Computed Tomography Screening Research Articles

Abstract A064: Lung cancer screening adherence among participants in DETECT-A, the first prospective interventional trial of a multi-cancer early detection (MCED) blood test

Abstract Background: Lung cancer is the leading cause of cancer death in the US. Adherence to guideline- recommended low-dose computed tomography screening is poor and late-stage diagnosis is common. MCED tests may improve early cancer detection if adherence to guideline- recommended single cancer screening is maintained. The DETECT-A study evaluated an MCED test in 9,911 women in Geisinger Health System (GHS), age 65-75, without personal cancer history. This analysis evaluates lung cancer screening adherence among DETECT-A participants to determine if adherence changed significantly following receipt of an MCED test. Methods: Monthly screening eligibility and adherence data (2013 USPSTF criteria) were available from September 2016 - May 2020 for DETECT-A participants and screening-eligible women in GHS who met DETECT-A eligibility criteria but did not participate in DETECT-A. A control group was constructed by randomly selecting GHS non-participants at a 7:1 ratio and assigning index months that matched the distribution of consent months for DETECT-A participants. All analyzed participants had ≥24 consecutive months of adherence data centered around the consent/index month. The proportion adherent at consent/index month and the end of active study participation at 12 months were calculated descriptively. Changes in adherence over time, as well as group comparisons, were assessed using a mixed effects logistic regression model. Results: DETECT-A participants (n=364) and controls (n=2,548) had similar age, race, and ethnicity distributions. The population was predominantly white (>98%) and non-Hispanic (>90%). The odds of adherence at 12 months were significantly higher than at consent/index for both DETECT-A participants (OR: 3.20 [1.04 -9.80], p=0.042) and controls (OR: 2.02 [1.23- 3.33], p=0.006). The increase in odds of adherence between consent/index and 12 months was not significantly different between the groups (OR: 1.58 [0.47-5.31], p=0.461). DETECT-A participant adherence was 28.0% (23.4%-32.6%) in the consent month and increased to 38.5% (33.5-43.5%) at 12 months. Among the 102 DETECT-A participants adherent in the consent month, 70 (68.6%) were also adherent at 12 months. Control adherence was 16.0% (14.6%-17.4%) in the index month and increased to 20.2% (18.7%-21.8%) at 12 months. Among 408 controls adherent in the index month, 257 (63.0%) were also adherent at 12 months. Conclusions: DETECT-A participation did not negatively affect lung cancer screening adherence among this subset of participants. Screening adherence increased over time in both DETECT-A participants and controls; most DETECT-A participants adherent in the study consent month were also adherent 12 months later. Effectiveness of GHS screening programs, DETECT-A participant self-selection, and DETECT-A educational efforts promoting screening may have contributed to these observations. Citation Format: Adam H. Buchanan, Anne Marie Lennon, Paul Z. Elias, Amy M. Lehman, Yongqiang Zhang, Darl D. Flake II, Eric S. Wagner, Seema P. Rego, Omair A. Choudhry, Nickolas Papadopoulos, Tomasz M. Beer. Lung cancer screening adherence among participants in DETECT-A, the first prospective interventional trial of a multi-cancer early detection (MCED) blood test [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A064.

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -The Recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.

Computed tomography morphological assessments of central airways in interstitial lung abnormalities and idiopathic pulmonary fibrosis

BackgroundLittle is known about whether central airway morphological changes beyond traction bronchiectasis develop and affect clinical outcomes in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to compare central airway structure comprehensively between patients with IPF, subjects with interstitial lung abnormality (ILA), and those without ILA (control) using computed tomography (CT). We further examined the prognostic impact of IPF-specific CT airway parameters in patients with IPF.MethodsThis retrospective study included male patients with IPF, and male health checkup subjects divided into those with ILA and control based on lung cancer screening CT. Using an artificial intelligence-based segmentation technique, the extent of fibrotic regions in the lung was quantified. After airway tree segmentation, CT parameters for central airway morphology, including the lumen area of the extrapulmonary airways (LAextra), wall and lumen area of the segmental/subsegmental intrapulmonary airways (WAintra and LAintra), tracheal distortion (tortuosity and curvature) and bifurcation angle of the main carina, were calculated.ResultsThere were 106 patients with IPF, 53 subjects with ILA, and 1295 controls. Multivariable models adjusted for age, height and smoking history revealed that LAintra and WAintra were larger in both ILA and IPF, and that tracheal tortuosity and curvature were higher in IPF, but not in ILA, than in the control, whereas the bifurcation angle did not differ between the 3 groups. According to multivariable Cox proportional hazards models including only patients with IPF, increased WAintra was significantly associated with greater mortality (standardized hazard ratio [95% confidence interval] = 1.58 [1.17, 2.14]), independent of the volume of fibrotic regions, normal-appearing regions, or the whole airway tree in the lung.ConclusionIncreased lumen area and wall thickening of the central airways may be involved in the pathogenesis of ILA and IPF, and wall thickening may affect the prognosis of patients with IPF.

