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Purpose: The Lung CT Screening Reporting and Data System (Lung-RADS™) is an algorithm that can be used to classify lung nodules in patients with significant smoking histories. It is published in table format but can be implemented as a computer program. The aim of this study was to assess the efficiency and accuracy of the use of a computer program versus the table in categorizing lung nodules.Methods: The Lung-RADS algorithm was implemented as a computer program. Through the use of a survey tool, respondents were asked to categorize 13 simulated lung nodules using the computer program and the Lung-RADS table as published. Data were gathered regarding time to completion, accuracy of each nodule's categorization, users' subjective categorization confidence, and users' perceived efficiency using each method.Results: The use of a computer program to categorize lung nodules resulted in significantly increased interpretation speed (80.8 ± 37.7 vs 156 ± 105 seconds, P < .0001), lung nodule classification accuracy (99.6% vs 76.5%, P < .0001), and perceived confidence and efficiency compared with the use of the table. There were no significant differences in accuracy when comparing thoracic radiologists with the remainder of the group.Conclusions: Radiologists were both more efficient and more accurate in lung nodule categorization when using computerized decision support tools. The authors propose that other institutions use computerized implementations of Lung-RADS in the interests of both efficiency and patient outcomes through proper management. Furthermore, they suggest the ACR design future iterations of the Lung-RADS algorithm with computerized decision support in mind. [ABSTRACT FROM AUTHOR]

Purpose The aim of this study was to assess the effect of applying ACR Lung-RADS in a clinical CT lung screening program on the frequency of positive and false-negative findings. Methods Consecutive, clinical CT lung screening examinations performed from January 2012 through May 2014 were retroactively reclassified using the new ACR Lung-RADS structured reporting system. All examinations had initially been interpreted by radiologists credentialed in structured CT lung screening reporting following the National Comprehensive Cancer Network’s Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012), which incorporated positive thresholds modeled after those in the National Lung Screening Trial. The positive rate, number of false-negative findings, and positive predictive value were recalculated using the ACR Lung-RADS-specific positive solid/part-solid nodule diameter threshold of 6 mm and nonsolid (ground-glass) threshold of 2 cm. False negatives were defined as cases reclassified as benign under ACR Lung-RADS that were diagnosed with malignancies within 12 months of the baseline examination. Results A total of 2,180 high-risk patients underwent baseline CT lung screening during the study interval; no clinical follow-up was available in 577 patients (26%). ACR Lung-RADS reduced the overall positive rate from 27.6% to 10.6%. No false negatives were present in the 152 patients with >12-month follow-up reclassified as benign. Applying ACR Lung-RADS increased the positive predictive value for diagnosed malignancy in 1,603 patients with follow-up from 6.9% to 17.3%. Conclusions The application of ACR Lung-RADS increased the positive predictive value in our CT lung screening cohort by a factor of 2.5, to 17.3%, without increasing the number of examinations with false-negative results. [ABSTRACT FROM AUTHOR]

Purpose The aim of this study was to compare results of National Comprehensive Cancer Network (NCCN) high-risk group 2 with those of NCCN high-risk group 1 in a clinical CT lung screening program. Methods The results of consecutive clinical CT lung screening examinations performed from January 2012 through December 2013 were retrospectively reviewed. All examinations were interpreted by radiologists credentialed in structured CT lung screening reporting, following the NCCN Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012). Positive results required a solid nodule ≥4 mm, a ground-glass nodule ≥5 mm, or a mediastinal or hilar lymph node >1 cm, not stable for >2 years. Significant incidental findings and findings suspicious for pulmonary infection were also recorded. Results A total of 1,760 examinations were performed (464 in group 2, 1,296 in group 1); no clinical follow-up was available in 432 patients (28%). Positive results, clinically significant incidental findings, and suspected pulmonary infection were present in 25%, 6%, and 6% in group 2 and 28.2%, 6.2%, and 6.6% in group 1, respectively. Twenty-three cases of lung cancer were diagnosed (6 in group 2, 17 in group 1), for annualized rates of malignancy of 1.8% in group 2 and 1.6% in group 1. Conclusion NCCN group 2 results were substantively similar to those for group 1 and closely resemble those reported in the National Lung Screening Trial. Similar rates of positivity and lung cancer diagnosis in both groups suggest that thousands of additional lives may be saved each year if screening eligibility is expanded to include this particular high-risk group. [ABSTRACT FROM AUTHOR]

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.

