Computed Tomography Lung Screening Research Articles

Factors Associated With Delay in Lung Cancer Diagnosis and Surgery in a Lung Cancer Screening Program.

Delays to biopsy and surgery after lung nodule detection can impact survival from lung cancer. The aim of this study was to identify factors associated with delay in a lung cancer screening (LCS) program. We evaluated patients in an LCS program from May 2015 through October 2021 with a malignant lung nodule classified as lung CT screening reporting and data system (Lung-RADS) 4B/4X. A cutoff of more than 30 days between screening computed tomography (CT) and first tissue sampling and a cutoff of more than 60 days between screening CT and surgery were considered delayed. We evaluated the relationship between delays to first tissue sampling and surgery and patient sex, age, race, smoking status, median income by zip code, language, Lung-RADS category, and site of surgery (academic vs community hospital). A total of 185 lung cancers met the inclusion criteria, of which 150 underwent surgical resection. The median time from LCS CT to first tissue sampling was 42 days, and the median time from CT to surgery was 52 days. 127 (69%) patients experienced a first tissue sampling delay and 60 (40%) had a surgical delay. In multivariable analysis, active smoking status was associated with delay to first tissue sampling (odds ratio: 3.0, CI: 1.4-6.6, P = 0.005). Only performing enhanced diagnostic CT of the chest before surgery was associated with delayed lung cancer surgery (odds ratio: 30, CI: 3.6-252, P = 0.02). There was no statistically significant difference in delays with patients' sex, age, race, language, or Lung-RADS category. Delays to first tissue sampling and surgery in a LCS program were associated with current smoking and performing diagnostic CT before surgery.

Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points

Screening with low-dose computed tomography (CT) has been shown to reduce mortality from lung cancer in randomized clinical trials in which the rate of adherence to follow-up recommendations was over 90%; however, adherence to Lung Computed Tomography Screening Reporting & Data System (Lung-RADS) recommendations has been low in practice. Identifying patients who are at risk of being nonadherent to screening recommendations may enable personalized outreach to improve overall screening adherence. To identify factors associated with patient nonadherence to Lung-RADS recommendations across multiple screening time points. This cohort study was conducted at a single US academic medical center across 10 geographically distributed sites where lung cancer screening is offered. The study enrolled individuals who underwent low-dose CT screening for lung cancer between July 31, 2013, and November 30, 2021. Low-dose CT screening for lung cancer. The main outcome was nonadherence to follow-up recommendations for lung cancer screening, defined as failing to complete a recommended or more invasive follow-up examination (ie, diagnostic dose CT, positron emission tomography-CT, or tissue sampling vs low-dose CT) within 15 months (Lung-RADS score, 1 or 2), 9 months (Lung-RADS score, 3), 5 months (Lung-RADS score, 4A), or 3 months (Lung-RADS score, 4B/X). Multivariable logistic regression was used to identify factors associated with patient nonadherence to baseline Lung-RADS recommendations. A generalized estimating equations model was used to assess whether the pattern of longitudinal Lung-RADS scores was associated with patient nonadherence over time. Among 1979 included patients, 1111 (56.1%) were aged 65 years or older at baseline screening (mean [SD] age, 65.3 [6.6] years), and 1176 (59.4%) were male. The odds of being nonadherent were lower among patients with a baseline Lung-RADS score of 1 or 2 vs 3 (adjusted odds ratio [AOR], 0.35; 95% CI, 0.25-0.50), 4A (AOR, 0.21; 95% CI, 0.13-0.33), or 4B/X, (AOR, 0.10; 95% CI, 0.05-0.19); with a postgraduate vs college degree (AOR, 0.70; 95% CI, 0.53-0.92); with a family history of lung cancer vs no family history (AOR, 0.74; 95% CI, 0.59-0.93); with a high age-adjusted Charlson Comorbidity Index score (≥4) vs a low score (0 or 1) (AOR, 0.67; 95% CI, 0.46-0.98); in the high vs low income category (AOR, 0.79; 95% CI, 0.65-0.98); and referred by physicians from pulmonary or thoracic-related departments vs another department (AOR, 0.56; 95% CI, 0.44-0.73). Among 830 eligible patients who had completed at least 2 screening examinations, the adjusted odds of being nonadherent to Lung-RADS recommendations at the following screening were increased in patients with consecutive Lung-RADS scores of 1 to 2 (AOR, 1.38; 95% CI, 1.12-1.69). In this retrospective cohort study, patients with consecutive negative lung cancer screening results were more likely to be nonadherent with follow-up recommendations. These individuals are potential candidates for tailored outreach to improve adherence to recommended annual lung cancer screening.