Time on task effects during interactive visual search.

There is a major shift taking place in airports across the globe, changing from 2D dual-view X-ray screening to 3D computed tomography (CT) screening. 3D CT screening is believed to improve target detection since it enables screeners to interact with images of passenger baggage (i.e., rotating and zooming into the displays). The change in screening technology is moving what was once a purely visual search task to an interactive search task. Here, we conducted two experiments with a large sample size during February of 2023 (695 participants) to examine (a) changes in search performance between a simulated dual-view and simulated interactive search task and (b) the effects of time on task upon performance. Consistent with past research, we found that interactive search, when compared with dual-view search, produced higher response accuracy rates coupled with increased reaction times (RTs). However, while we found effects of time on task (RTs reduced, and participants became more likely to respond "absent" as the experiments progressed), there was no evidence that these effects differed across simulated dual-view and simulated interactive searches. The results are discussed in relation to benefits of interactive search for supporting target detection by airport screeners. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Opportunistic Screening of Bone Fragility Using Computed Tomography.

Opportunistic screening uses existing imaging studies for additional diagnostic insights without imposing further burden on patients. We explore the potential of opportunistic computed tomography (CT) screening for osteoporosis, a condition affecting 500 million people globally and leading to significant health care costs and fragility fractures. Although dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosing osteoporosis, > 50% of fractures occur in individuals not screened previously with DXA. With recent advancements in technology, CT has emerged as the most promising tool for opportunistic screening due to its wide use and the ability to provide quantitative measurements of bone attenuation, a surrogate of bone mineral density. This article discusses the technical considerations, calibration methods, and potential benefits of CT for osteoporosis screening. It also explores the role of automation, supervised and unsupervised, in streamlining the diagnostic process, improving accuracy, and potentially developing new biomarkers of bone health. The potential addition of radiomics and genomics is also highlighted, showcasing the synergy between genetic and imaging data for a more comprehensive understanding of osteoporosis pathophysiology and with it possible novel osteoporosis therapies. The future of opportunistic CT screening holds significant promise, with automation and advanced image processing ultimately enhancing patient care, reducing rates of osteoporotic fractures, and improving patient outcomes.

P4.04C.08 Sensitivity of Artificial Intelligence in Low-dose Computed Tomography Screening for Lung Cancer

P4.04C.08 Sensitivity of Artificial Intelligence in Low-dose Computed Tomography Screening for Lung Cancer

Preoperative Chest Computed Tomography Screening Reduces the Perioperative Stroke Rate in Patients at Risk of Aortic Calcification.

We evaluated the effect of preoperative chest computed tomography (CT) screening on the perioperative stroke rate in cardiosurgical patients at risk of aortic calcification. Between May 2019 and April 2020, 129 patients at risk of aortic calcification underwent non-contrast chest CT screening before their procedure. They were assigned to Group 1 and compared with a historical Group 2, who were treated the previous year without a preoperative CT scan. The primary endpoint was to determine postoperative stroke occurrence. The secondary outcomes were the rate reintubation/tracheostomy, the length of hospital stay, and any change in surgical strategy based on the CT findings. Groups 1 and 2 comprised 129 and 261 patients, respectively. Group 1 had a lower left ventricular ejection fraction, less carotid stenosis, a history of carotid endarterectomy, and a longer cross-clamp time. The surgical strategy was changed for 6 patients in Group 1. Group 1 had a significantly lower stroke rate. No significant differences were observed in reintubation and tracheostomy rates, or length of hospital stay. Lack of CT screening, age, aortic valve surgery, aortic surgery, and rethoracotomy were identified as independent risk factors for a stroke. Preoperative non-contrast chest CT screening of patients at risk of aortic calcification reduces postoperative stroke through adaptation of the surgical approach and should be used routinely in these patients.