BACKGROUND. Lung-RADS recommends 3-month follow-up for category 4A nodules and downgrading to category 2 of all category 3 or 4 nodules that are unchanged for 3 months or longer, indicating benign behavior. This guidance may be problematic considering the potential for slow-growing cancers in that lack of nodule growth, particularly at short follow-up intervals, may provide false reassurance. OBJECTIVE. The purpose of this study was to evaluate the yield of short-term follow-up CT in showing growth among malignant nodules detected on lung cancer screening CT. METHODS. This retrospective study included 76 patients (53 women, 23 men; median age, 68 years) with a positive lung cancer screening CT result (Lung-RADS category ≥ 3) between June 2015 and May 2021 with a subsequent lung cancer diagnosis and at least one follow-up CT examination at least 3 months before diagnostic or therapeutic intervention. Semiautomated software was used for linear and volumetric nodule measurements. Diameter was defined as the mean of short- and long-axis measurements. For solid nodules, growth was defined as an at least 1.5-mm increase in mean diameter or an at least 25% increase in volume; part-solid nodules, an at least 1.5-mm increase in solid-component mean diameter or an at least 25% increase in volume; and ground-glass nodules, an at least 3-mm increase in mean diameter or development of a new solid component within the nodule. RESULTS. Median time to growth was 13 months by linear and 11 months by volumetric measurement. Frequency of growth at 3 months was 5% by linear and 7% by volumetric measurement. By linear measurement, median time to growth and frequency of growth at 3 months were 13 months and 7% (solid nodules), 18 months and 6% (part-solid nodules), not reached and 0% (ground-glass nodules), not reached and 0% (category 3 nodules), 13 months and 6% (category 4A nodule)s, 6 months and 11% (category 4B nodules), and 12 months and 10% (category 4X nodules). CONCLUSION. Malignant nodules manifest growth slowly on follow-up CT, and 3-month follow-up CT has very low yield. Stability at 3-month follow-up should not instill high confidence in benignancy, and downgrading all such nodules to Lung-RADS category 2 may be problematic. CLINICAL IMPACT. This study highlights the possibility of slow-growing malignancy and associated challenges in application of Lung-RADS to management of unchanged nodules on follow-up imaging.

Background: Lung cancer screening trials generally enroll motivated, relatively healthy, and adherent populations. We therefore evaluated the prevalence and effects of comorbidities in a real-world population undergoing low-dose computed tomography (LDCT) scans., Patients and Methods: We calculated the Charlson Comorbidity Index (CCI) of patients for whom an initial low-dose computed tomography (LDCT) for lung cancer screening was ordered between February 2017 and February 2019 in an integrated safety-net healthcare system. We examined the association between CCI and initial LDCT completion using multivariable logistic regression, assessed the association between specific medical comorbidity and LDCT completion using Chi-square test or Fisher's exact test as appropriate, and examined the association between CCI and LDCT Lung-RADS results using Fisher's exact test., Results: A total of 1358 patients were included in the analysis. Mean age was 63 years, 57% were women, and 50% were Black. Patients had moderate comorbidity burden (median CCI 3) with chronic pulmonary disease the most common comorbidity. Overall, 943 LDCT (70%) were completed. There was no difference in 30-day, 90-day, or 1-year completion rates of initial LDCT according to CCI. However, 30-day LDCT completion rates did increase over time (P < .001). Lung-RADS scores were not associated with CCI., Conclusion: In a real-world setting, patients undergoing lung cancer screening have moderate comorbidity burden. The degree and type of medical comorbidity are not associated with initial screening completion or results. Timeliness of LDCT completion may improve as program experience increases., (Copyright © 2022. Published by Elsevier Inc.)