Intramedullary spinal cavernous malformations with high ossification: a case report and review of the literature

BackgroundCavernous malformations of the spinal cord are a rare type of vascular malformation, comprising approximately 5 to 16% of all vascular lesions in the spinal cord. Depending on their origin position, these malformations can be distributed in different locations within the spinal canal. Although intramedullary cavernous malformations have been reported in the literature, they are exceedingly rare. Furthermore, highly calcified or ossified intramedullary cavernous spinal malformations are even rarer.Case presentationHere, we present a case report of a 28-year-old woman diagnosed with a thoracic intramedullary cavernous malformation. The patient had been experiencing progressive numbness in her distal limbs for a period of 2 months. During routine lung computed tomography screening for COVID-19, a hyperdense mass was noted in the patient’s spinal canal. Magnetic resonance imaging revealed a mulberry-shaped intramedullary mass at the T1-2 level. The patient underwent surgical treatment, during which the entire lesion was successfully removed, resulting in a gradual improvement of her symptoms. Histological examination confirmed the presence of cavernous malformations with calcification.ConclusionsIntramedullary cavernous malformations with calcification are rare and special type that should be treated surgically in the early stage without significant neurological impairment before rebleeding or enlargement of the lesion can occur.

Delays in Recommended Follow-Up after Positive Findings in Lung Cancer Screening.

Rationale: Lung cancer screening (LCS) is an effective tool to reduce mortality. However, barriers along the LCS care continuum, including delay in follow-up care, may reduce effectiveness. Objectives: The primary goals of this study were to evaluate delays in follow-up in patients with positive findings on LCS and to examine the impact of delay on lung cancer staging. Methods: This was a retrospective cohort study of patients enrolled in a multisite LCS program with positive LCS findings, defined as Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) 3, 4A, 4B, or 4X. Time to first follow-up was evaluated with delay considered >30 days beyond the standardized Lung-RADS recommendation. Multivariable Cox models were used to evaluate the likelihood of delay by Lung-RADS category. Participants with resultant non-small cell lung cancer were evaluated to determine if delay in follow-up was associated with clinical upstaging. Results: Three hundred sixty-nine patients with 434 examinations had positive findings; 16% of findings were ultimately diagnosed as lung cancer. In 47% of positive examinations, there was a delay in follow-up (median delay, 104 d), representing 59% (210 d) of Lung-RADS 3 examinations, 35% (64 d) of Lung-RADS 4A examinations, and 40% (34 d) of Lung-RADS 4B/4X examinations (P < 0.001). In the 54 patients diagnosed with non-small cell lung cancer through LCS, delay was associated with increased likelihood of clinical upstaging (P < 0.001). Conclusions: In this study of delay in follow-up after positive LCS findings, we found that nearly half of patients had delays in follow-up and that delay was associated with clinical upstaging in patients whose positive findings represented lung cancer. Further targeted interventions to ensure timely follow-up after positive LCS examination are critical.

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software

To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD). A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference. Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively. Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

PD58 Implementation And Assessment Of A Lung Cancer Screening Pilot Project In Québec Through Multi-Stakeholder Collaboration

IntroductionIn 2019, the Québec provincial health technology assessment body (INESSS) recommended that lung cancer screening with low-dose computed tomography (LDCT) be accessible in Québec only within the context of an evaluation in the ‘real-world’ care setting. Based on this recommendation, the ministry of health (MSSS) decided, in 2020, to implement a screening pilot project and to conduct a formal evaluation, partnering with a clinical leader (principal investigator), participating hospitals, the provincial public health agency (INSPQ) and INESSS. The goal of this evaluation is to facilitate decision-making regarding the implementation of a province-wide screening program.MethodsTo support the implementation of the pilot project, algorithms and recommendations were developed to guide management of screening program participants. This material, based on Lung-RADS (Lung Computed Tomography Screening Reporting and Data System of the American College of Radiology), was developed by reviewing the literature and by consulting clinical experts. The evaluation plan proposes various indicators, focusing on six main topics: (i) costs, (ii) screening and investigation processes, (iii) clinical effectiveness and other effects on health, (iv) effects on smoking cessation, (v) organizational impact and (vi) implementation issues.ResultsINESSS has developed 12 algorithms and close to 50 recommendations for lung cancer screening and investigation, a tool for assessing lung cancer risk and a benefits/risks table. For the evaluation of the pilot project, MSSS, INSPQ and INESSS developed more than 100 indicators; short-term indicators are currently being measured and others will follow in the longer term. Since starting in June 2021, the pilot project is progressing well (as of November 28, 2021): 2,365 people have been referred, 1,272 were eligible for screening, 678 have had their first LDCT and 19 were Lung-RADS 4B or 4X. Results on indicators will help the ministry decide on the feasibility of scaling up screening to the provincial level and will highlight aspects to be improved.ConclusionsThis project shows how health technology assessment products can elicit changes in the health system, and how multi-stakeholder collaboration can actively support practice implementation and inform decision-making.

A Cost-Effectiveness Analysis of Lung Cancer Screening With Low-Dose Computed Tomography and a Diagnostic Biomarker.

BackgroundThe Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) reduces the false-positive rate of lung cancer screening but introduces prolonged periods of uncertainty for indeterminate findings. We assess the cost-effectiveness of a screening program that assesses indeterminate findings earlier via a hypothetical diagnostic biomarker introduced in place of Lung-RADS 3 and 4A guidelines.MethodsWe evaluated the performance of the US Preventive Services Task Force (USPSTF) recommendations on lung cancer screening with and without a hypothetical noninvasive diagnostic biomarker using a validated microsimulation model. The diagnostic biomarker assesses the malignancy of indeterminate nodules, replacing Lung-RADS 3 and 4A guidelines, and is characterized by a varying sensitivity profile that depends on nodules' size, specificity, and cost. We tested the robustness of our findings through univariate sensitivity analyses.ResultsA lung cancer screening program per the USPSTF guidelines that incorporates a diagnostic biomarker with at least medium sensitivity profile and 90% specificity, that costs $250 or less, is cost-effective with an incremental cost-effectiveness ratio lower than $100 000 per quality-adjusted life year, and improves lung cancer-specific mortality reduction while requiring fewer screening exams than the USPSTF guidelines with Lung-RADS. A screening program with a biomarker costing $750 or more is not cost-effective. The health benefits accrued and costs associated with the screening program are sensitive to the disutility of indeterminate findings and specificity of the biomarker, respectively.ConclusionsLung cancer screening that incorporates a diagnostic biomarker, in place of Lung-RADS 3 and 4A guidelines, could improve the cost-effectiveness of the screening program and warrants further investigation.

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models. Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets. The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.

Quantitative Pectoralis Muscle Area is Associated with the Development of Lung Cancer in a Large Lung Cancer Screening Cohort.

Studies have demonstrated an inverse relationship between body mass index (BMI) and the risk of developing lung cancer. We conducted a retrospective cohort study evaluating baseline quantitative computed tomography (CT) measurements of body composition, specifically muscle and fat area in a large CT lung screening cohort (CTLS). We hypothesized that quantitative measurements of baseline body composition may aid in risk stratification for lung cancer. Patients who underwent baseline CTLS between January 1st, 2012 and September 30th, 2014 and who had an in-network primary care physician were included. All patients met NCCN Guidelines eligibility criteria for CTLS. Quantitative measurements of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) were performed on a single axial slice of the CT above the aortic arch with the Chest Imaging Platform Workstation software. Cox multivariable proportional hazards model for cancer was adjusted for variables with a univariate p < 0.2. Data were dichotomized by sex and then combined to account for baseline differences between sexes. One thousand six hundred and ninety six patients were included in this study. A total of 79 (4.7%) patients developed lung cancer. There was an association between the 25th percentile of PMA and the development of lung cancer [HR 1.71 (1.07, 2.75), p < 0.025] after adjusting for age, BMI, qualitative emphysema, qualitative coronary artery calcification, and baseline Lung-RADS® score. Quantitative assessment of PMA on baseline CTLS was associated with the development of lung cancer. Quantitative PMA has the potential to be incorporated as a variable in future lung cancer risk models.

Cost-Effectiveness of Follow-Up for Subsolid Pulmonary Nodules in High-Risk Patients.

To evaluate the cost-effectiveness of a number of follow-up guidelines and variants for subsolid pulmonary nodules. We used a simulation model informed by data from the literature and the National Lung Screening Trial to simulate patients with ground-glass nodules (GGNs) detected at baseline computed tomography undergoing follow-up. The nodules were allowed to grow and develop solid components over time. We tested the guidelines generated by varying follow-up recommendations for low-risk nodules, that is, pure GGNs or those stable over time. For each guideline, we computed average US costs and quality-adjusted life-years (QALYs) gained per patient and identified the incremental cost-effectiveness ratios of those on the efficient frontier. In addition, we compared the costs and effects of the most recently released version of the Lung Computed Tomography Screening Reporting and Data System (Lung-RADS), version 1.1, with those of the previous version, 1.0. Finally, we performed sensitivity analyses of our results by varying several relevant parameters. Relative to the no follow-up scenario, the follow-up guideline system that was cost-effective at a willingness-to-pay of $100,000/QALY and had the greatest QALY assigned low-risk nodules a 2-year follow-up interval and stopped follow-up after 2 years for GGNs and after 5 years for part-solid nodules; this strategy yielded an incremental cost-effectiveness ratio of $99,970. Lung-RADS version 1.1 was found to be less costly but no less effective than Lung-RADS version 1.0. These findings were essentially stable under a range of sensitivity analyses. Ceasing follow-up for low-risk subsolid nodules after 2 to 5 years of stability is more cost-effective than perpetual follow-up. Lung-RADS version 1.1 was cheaper but similarly effective to version 1.0.

Possibilities of single photon emission tomography in the diagnosis of bone metastases in patients with disseminated medullary thyroid carcinoma (case report)

The study objective : using a clinical example to demonstrate possibilities of single photon emission computerized tomography (SPECT) in combination with computed tomography (CT) in identifying latent bone metastases, taking into account the dynamics growth of serum basal calcitonin. Materials and methods . Patient S., 60 years old, visited N.N. Blokhin National Medical Research Center of Oncology for consultation on multiple lung metastases of cancer of unknown primary. Results. Taking into account basal calcitonin level, immunohistochemistry, ultrasound investigation andfine-needle aspiration biopsy of the node in the right thyroid lobe a diagnose of medullary thyroid cancer was made. CT revealed multiple metastases in both lungs. Specialist erformed thyroidectomy with central lymphadenectomy and facial neck dissection on both sides. During next four months basal calcitonin level increased twice. Control contrast CT lung screening showed the growth of previously identified metastases and the appearance of multiple new ones. Bone scan showed focuses of increased radio-pharmaceutical accumulation in the area of 7 h left rib, left iliac wing, in the left bones of cranial vault, in C 7 , Th 6 , Th 9 , Th I2 vertebrae and right foot bones. Additional examination using SPECT/CT (from the skull base to the hip joints) revealed metastases in corpuses of Th 9 , Th I2 vertebrae and the left iliac wing, and suspicion for metastasis in 7 h left rib. Magnetic resonance imaging (MRI) confirmed metastasis in Th 9 , Th I2 , L 3 vertebral bodies and in the left iliac wing. Conclusion. Conclusion . In this clinical example, SPECT/CT allowed to correctly detect metastases in both Th 9 and Th I2 vertebrae while bone scan was questionable, and MRI showed an additional damage of L 3 vertebra. Changes in the 7 h left rib could not be verified using CT, although this changes may be an emerging metastasis. Obviously, extensive use of radiation methods does not guarantee complete identification of all pathological focuses, it therefore allows assessing the prevalence of the pathological process. It is crucially important to control calcitonin level in these patients, since its rapid growth allowed suspecting additional distant metastases.

Lung-RADS: Pushing the Limits.

In response to the recommendation of the U.S. Preventive Services Task Force and the coverage decision by the Centers for Medicare and Medicaid Services for lung cancer screening (LCS) computed tomography (CT), the American College of Radiology introduced the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screening-detected lung nodules. As with many first-edition guidelines, questions arise when such reporting systems are used in daily practice. In this article, a collection of 15 LCS-related scenarios are presented that address situations in which the Lung-RADS guidelines are unclear or situations that are not currently addressed in the Lung-RADS guidelines. For these 15 scenarios, the authors of this article provide the reader with recommendations that are based on their collective experiences, with the hope that future versions of Lung-RADS will provide additional guidance, particularly as more data from widespread LCS are collected and analyzed. ©RSNA, 2017.

Lung Computed Tomography Screening Reporting and Data System Version 1.0

Lung Computed Tomography Screening Reporting and Data System Version 1.0

Abstract IA18: Development and validation of risk models to select ever-smokers for CT lung-cancer screening

Abstract The US Preventive Services Task Force (USPSTF) recommends computed-tomography (CT) lung-cancer screening for ever-smokers ages 55-80 years who smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung-cancer risk calculations may be more effective and efficient than current USPSTF recommendations. We compare of modeled outcomes from risk-based CT lung-screening strategies versus USPSTF recommendations. We developed empirical risk models for lung-cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age, education, sex, race, smoking intensity/duration/quit-years, Body Mass Index, family history of lung-cancer, and self-reported emphysema. Models were validated in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the US. Models applied to US ever-smokers ages 50-80 (NHIS 2010-2012) to estimate outcomes of risk-based selection for annual CT lung-screening for 3 years, assuming screening for all ever-smokers yields the percent changes in lung-cancer detection and death observed in the NLST. Lung-cancer incidence and death risk models were well-calibrated in PLCO and NLST. The lung-cancer death model calibrated and discriminated well for US ever-smokers ages 50-80 (NHIS 1997-2001: Estimated/Observed=0.94, 95%CI=0.84-1.05; AUC=0.78, 95%CI=0.76-0.80). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung-cancer screening and 46,488 (95%CI=43,924-49,053) lung-cancer deaths were estimated as screen-avertable over 5 years (estimated NNS=194, 95%CI=187-201). In contrast, risk-based selection screened the same number of ever-smokers (9.0 million) at highest 5-year lung-cancer risk (≥1.9%), was estimated to avert 20% more deaths (55,717; 95%CI=53,033-58,400) and was estimated to reduce the estimated NNS by 17% (NNS=162, 95%CI=157-166). Among a cohort of US ever-smokers age 50-80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung-cancer deaths prevented over 5 years along with a lower NNS to prevent 1 lung-cancer death. Citation Format: Hormuzd A. Katki, Stephanie A. Kovalchik, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi. Development and validation of risk models to select ever-smokers for CT lung-cancer screening. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA18.