Utilizing Un-enhanced Chest Computed Tomography Screening for Blunt Trauma Surgery Decisions.

Background Blunt chest trauma is a common and potentially life-threatening condition that requires prompt assessment for potential surgical intervention. Computed tomography (CT) of the chest has emerged as a valuable tool due to its heightened sensitivity and specificity in detecting thoracic injuries compared to conventional chest radiography. Objective This study aims to assess the impact of non-contrast CT chest findings on surgical decision-making and compare these findings with those from chest radiographs. Methods The study was conducted at the Accident and Emergency Department of Medical Teaching Institute, Lady Reading Hospital, Peshawar, Khyber Pakhtunkhwa, Pakistan. Patients of all ages and genders who presented with blunt chest trauma were included. Non-contrast CT chest scans were used as an initial screening tool and compared with traditional chest radiographs. Data collected included patient demographics, mechanism of injury, diagnostic findings, and treatment decisions. Imaging was performed using a GE Optima 16-slice scanner (Medsystems Sp. z o.o., Lublin, Poland). Results The study included 246 patients, of whom 210 (85.4%) were males. The most common age group was 50 years or older, comprising 71 (28.9%) of the sample. The predominant mechanism of trauma was road traffic accidents, reported by 188 (76.4%) patients. Hemopneumothorax was detected in 121 (49.2%) patients on CT scans compared to 34 (13.8%) patients on chest radiographs. On chest radiograph, the pneumothorax component was missed in 43 (17.5%) patients, and the hemothorax component was not detected in 21 (8.5%) patients. Patient management included conservative management in 30 (12.2%) cases and surgical intervention in the form of unilateral tube thoracostomy in 173 (70.3%) patients or bilateral tube thoracostomy in 43 (17.5%) patients. Conclusion Our study supports the use of non-contrast CT scans as a reliable diagnostic tool for blunt chest trauma, consistent with current literature. This approach facilitates prompt management decisions, particularly for initiating tube thoracostomy based on findings of pneumothorax and hemothorax. The rarity of mediastinal great vessel trauma further justifies minimizing routine contrast use, thereby enhancing the efficiency of trauma evaluations.

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.

Association between incidental perirenal fat stranding on CT and metabolic syndrome in otherwise healthy adults.

To investigate the association between metabolic syndrome and perirenal fat stranding (PRFS), which is defined as linear or curvilinear soft tissue densities in the perirenal fat on computed tomography (CT). Adults who had abdominal CT for health screening at a single institution between October 2022 and March 2023 were included retrospectively. Two radiologists assessed the extent of PRFS for each CT and graded it as absent, mild/moderate, and severe. Logistic regression analyses were used to investigate the associations between PRFS and metabolic syndrome-related factors, as well as age and gender. Among 701 participants (mean age, 56.8years ± 9.7; 336 women and 365 men), 87 (12.4%) had mild (n = 80) or moderate (n = 7) PRFS. None had severe PRFS. The presence of PRFS was independently associated with higher body mass index (odds ratio [OR], 2.561 and 9.842 for overweight and obese, respectively; p ≤ 0.001), elevated blood pressure with or without anti-hypertensive medication (OR, 2.232; p = 0.015), anti-diabetic medication (OR, 3.129; p < 0.001), and lipid-lowering medication (OR, 1.919; p = 0.019), older age (OR, 4.545 and 9.109 for 50-59years and ≥ 60years, respectively; p ≤ 0.002), and male gender (OR, 10.065; p < 0.001). Sixty three of 87 (72.4%) participants with PRFS had metabolic syndrome, while 265 of 614 (43.2%) participants without PRFS did (p < 0.001). Incidental mild or moderate PRFS may be associated with the presence of metabolic syndrome or related disorders in otherwise healthy adults.

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.

Short-term impact of cardiovascular screening by traditional risk assessment or coronary artery calcium score on health-related quality of life: the ROBINSCA trial.