BACKGROUND. Lung-RADS category 3 and 4 nodules account for most screening-detected lung cancers and are considered actionable nodules with management implications. The cancer frequency among such nodules is estimated in the Lung-RADS recommendations and has been investigated primarily by means of retrospectively assigned Lung-RADS classifications. OBJECTIVE. The purpose of this study was to assess the frequency of cancer among lung nodules assigned Lung-RADS category 3 or 4 at lung cancer screening (LCS) in clinical practice and to evaluate factors that affect the cancer frequency within each category. METHODS. This retrospective study was based on review of clinical radiology reports of 9148 consecutive low-dose CT LCS examinations performed for 4798 patients between June 2014 and January 2021 as part of an established LCS program. Unique nodules assigned Lung-RADS category 3 or 4 (4A, 4B, or 4X) that were clinically categorized as benign or malignant in a multidisciplinary conference that considered histologic analysis and follow-up imaging were selected for further analysis. Benign diagnoses based on stability required at least 12 months of follow-up imaging. Indeterminate nodules were excluded. Cancer frequencies were evaluated. RESULTS. Of the 9148 LCS examinations, 857 (9.4%) were assigned Lung-RADS category 3, and 721 (7.9%) were assigned category 4. The final analysis included 1297 unique nodules in 1139 patients (598 men, 541 women; mean age, 66.0 ± 6.3 years). A total of 1108 of 1297 (85.4%) nodules were deemed benign, and 189 of 1297 (14.6%) were deemed malignant. The frequencies of malignancy of category 3, 4A, 4B, and 4X nodules were 3.9%, 15.5%, 36.3%, and 76.8%. A total of 45 of 46 (97.8%) endobronchial nodules (all category 4A) were deemed benign on the basis of resolution. Cancer frequency was 13.1% for solid, 24.4% for part-solid, and 13.5% for ground-glass nodules. CONCLUSION. In the application of Lung-RADS to LCS clinical practice, the frequency of Lung-RADS category 3 and 4 nodules and the cancer frequency in these categories were higher than the prevalence and cancer risk estimated for category 3 and 4 nodules in the Lung-RADS recommendations and those reported in earlier studies in which category assignments were retrospective. Nearly all endobronchial category 4A nodules were benign. CLINICAL IMPACT. Future Lung-RADS iterations should consider the findings of this study from real-world practice to improve the clinical utility of the system.

BACKGROUND. Nodules may have different lung cancer risks when new on follow-up CT versus when present on previously performed CT (i.e., existing nodules). Diameter-based Lung-RADS and volume-based NELSON (Nederlands-Leuvens Longkanker Screenings ONderzoek trial) categories have shown variable performance in nodule risk assessment. OBJECTIVE. The purpose of this study was to assess Lung-RADS and NELSON classifications of nodules detected on follow-up lung cancer screening CT examinations. METHODS. This retrospective study included 185 patients (100 women and 85 men; median age, 66 years) who underwent a lung cancer screening CT examination for which a prior CT examination was available. Stratified random sampling was performed to enrich the sample with suspicious nodules, yielding 50, 45, 47, 30, and 13 nodules with Lung-RADS categories 2, 3, 4A, 4B, and 4X, respectively. Lung-RADS categories were recorded from clinical reports. The linear measurements of the nodules were extracted from clinical reports to generate Lung-RADS categories by use of strict criteria from Lung-RADS version 1.1. Two radiologists used a semiautomated tool to obtain nodule volumes, which were used to generate NELSON categories. Lung cancer risk was assessed. ROC analysis was performed. Percentages and AUCs were weighted on the basis of Lung-RADS category frequencies in the underlying screening cohort. RESULTS. Twenty-nine cancers were diagnosed. The weighted cancer risk was 5% for new nodules, 1% for stable existing nodules, and 44% for growing existing nodules. None of the clinical Lung-RADS category 2 nodules were cancer. With use of strict Lung-RADS version 1.1 criteria, 34 nodules, including seven cancers, were downgraded to category 2. The AUC for cancer was 0.96 for clinical Lung-RADS, 0.81 for strict Lung-RADS, 0.71-0.84 for the NELSON algorithm (two readers), and 0.89 for nodule diameter measurement. Clinical Lung-RADS achieved weighted sensitivity and specificity, respectively, of 100% and 85% for the entire sample, 100% and 41% for new nodules, and 100% and 94% for existing nodules. The optimal diameter threshold was 8 mm for existing nodules versus 6 mm for new nodules. CONCLUSION. Lung-RADS, as applied by radiologists in clinical practice, achieved excellent performance on follow-up screening examinations. Strict Lung-RADS resulted in the downgrading of some cancers to category 2. Volumetric assessments had weaker performance than clinical Lung-RADS. New nodules warrant smaller size thresholds than existing nodules. CLINICAL IMPACT. The findings of the present study provide insight into radiologists' management of nodules detected on follow-up screening examinations.

Simple Summary: The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging. The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv. [ABSTRACT FROM AUTHOR]

Introduction: To determine the clinical significance of category 3 (CAT3) abnormalities and the necessity of a 6-month follow-up computed tomography (CT). We also explored features associated with increased lung cancer risk., Methods: From the National Lung Screening Trial database, we identified participants with CAT3 lesions at prevalence screen. Rates of lung cancer and lung cancer-specific deaths (LSDs) were compared between those who underwent first follow-up CT before 6 months (early diagnostic group) and those who underwent annual screening (annual diagnostic group). We estimated the change in LSD if the 6-month CT was eliminated. Regression analysis was performed to identify features associated with participants with CAT3 who developed lung cancer., Results: A total of 1763 CAT3s were identified (6.6% of all participants who had low-dose CT), with 108 lung cancers (6.1%) and 41 LSDs (2.3%) in a 7-year period. Rates of lung cancer (7.5% versus 3.1%) and LSD (4.0% versus 1.0%) were higher in the early diagnostic group than in the annual diagnostic group. We estimated an increase in LSD of 0.6% of all participants with CAT3 (24.4% of all LSDs) if the 6-month CT was not performed. Multivariate regression analysis found that increased age, emphysema, and a part-solid nodule greater than 5 mm were associated with participants with CAT3 who developed lung cancer., Conclusions: CAT3 lesions are uncommon, and eliminating the 6-month CT would potentially increase LSD by 0.6% of all patients with CAT3. Age, emphysema, and part-solid nodule greater than 5 mm may be useful in risk prediction models to determine which participants with CAT3 are more likely to develop lung cancer and suggest which patients may need more intense follow-up., (Copyright © 2021 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Background: Recently, lung cancer screenings based on age and smoking history using low-dose computed tomography (LDCT) have begun in Korea. This study aimed to evaluate the distribution of lung imaging reporting and data system (Lung-RADS) categories in shipyard workers exposed to lung carcinogens such as nickel, chromium, and welding fumes according to job type, to provide basic data regarding indications for LDCT in shipyard workers., Methods: This study included 6,326 workers from a single shipyard, who underwent health examinations with LDCT between January 2010 and December 2018. Data on age, smoking status and history, medical history, and job type were investigated. The participants were categorized into high-exposure, low-exposure, and non-exposure job groups based on the estimated exposure level of nickel, chromium, and welding fumes according to job type. Cox proportional hazard regression analysis was used to determine the difference between exposure groups in Lung-RADS category ≥ 3 (3, 4A, and 4B)., Results: Out of all participants, 97 (1.5%) participants were classified into Lung-RADS category ≥ 3 and 7 (0.1%) participants were confirmed as lung cancer. The positive predictive value (ratio of diagnosed lung cancer cases to Lung-RADS category ≥ 3) was 7.2%. The hazard ratio (HR) of Lung-RADS category ≥ 3 was 1.451 (95% confidence interval [CI]: 0.911-2.309) in low-exposure and 1.692 (95% CI: 1.007-2.843) in high-exposure job group. Adjusting for age and pack-years, the HR was statistically significant only in the high-exposure job group (HR: 1.689; 95% CI: 1.004-2.841)., Conclusions: Based on LDCT and Lung-RADS, among male shipyard workers, Lung-RADS category ≥ 3 were significantly higher in the high-exposure job group. Their HR tended to be > 1.0 and was statistically significant in the high-exposure job group. Additional studies should be conducted to establish more elaborate LDCT indications for occupational health examination., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2021 Korean Society of Occupational & Environmental Medicine.)

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Background: Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography (CT) or appearance of new nodule to determine the timing of the next CT. We aimed at developing a more accurate screening protocol by estimating the 3-year lung cancer risk after two screening CTs using deep machine learning (ML) of radiologist CT reading and other universally available clinical information., Methods: A deep machine learning (ML) algorithm was developed from 25,097 participants who had received at least two CT screenings up to two years apart in the National Lung Screening Trial. Double-blinded validation was performed using 2,294 participants from the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). Performance of ML score to inform lung cancer incidence was compared with Lung-RADS and volume doubling time using time-dependent ROC analysis. Exploratory analysis was performed to identify individuals with aggressive cancers and higher mortality rates., Findings: In the PanCan validation cohort, ML showed excellent discrimination with a 1-, 2- and 3-year time-dependent AUC values for cancer diagnosis of 0·968±0·013, 0·946±0·013 and 0·899±0·017. Although high ML score cohort included only 10% of the PanCan sample, it identified 94%, 85%, and 71% of incident and interval lung cancers diagnosed within 1, 2, and 3 years, respectively, after the second screening CT. Furthermore, individuals with high ML score had significantly higher mortality rates (HR=16·07, p<0·001) compared to those with lower risk., Interpretation: ML tool that recognizes patterns in both temporal and spatial changes as well as synergy among changes in nodule and non-nodule features may be used to accurately guide clinical management after the next scheduled repeat screening CT.

The National Lung Cancer Screening Trial (NLST) demonstrated the use of low dose helical computed tomography (LDCT) scans for lung cancer screening. However, the NLST was implemented in urban hospitals and prior to the Lung CT Screening Reporting and Data System (Lung-RADS). In this retrospective cohort study, 774 eligible patients received LDCT screening using Lung-RADS criteria. Eighty-four patients (10.9%) had subsequent testing performed compared to 24.2% in the NLST study. Of those with subsequent testing, 21.4% were diagnosed with lung cancer compared to only 4.6% in the NLST study. Lung-RADS significantly reduced unnecessary testing while identifying higher rates of lung cancer compared to the NLST., Competing Interests: The authors report no conflicts of interest or any financial disclosures., (© The Association for Clinical and Translational Science 2019.)

In the optimism surrounding screening for lung cancer, the downsides of screening should not be ignored. The article examines the dark side of screening, which is not an unmitigated good. [ABSTRACT FROM AUTHOR]

Objective: To report the radiological results of a pilot study for the Korean Lung Cancer Screening project conducted to evaluate the feasibility of lung cancer screening using low-dose chest computed tomography (LDCT) in Korea., Materials and Methods: The National Cancer Center and three regional cancer centers participated in this study. Asymptomatic current or ex-smokers aged 55-74 years with a smoking history of at least 30 pack-years who had used tobacco within the last 15 years were considered eligible. In total, 256 participants underwent LDCT November 2016 through March 2017. The American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS) was used to categorize the LDCT findings., Results: In total, 57%, 35.5%, 3.9%, and 3.5% participants belonged to Lung-RADS categories 1, 2, 3, and 4, respectively. Accordingly, 7.4% participants exhibited positive findings (category 3 or 4). Lung cancer was diagnosed in one participant (stage IA, small cell lung cancer). Other LDCT findings included pulmonary emphysema (32.8%), coronary artery calcification (30.9%), old pulmonary tuberculosis (11.7%), bronchiectasis (12.9%), interstitial lung disease with a usual interstitial pneumonia pattern (1.2%), and pleural effusion (0.8%)., Conclusion: Even though the size of our study population was small, the positive rate of 7.4% was like or lower than those in other lung cancer screening studies. Early lung cancer was detected using LDCT screening in one participant. Lung-RADS may be applicable to participants in Korea, where pulmonary tuberculosis is endemic.

The number of screening-detected lung nodules is expected to increase as low-dose computed tomography screening is implemented nationally. Standardized guidelines for image acquisition, interpretation, and screen-detected nodule workup are essential to ensure a high standard of medical care and that lung cancer screening is implemented safely and cost effectively. In this article, we review the current guidelines for pulmonary nodule management in the lung cancer screening setting., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Rationale: Lung computed tomography (CT) Screening Reporting and Data System (lung-RADS) has standardized follow-up and management decisions in lung cancer screening. To date, little is known how lung-RADS classification compares with radiological semantic features in risk prediction and diagnostic discrimination., Objectives: To compare the performance of radiological semantic features and lung-RADS in predicting nodule malignancy in lung cancer screening., Methods: We used data and low-dose CT (LDCT) images from the National Lung Screening Trial (NLST). The training cohort contained 60 patients with screen-detected incident lung cancers who had a positive baseline screen (T0) that was not diagnosed and then was diagnosed at second follow-up (T2), and 139 nodule-positive controls who had 3 consecutive positive screens (T0 to T2) that were not diagnosed as lung cancer. The testing cohort included 40 patients with incident lung cancers that were diagnosed at first follow-up (T1) and 40 nodule-positive controls. Twenty-four semantic features were scored on a point scale from the LDCT images. Multivariable linear predictor model was built on the semantic features and the performances were compared with lung-RADS in 3 screening rounds. We also combined non-size-based semantic features with lung-RADS to improve malignancy detection., Results: At T0, the average area under the receiver operating characteristic curve (AUROC) for border definition in risk prediction was 0.72. The average AUROC for contour at T1 in risk prediction and T2 in diagnostic discrimination was 0.82 and 0.88, respectively. By comparison, the average AUROC of lung-RADS at T0, T1 and T2 were 0.60, 0.76 and 0.87, respectively. The combined model of the semantic features and lung-RADS shows improvement with AUROCs of 0.74, 0.88 and 0.96 at T0, T1, and T2, respectively, achieved by adding border definition (at T0) or contour (at T1 and T2)., Conclusion: We find semantic features defined by border definition and contour performed similar to lung-RADS at follow-up time point and outperformed lung-RADS at baseline. These semantics alongside of lung-RADS shows improved performance to detect malignancy., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Purpose: The aim of this study was to compare results of National Comprehensive Cancer Network (NCCN) high-risk group 2 with those of NCCN high-risk group 1 in a clinical CT lung screening program.Methods: The results of consecutive clinical CT lung screening examinations performed from January 2012 through December 2013 were retrospectively reviewed. All examinations were interpreted by radiologists credentialed in structured CT lung screening reporting, following the NCCN Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012). Positive results required a solid nodule ≥4 mm, a ground-glass nodule ≥5 mm, or a mediastinal or hilar lymph node >1 cm, not stable for >2 years. Significant incidental findings and findings suspicious for pulmonary infection were also recorded.Results: A total of 1,760 examinations were performed (464 in group 2, 1,296 in group 1); no clinical follow-up was available in 432 patients (28%). Positive results, clinically significant incidental findings, and suspected pulmonary infection were present in 25%, 6%, and 6% in group 2 and 28.2%, 6.2%, and 6.6% in group 1, respectively. Twenty-three cases of lung cancer were diagnosed (6 in group 2, 17 in group 1), for annualized rates of malignancy of 1.8% in group 2 and 1.6% in group 1.Conclusion: NCCN group 2 results were substantively similar to those for group 1 and closely resemble those reported in the National Lung Screening Trial. Similar rates of positivity and lung cancer diagnosis in both groups suggest that thousands of additional lives may be saved each year if screening eligibility is expanded to include this particular high-risk group. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to assess the effect of applying ACR Lung-RADS in a clinical CT lung screening program on the frequency of positive and false-negative findings.Methods: Consecutive, clinical CT lung screening examinations performed from January 2012 through May 2014 were retroactively reclassified using the new ACR Lung-RADS structured reporting system. All examinations had initially been interpreted by radiologists credentialed in structured CT lung screening reporting following the National Comprehensive Cancer Network's Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012), which incorporated positive thresholds modeled after those in the National Lung Screening Trial. The positive rate, number of false-negative findings, and positive predictive value were recalculated using the ACR Lung-RADS-specific positive solid/part-solid nodule diameter threshold of 6 mm and nonsolid (ground-glass) threshold of 2 cm. False negatives were defined as cases reclassified as benign under ACR Lung-RADS that were diagnosed with malignancies within 12 months of the baseline examination.Results: A total of 2,180 high-risk patients underwent baseline CT lung screening during the study interval; no clinical follow-up was available in 577 patients (26%). ACR Lung-RADS reduced the overall positive rate from 27.6% to 10.6%. No false negatives were present in the 152 patients with >12-month follow-up reclassified as benign. Applying ACR Lung-RADS increased the positive predictive value for diagnosed malignancy in 1,603 patients with follow-up from 6.9% to 17.3%.Conclusions: The application of ACR Lung-RADS increased the positive predictive value in our CT lung screening cohort by a factor of 2.5, to 17.3%, without increasing the number of examinations with false-negative results. [ABSTRACT FROM AUTHOR]

This article explains the rationale of lung cancer screening with low-dose computed tomography and provides a practical approach to all relevant aspects of a lung cancer screening program. Imaging protocols, patient eligibility criteria, facility readiness, and reimbursement criteria are addressed step by step. Diagnostic criteria and Lung-RADS (Lung Computed Tomography Screening Reporting and Data System) nodule management pathways are illustrated with examples. Pearls and pitfalls for interpretation of lung cancer screening low-dose chest computed tomography are discussed., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: The National Lung Screening Trial showed a reduction in overall and cancer-specific mortality for patients screened with low-dose computed tomography (LDCT) versus chest radiograph. Some question whether this can be achieved in community healthcare settings. Our aim was to analyze lung cancer screening outcomes and administered radiation dose using LDCT scans at a community hospital., Patients and Methods: We retrospectively reviewed the records of 680 patients who underwent LDCT between June 2014 and December 2015, and who met Centers for Medicare and Medicaid Services lung cancer screening criteria: asymptomatic, aged 55 to 77 years, smoked within the last 15 years, and ≥ 30 pack-year history. Effective and absorbed doses were calculated and correlated with gender and body mass index., Results: Among the 133 patients (19.6%) with a positive screening result (Lung Imaging Reporting and Data System score of 3 or 4), 18 lung cancers were identified in 16 patients, 56.3% (9 of 16) of which were stage I non-small-cell lung cancer. The false-positive rate was 82.8% (95% confidence interval, 73.6%-89.8%). Mean estimated effective dose using dose length product and size-specific dose estimate using water equivalent diameter were 1.2 mSv and 3.7 mGy for women and 1.4 mSv and 3.9 mGy for men, respectively. All dosing metrics were strongly correlated with body mass index (P < .0001)., Conclusions: Over half of screening patients diagnosed with non-small-cell lung cancer in our community had stage I disease, which we anticipate translating into significantly improved mortality. Patient radiation dose from LDCT scans is approximately one-fifth that from standard CT chest examinations., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: To evaluate possible variability in chest radiologists' interpretations of the Lung Imaging Reporting and Data System (Lung-RADS) on difficult-to-classify scenarios., Materials and Methods: Ten scenarios of difficult-to-classify imaginary lung nodules were prepared as an online survey that targeted Korean Society of Thoracic Radiology members. In each question, a description was provided of the size, consistency, and interval change (new or growing) of a lung nodule observed using annual repeat computed tomography, and the respondent was instructed to choose one answer from five choices: category 2, 3, 4A, or 4B, or "un-categorizable." Consensus answers were established by members of the Korean Imaging Study Group for Lung Cancer., Results: Of the 420 answers from 42 respondents (excluding multiple submissions), 310 (73.8%) agreed with the consensus answers; eleven (26.2%) respondents agreed with the consensus answers to six or fewer questions. Assigning the imaginary nodules to categories higher than the consensus answer was more frequent (16.0%) than assigning them to lower categories (5.5%), and the agreement rate was below 50% for two scenarios., Conclusion: When given difficult-to-classify scenarios, chest radiologists showed large variability in their interpretations of the Lung-RADS categories, with high frequencies of disagreement in some specific scenarios.

Background: The National Lung Screening Trial (NLST) showed low-dose screening chest computed tomography (CT) reduced the lung cancer mortality rate up to 20% in high-risk patients in the United States. We aimed to investigate the impact of applying the NLST eligibility criteria to the population in Taiwan, and to identify additional risk factors to select subjects at risk for lung cancer., Patients and Methods: We retrospectively reviewed the medical records of 1763 asymptomatic healthy subjects (age range, 40-80 years) who voluntarily underwent low-dose chest CT (1029 male, 734 female) from August 2013 to August 2014. Clinical information and nodule characteristics were recorded. The results of subsequent follow-up and outcome were also recorded., Results: A total of 8.4% (148/1763) of subjects would have been eligible for lung cancer screening based on the NLST criteria. However, only 1 of these eligible subjects would have a lung cancer detected at baseline. Among the 1615 subjects who did not meet the NLST criteria, the detection rates of lung cancer were 2.6% in women and 0.56% in men. Logistic regression showed that female gender and a family history of lung cancer were the 2 most important predictors of lung cancer in Taiwan (odds ratio, 6.367; P = .003; odds ratio, 3.017; P = .016, respectively)., Conclusions: In conclusion, NLST eligibility criteria may not be effective in screening for lung cancer in Taiwan. A risk-based prediction model based on the family history of lung cancer and female gender can potentially improve the efficiency of lung cancer screening programs in Taiwan., (Copyright © 2016 Elsevier Inc. All rights reserved.)