SC24.02 Radiological Techniques for the Evaluation of Pulmonary Nodules

Radiologic Techniques for the Evaluation of Pulmonary Nodules The incidental detection of pulmonary nodules has increased with improved CT technology and thin section imaging techniques.1,2 Adding to this increased detection of nodules is the heightened interest in the purposeful search for nodules such as in oncology patients and lung cancer screening programs. The management of CT detected nodules is a subject of much debate and dependent upon the clinical setting. For instance, in a lung cancer screening setting, there has been a large volume of investigation of solid, semi-solid and ground glass nodules that is the foundation of management recommendations such as LungRads.3 In patients with a known malignancy, there is minimal literature on management recommendations and thus more influenced by pulmonary metastatic potential of the malignancy and clinician experience.4 Finally incidentally detected nodule management is greatly influenced by cancer risk factors and nodule texture; for these situations, the Fleischner criteria have been the most widely used and accepted management guidelines.5 The radiologic evaluation of nodules most often utilizes conventional imaging techniques of chest radiographs, computed tomography (CT), PET/CT. Occasionally MRI and ultrasound may be employed. Most recent changes involve risk stratification, computer software applications to enhance nodule analysis such as nodule enhancement patterns, volumetric computations, and texture analysis.6-8 Future directions include incorporation of genomics into imaging as well as radiomic analysis and machine learning.9,10 This presentation will review the highlights of the radiologic methods for evaluating pulmonary nodules with a focus on current guidelines and future directions. 1. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer imaging : the official publication of the International Cancer Imaging Society 2012;12:41-8. 2. Jacobs PC, Mali WP, Grobbee DE, van der Graaf Y. Prevalence of incidental findings in computed tomographic screening of the chest: a systematic review. Journal of computer assisted tomography 2008;32:214-21. 3. Lung CT Screening Reporting and Data Systen (Lung-RADS). 2014. (Accessed March 27, 2015, at www.acr.org/Quality-Safety/Resources/LungRADS) 4. Munden RF, Erasmus JJ, Wahba H, Fineberg NS. Follow-up of small (4 mm or less) incidentally detected nodules by computed tomography in oncology patients: a retrospective review. J Thorac Oncol 2010;5:1958-62. 5. McMahon PM, Meza R, Plevritis SK, et al. Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling. PloS one 2014;9:e99978. 6. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med 2013;369:910-9. 7. Revel MP, Merlin A, Peyrard S, et al. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol 2006;187:135-42. 8. Talwar A, Gleeson FV, Rahman NM, Pickup L, Gooding M, Kadir T. A Review Of The Use Of Computer Aided Texture Analysis For Pulmonary Nodules Classification. American journal of respiratory and critical care medicine 2015;191. 9. El-Zein RA, Lopez MS, D'Amelio AM, Jr., et al. The cytokinesis-blocked micronucleus assay as a strong predictor of lung cancer: extension of a lung cancer risk prediction model. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2014;23:2462-70. 10. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563-77. Radiomics, Incidental, nodules, CT

Size and Growth Assessment of Pulmonary Nodules: Consequences of the Rounding

ObjectivesThe aim of this study was to understand the effect of rounding nodule size measurements on the frequency of positive results in the setting of lung cancer screening. MethodsFour methods for determining nodule size were compared, including rounding each individual length and width measurement and also rounding the overall average. These were applied to the International Early Lung Cancer Action Program database, in which we determined the frequency of a positive result by using standard size thresholds of 6.0 mm on baseline screening and 3.0 mm on repeat scanning. We also explored how rounding influences the ability to measure growth according to a predefined cutoff value of 1.5 mm as required in the Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) (version 1.0). ResultsEach method for rounding increased the frequency of positive results compared with that with no rounding. The largest increases—28.9% and 22.3% for the baseline and repeat round, respectively—occurred when rounding was used for both the individual length and width measurements and again for the final diameter. If the 1.5-mm increase in size were used for determining growth, a 4-mm nodule would need to have a volume doubling time of 130.6 days or less to demonstrate growth in 6 months (Lung-RADS category 3) whereas a 6-mm nodule would need volume doubling time of 93.2 days to demonstrate growth in 3 months (Lung-RADS category 4A). In addition, rounding can have the effect of having nodules that appear to not be growing meet the criteria for growth and vice versa. ConclusionsRounding influences the frequency of positive results and growth assessment, substantially decreasing the efficiency of screening.

Abstract 2596: Time to first morning cigarette and lung cancer in National Lung Screening Trial

Abstract In the National Lung Screening Trial (NLST), the effectiveness of lung cancer screening by low-dose computed tomography (CT) depended on individual lung cancer risk. Time to fist cigarette (TTFC) is an easily assessed component of nicotine dependency that may be an independent predictor of lung cancer risk. We validated the independent association of TTFC with lung cancer and assessed whether including TTFC into risk prediction models might improve CT screening effectiveness. We examined associations between TTFC and lung cancer incidence and mortality in the 18,729 participants of the ACRIN component of the NLST, which included 773 lung cancer diagnoses and 299 lung cancer deaths. Multivariable cox-proportional hazard regression models, adjusted for other metrics of smoking use and lung cancer risk factors, were used. In terms of screening effectiveness, we predicted each participant's 5-year cumulative lung cancer death risk using multivariable models that excluded or included TTFC. We then investigated 3 possible screening strategies based on risk scores from each model (for a total of six strategies): in each model scenario, those with a 5-year risk score above one of three thresholds (0.5%, 0.75%, and 1%) would be screened, whereas others would not. For each risk threshold, we compared screening sensitivities (the percentage who would have been screened among smokers who died of lung cancer) and specificities (the percentages who would not have been screened among smokers without lung cancer) between models without and with TTFC. We also estimated the absolute reduction in lung cancer death risk, due to screening, within each TTFC group (&amp;lt;15, 15-59, ≥60 minutes (min)). Compared to smokers with TTFC≥60 min, smokers with shorter TTFC had higher lung cancer incidence [hazard ratios (HR) and 95% confidence intervals (CI) for TTFC 30-59 min = 1.69(1.19-2.41), HR15-29min = 1.72(1.22-2.43), HR6-14min = 2.05(1.48-2.84), HR&amp;lt;5min = 2.19(1.59-3.02), p-trend&amp;lt;0.001] and mortality [HR30-59min = 2.10(1.11-3.96), HR15-29min = 1.91(1.02-3.59), HR6-14min = 2.59(1.43-4.69), HR&amp;lt;5min = 2.77(1.54-4.97), p-trend&amp;lt;0.001]. With regards to lung cancer screening, fewer lung cancer deaths would be avoided by screening smokers with TTFC≥60 min compared to screening smokers with TTFC 15-59 min or TTFC&amp;lt;15 min (2.03 vs. 4.99 vs. 7.38 lung cancer deaths avoided per 10000 person-years, respectively). Adding TTFC to the multivariable risk prediction algorithm increased the specificity of screening for all three risk thresholds by 1.6-2% (p&amp;lt;0.01) while maintaining sensitivity. We confirmed the independent inverse association between TTFC and lung cancer in NLST. Smokers with a TTFC greater than 60 min had less benefit from CT screening than smokers with a shorter TTFC. By increasing specificity and maintaining sensitivity, including TTFC in risk prediction models may more precisely identify those who benefit less from CT lung screening. Citation Format: Fangyi Gu, Hormuzd Katki, Neal Freedman, Pam Marcus, Neil Caporaso. Time to first morning cigarette and lung cancer in National Lung Screening Trial. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2596.

Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening.

The US Preventive Services Task Force (USPSTF) recommends computed tomography (CT) lung cancer screening for ever-smokers aged 55 to 80 years who have smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung cancer risk calculations may be more effective and efficient than current USPSTF recommendations. Comparison of modeled outcomes from risk-based CT lung-screening strategies vs USPSTF recommendations. Empirical risk models for lung cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age; education; sex; race; smoking intensity, duration, and quit-years; body mass index; family history of lung cancer; and self-reported emphysema. Model validation in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the United States. Models were applied to US ever-smokers aged 50 to 80 years (NHIS 2010-2012) to estimate outcomes of risk-based selection for CT lung screening, assuming screening for all ever-smokers, yield the percent changes in lung cancer detection and death observed in the NLST. Annual CT lung screening for 3 years beginning at age 50 years. For model validity: calibration (number of model-predicted cases divided by number of observed cases [estimated/observed]) and discrimination (area under curve [AUC]). For modeled screening outcomes: estimated number of screen-avertable lung cancer deaths and estimated screening effectiveness (number needed to screen [NNS] to prevent 1 lung cancer death). Lung cancer incidence and death risk models were well calibrated in PLCO and NLST. The lung cancer death model calibrated and discriminated well for US ever-smokers aged 50 to 80 years (NHIS 1997-2001: estimated/observed = 0.94 [95%CI, 0.84-1.05]; AUC, 0.78 [95%CI, 0.76-0.80]). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung cancer screening and 46,488 (95% CI, 43,924-49,053) lung cancer deaths were estimated as screen-avertable over 5 years (estimated NNS, 194 [95% CI, 187-201]). In contrast, risk-based selection screening of the same number of ever-smokers (9.0 million) at highest 5-year lung cancer risk (≥1.9%) was estimated to avert 20% more deaths (55,717 [95% CI, 53,033-58,400]) and was estimated to reduce the estimated NNS by 17% (NNS, 162 [95% CI, 157-166]). Among a cohort of US ever-smokers aged 50 to 80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung cancer deaths prevented over 5 years, along with a lower NNS to prevent 1 lung cancer death.

Low-dose Computed Tomography Screening for Lung Cancer in a Clinical Setting

The purpose of this article is to review clinical computed tomography (CT) lung screening program elements essential to safely and effectively manage the millions of Americans at high risk for lung cancer expected to enroll in lung cancer screening programs over the next 3 to 5 years. To optimize the potential net benefit of CT lung screening and facilitate medical audits benchmarked to national quality standards, radiologists should interpret these examinations using a validated structured reporting system such as Lung-RADS. Patient and physician educational outreach should be enacted to support an informed and shared decision-making process without creating barriers to screening access. Programs must integrate smoking cessation interventions to maximize the clinical efficacy and cost-effectiveness of screening. At an institutional level, budgets should account for the necessary expense of hiring and/or training qualified support staff and equipping them with information technology resources adequate to enroll and track patients accurately over decades of future screening evaluation. At a national level, planning should begin on ways to accommodate the upcoming increased demand for physician services in fields critical to the success of CT lung screening such as diagnostic radiology and thoracic surgery. Institutions with programs that follow these specifications will be well equipped to meet the significant oncoming demand for CT lung screening services and bestow clinical benefits on their patients equal to or beyond what was observed in the National Lung Screening Trial.

Establishing a Computed Tomography Screening Clinic

Since the publication of the National Lung Screening Trial results, there has been increasing interest among radiologists to establish computed tomography (CT) screening centers. During the past 10 years, we have operated a CT screening center in suburban Boston, MA. The purpose of this paper is to describe our process for performing self-referred CT lung screening exams, including our organizational structure, marketing, patient selection process, and standardized workflow. Direct communication between the patient and radiologist, both before and after the CT scan, in our opinion, is critical to a successful screening program. In many ways, CT screening offers radiologists a wonderful opportunity to interact directly with patients. Showing abnormal lung findings to patients on their CT scans may help create a unique "teachable" moment for current smokers and may help motivate patients to quit smoking.