Evidence on the impact of screening for cardiovascular diseases (CVDs) on health-related quality of life (HRQoL) is important for policy decisions about screening implementation and to uncover teachable moments to motivate healthy lifestyle choices. It is unknown whether screening by cardiac computed tomography (CT) scan has a stronger impact on HRQoL than screening by traditional risk prediction models. The study aims to investigate differences in HRQoL across the screening process between participants who were randomized to CVD risk estimation by coronary artery calcium score or Systematic COronary Risk Evaluation. A subset of 2687 ROBINSCA participants filled in questionnaires at (T0) randomization, (T1) invitation, (T2) 1-3 days before screening, (T3) 1-3 days after, and (T4) screening result. Generic HRQoL (SF-12; EQ-5D) and anxiety (STAI-6) were measured. We investigated the differences in changes in HRQoL across the screening process with linear mixed models. We found comparable levels of HRQoL at all screening moments for the two intervention groups. Mental health scores were worse at invitation and randomization than at the later time points, irrespective of screening group (all P < 0.001). A result indicating a heightened CVD risk was associated with increased anxiety in the CT screening group. Computed tomography screening for CVD risk has no detrimental impact on HRQoL and anxiety levels compared to screening by traditional risk assessment. Receiving an invitation to screenning or a result implying increased CVD risk could function as teachable moments for high-risk individuals. ROBINSCA trial registration number: NTR6471 in Dutch Trial Register (NTR).

Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model.

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies. This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ. Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018. When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

PP01.169 Promotion of Low-Dose Computed Tomography Screening and Blood Biomarkers in Minority Communities for Early Detection of Lung Cancer in Northern Illinois

PP01.169 Promotion of Low-Dose Computed Tomography Screening and Blood Biomarkers in Minority Communities for Early Detection of Lung Cancer in Northern Illinois

Impact of lung cancer screening on stage migration and mortality among the national Veterans Health Administration population with lung cancer.

Despite randomized trials demonstrating a mortality benefit to low-dose computed tomography screening to detect lung cancer, uptake of lung cancer screening (LCS) has been slow, and the benefits of screening remain unclear in clinical practice. This study aimed to assess the impact of screening among patients in the Veterans Health Administration (VA) health care system diagnosed with lung cancer between 2011 and 2018. Lung cancer stage at diagnosis, lung cancer-specific survival, and overall survival between patients with cancer who did and did not receive screening before diagnosis were evaluated. We used Cox regression modeling and inverse propensity weighting analyses with lead time bias adjustment to correlate LCS exposure with patient outcomes. Of 57,919 individuals diagnosed with lung cancer in the VA system between 2011 and 2018, 2167 (3.9%) underwent screening before diagnosis. Patients with screening had higher rates of stage I diagnoses (52% vs. 27%; p ≤ .0001) compared to those who had no screening. Screened patients had improved 5-year overall survival rates (50.2% vs. 27.9%) and 5-year lung cancer-specific survival (59.0% vs. 29.7%) compared to unscreened patients. Among screening-eligible patients who underwent National Comprehensive Cancer Network guideline-concordant treatment, screening resulted in substantial reductions in all-cause mortality (adjusted hazard ratio [aHR], 0.79; 95% confidence interval [CI], 0.67-0.92; p=.003) and lung-specific mortality (aHR, 0.61; 95% CI, 0.50-0.74; p<.001). While LCS uptake remains limited, screening was associated with earlier stage diagnoses and improved survival. This large national study corroborates the value of LCS in clinical practice; efforts to widely adopt this vital intervention are needed.

WS11.04 Evaluation of lung computed tomography screening of patients with cystic fibrosis according to a modified Lung-RADS score

WS11.04 Evaluation of lung computed tomography screening of patients with cystic fibrosis according to a modified Lung-RADS score

EE329 Budget Impact of Low-Dose Computed Tomography Screening for Lung Cancer in Argentina

EE329 Budget Impact of Low-Dose Computed Tomography Screening for Lung Cancer in Argentina

Predicting Lung Cancer Survival to the Future: Population-Based Cancer Survival Modeling Study.

Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025. Using the Taiwan Cancer Registry, this study uses the survivorship-period-cohort model to assess the feasibility of achieving this goal by predicting future survival rates of patients with lung cancer in Taiwan. This retrospective study analyzed data from 205,104 patients with lung cancer registered between 1997 and 2018. Survival rates were calculated using the survivorship-period-cohort model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error. The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013. Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance's goal could be within reach. The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes.