%, respectively. After a median follow-up of 5.81 years, screenings detected 12 (4%) lung cancer, 9 of 12 via low-dose CT (78% localized) and 3 of 12 during follow-up (all stage IV), 83 (27%) unknown obstructive lung disease, and 131 (43.4%) moderate to severe CACs warranting a cardiology consultation. Preexisting COPD and moderate to severe CACs were associated with major CVD events with odds ratios of 1.98 (95% confident interval [CI] = 1.00 to 3.88) and 3.27 (95% CI = 1.72 to 6.43), respectively. The LUMASCAN study demonstrated the feasibility of combined screening for lung cancer, COPD, and CVD in a community population. Its centralized organization enabled high participation and coordination of healthcare practitioners.

What’s behind thoracic surgery explosion in young patients under the age of 40 in Wuhan after COVID-19 outbreak?

IntroductionThe COVID-19 pandemic was associated with a dramatic increase of chest CT scanning in Wuhan. This was partly a COVID effect: some private and public employers required employees to have CT examinations to confirm they were healthy before going back to work. But it also likely reflects the growing enthusiasm for low-dose computed tomography (LDCT) screening. This investigation examines the resulting impact in the under 40 population. MethodsThe relevant de-identified information of the patients under age 40 who had also received thoracic surgery from 2018 to 2022 was analyzed using the medical record information system of Tongji Hospital in Wuhan. ResultsThe volume of thoracic surgeries increased continuously in young patients under the age of 40, from 219 in 2018 to 732 in 2022. The number of surgeries for pulmonary nodules or masses in this group increased over 6-fold, from 91 to 576. The number of surgeries leading to a diagnosis of adenocarcinoma of the lung increased more than 15-fold, from 26 to 415. The median adenocarcinoma size fell in half (from 15 mm to 7 mm) and the most common stage changed from Stage I invasive (46 % of adenocarcinomas in 2018) to microinvasive (60 % of adenocarcinomas in 2022). 70 % of lung adenocarcinomas were found in females. ConclusionsThere had been an explosion of thoracic surgery for adenocarcinomas among the under-40 population in Wuhan. The decrease in tumor size and the increase in microinvasive and in situ lesions in this young age group suggest considerable overdiagnosis. We should be vigilant about the risk of overdiagnosis and overtreatment especially in young women.

Real-world first round results from a charity lung cancer screening program in East Asia.

Screening with low-dose computed tomography (LDCT) has been proven to potentially reduce the rate of mortality of lung cancer. Lack of real-world data outside of protocolized trials has been cited as an impediment to its more widespread implementation, especially in Asia. This report aims to provide such real-world data. A single round of LDCT was provided through a community-based charity program in Hong Kong, China to asymptomatic adults with a family history of lung cancer and/or smoking history. Anonymized data from this program were analyzed. LDCT was performed for 99 participants, including 98 (99%) who had one or more family members with history of lung cancer, and 70 (71%) who were never-smokers. After a single round of screening, a positive LDCT was noted in 47 participants (47%). A sister with a history of lung cancer (28% vs. 8%, P=0.01) and a multiplex family (MF) (47% vs. 23%, P=0.02) were factors associated with a positive LDCT. After a median period of 10 months (range, 5-16 months) following LDCT, lung cancer (all adenocarcinoma) was diagnosed as a direct consequence of positive LDCT findings in six participants (6%), of whom four had stage I disease and five received surgery with curative intent. In the 47 participants with a positive LDCT, having a sister with a history of lung cancer was associated with an increased risk of lung cancer (relative risk =5.23; 95% confidence interval: 1.09-25.21). Detected lesions categorized as Lung Imaging Reporting and Data System (Lung-RADS) 3 or above (odds ratio =12.08; 95% confidence interval: 1.27-114.64) or deemed by an experienced specialist to be suspicious (odds ratio =63.33; 95% confidence interval: 5.48-732.29) were significantly more likely to turn out to be a lung cancer. This real-world data demonstrates that a single round of LDCT screening at a community level in East Asia can detect potentially curable lung cancer at a rate comparable to those reported by protocolized trials. When considering future LDCT screening programs in East Asia, a family history of lung cancer may be a key factor indicating a person for screening, and how features of a LDCT-detected lesion should trigger further intervention warrant further definition.

Inter-observer consistency on subsolid nodule follow-up recommendation based on National Comprehensive Cancer Network (NCCN) guidelines in low-dose computed tomography (LDCT) lung cancer screening.

Follow-up management of pulmonary nodules is a crucial component of lung cancer screening. Consistency in follow-up recommendations is essential for effective lung cancer screening. This study aimed to assess inter-observer agreement on National Comprehensive Cancer Network (NCCN) guideline-based follow-up recommendation for subsolid nodules from low-dose computed tomography (LDCT) screening. A retrospective collection of LDCT reports from 2014 to 2017 for lung cancer screening was conducted using the Radiology Information System and keyword searches, focusing on subsolid nodules. A total of 110 LDCT cases containing subsolid nodules were identified. Two senior radiologists provided standardized follow-up recommendation. Follow-up recommendation was categorized into four groups (0-, 3-, 6-, and 12-month). To ensure overall balance and representativeness of the follow-up categories, 60 scans from 60 participants were included (distribution ratio 1:1:2:2). Cases were categorised into follow-up recommendation groups by five observers following NCCN guidelines. Fleiss' kappa statistic was used to evaluate inter-observer agreement. Overall accuracy rate for follow-up recommendation among five observers was 72.3%. Chest radiologists' overall agreement was significantly higher than radiology residents (P<0.01). The overall agreement among the five observers was moderate, with a Fleiss' kappa of 0.437. For all paired readers, the mean Cohen's kappa value was 0.603, with 95% confidence interval (CI) from 0.489 to 0.716. Chest radiologists demonstrated substantial agreement, evidenced by a Cohen's kappa of 0.655 (95% CI: 0.503-0.807). In contrast, the mean Cohen's kappa among radiology residents was 0.533 (95% CI: 0.501-0.565). The majority of cases with discrepancies, accounting for 73.5%, were associated with the same risk-dominant nodules. A higher proportion of part-solid nodule was a risk factor for discrepancies. Of the 600 paired readings, major discrepancies and substantial discrepancies were observed in 27.5% and 4.8% (29/600) of the cases. In subsolid nodules, category evaluation of observer follow-up recommendation based on NCCN guidelines achieved moderate consistency. Disagreements were mainly caused by measurement and type disagreements of identical risk-dominant nodules. Part-solid nodule was a contributor for discrepancies in follow-up recommendation. Major and substantial management discrepancies were 27.5% and 4.8% in the paired evaluations.

Achieving Equitable Lung Cancer Screening Implementation in a Texas Safety Net Health System

IntroductionA lung cancer screening program using low dose CT (LDCT) in a Federally Qualified Health Center (FQHC) in Central Texas was developed and assessed for equitable implementation. MethodsFrom 11/2020-8/2023, patients aged 55-77 years who currently smoked or quit within 15 years with ≥ 20 pack-years of exposure were identified through EHR query and mailed outreach, or through direct provider referrals. A bilingual social worker confirmed eligibility, provided tele-care shared decision-making (SDM), coordinated screening, and offered smoking cessation. To assess equity, LDCT completion across demographics was compared, in 2023. Results6,486 patients were mailed outreach materials; 479 patients responded, of whom 108 (22.5%) were eligible and 71 (65.7%) participated in SDM. 629 eligible patients were referred internally; 579 (92.0%) completed SDM. Of the 650 patients who completed SDM, 636 (97.8%) agreed to screening.Mean age was 61.7; 38.1% were female. The population was diverse: 35.8% identified as Latino, 17.8% as African-American, 26.8% had Medicare or Medicaid, 48.0% used the county medical assistance program, 14.2% were uninsured, and 76.7% currently smoked.Overall, 528 (83.0%) patients completed LDCT. There were no statistically significant differences in completion by age, gender, race/ethnicity, or insurance status. Spanish-speaking patients were more likely to complete the CT than English speakers (OR 2.22, 95% CI 1.22, 4.41) and those who formerly smoked were more likely to complete the CT than patients who currently smoked (OR 1.93, 95% CI 1.12, 3.51). ConclusionsThe navigator-centered program achieved equitable implementation of lung cancer screening in a diverse FQHC system.

Gender-specific outcomes of low-dose computed tomography screening for lung cancer detection: A retrospective study in Chinese never-smoker population.

Low-dose computed tomography (LDCT) has emerged as a pivotal tool for detecting lung cancer among ever-smokers. This study aims to evaluate the gender-specific outcomes of LDCT screening within the Chinese never-smoking population. We conducted a single-center, retrospective cohort study, which analyzed LDCT screening outcomes for 42,018 asymptomatic participants. Specifically, we focused on assessing gender-specific differences in the prevalence of pulmonary nodules, and the incidence of lung cancer diagnosis among never-smokers. Among the 42,018 eligible participants, 41.50% were females and 58.50% were males. Most participants were non-smokers (77.57%), with a significantly higher proportion of non-smokers among females than males (99.33% vs. 62.14%). Pulmonary nodules were identified in 2.66% of participants, with a higher prevalence in females (2.99%) than males (2.43%) (p < 0.001). Non-smoking females had a higher incidence of positive nodules than non-smoking males (2.98% vs. 2.38%, p < 0.001). Invasive biopsies were performed in 334 individuals with nodules, confirming lung cancer in 258 cases. The majority of these cancer cases were non-smokers (212), with non-smoking females showing a higher incidence (0.85%) compared to males (0.43%) (p < 0.001). There was no significant difference in the false-positive rates between non-smoking females (0.14%) and males (0.13%). Multivariate analysis showed that never-smoking women were more likely to undergo biopsies (OR 1.65, p = 0.0016) and had a higher, though not statistically significant, probability of lung cancer diagnosis (OR 1.84, p = 0.06). This study elucidates sex-based differences within the Chinese population, revealing a higher prevalence of pulmonary nodules and lung cancers among non-smoking females. These findings offer valuable reference for both clinical practice and future research initiatives.

Application research on asthma-COPD overlap using low-dose CT scan and quantitative analysis

The aim of this study was to assess the proximal airway remodeling, emphysema, and air trapping of asthma-COPD Overlap. 20 ACO patients, 55 mild to moderate COPD patients and 38 non-severe asthma patients were participated in low-dose dual phase CT scanning and pulmonary function test, comparative analysis was performed to identify differences in CT measurements among three groups. (Ⅰ) The average age and smoking index of ACO and mild to moderate COPD were both higher than non-severe asthma. ACO and mild to moderate COPD group had a higher proportion of males than non-severe asthma. (Ⅱ) In terms of pulmonary function test, FEV1 (%Pred), FEV1/FVC%, MEF25% (%Pred), MEF 50% (%Pred), MMEF (%Pred), and PEF (%Pred) in ACO were significantly reduced than those in non-severe asthma. (Ⅲ) On proximal airway parameters, ACO exhibited higher WA% and Pi10 compared to mild to moderate COPD. However, there was no statistically significant difference in WA% and Pi10 between ACO and non-severe asthma. (Ⅳ) On CT lung function, the emphysema index VI-910ex of ACO was significantly higher than non-severe asthma.Additionally, ACO demonstrated higher MLDex and VI-856ex compared to non-severe asthma. Compared with non-severe asthma, ACO is more common in male patients with older age and a longer history of smoking, which had more severe airflow obstruction and airway dysfunction than non-severe asthma.ACO showed more obvious proximal airway remodeling than mild to moderate COPD, and was more similar to non-severe asthma.The extent of emphysema and air trapping in ACO were more pronounced compared to non-severe asthma.