ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

Radiologic Features of Nodules Attached to the Mediastinal or Diaphragmatic Pleura at Low-Dose CT for Lung Cancer Screening

Background Pulmonary noncalcified nodules (NCNs) attached to the fissural or costal pleura with smooth margins and triangular or lentiform, oval, or semicircular (LOS) shapes at low-dose CT are recommended for annual follow-up instead of immediate workup. Purpose To determine whether management of mediastinal or diaphragmatic pleura-attached NCNs (M/DP-NCNs) with the same features as fissural or costal pleura-attached NCNs at low-dose CT can follow the same recommendations. Materials and Methods This retrospective study reviewed chest CT examinations in participants from two databases. Group A included 1451 participants who had lung cancer that was first present as a solid nodule with an average diameter of 3.0-30.0 mm. Group B included 345 consecutive participants from a lung cancer screening program who had at least one solid nodule with a diameter of 3.0-30.0 mm at baseline CT and underwent at least three follow-up CT examinations. Radiologists reviewed CT images to identify solid M/DP-NCNs, defined as nodules 0 mm in distance from the mediastinal or diaphragmatic pleura, and recorded average diameter, margin, and shape. General descriptive statistics were used. Results Among the 1451 participants with lung cancer in group A, 163 participants (median age, 68 years [IQR, 61.5-75.0 years]; 92 male participants) had 164 malignant M/DP-NCNs 3.0-30.0 mm in average diameter. None of the 164 malignant M/DP-NCNs had smooth margins and triangular or LOS shapes (upper limit of 95% CI of proportion, 0.02). Among the 345 consecutive screening participants in group B, 146 participants (median age, 65 years [IQR, 59-71 years]; 81 female participants) had 240 M/DP-NCNs with average diameter 3.0-30.0 mm. None of the M/DP-NCNs with smooth margins and triangular or LOS shapes were malignant after a median follow-up of 57.8 months (IQR, 46.3-68.1 months). Conclusion For solid M/DP-NCNs with smooth margins and triangular or LOS shapes at low-dose CT, the risk of lung cancer is extremely low, which supports the recommendation of Lung Imaging Reporting and Data System version 2022 for annual follow-up instead of immediate workup. © RSNA, 2024 See also the editorial by Goodman and Baruah in this issue.

Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region

OBJECTIVES

To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.

METHODS

This single-centre retrospective study screened 21,150 consecutive body CT studies from September 2018 to February 2019. Pulmonary nodules detected by the DLS on axial CT images but not mentioned in initial radiology reports were flagged. Flagged images were scored by four board-certificated radiologists each with at least 5 years of experience. Nodules with scores of 2 (understandable miss) or 3 (should not be missed) were then categorised as unlikely to be clinically significant (2a or 3a) or likely to be clinically significant (2b or 3b) according to the 2017 Fleischner guidelines for pulmonary nodules. The miss rate was defined as the total number of studies receiving scores of 2 or 3 divided by total screened studies.

RESULTS

Among 172 nodules flagged by the DLS, 60 (35%) missed nodules were confirmed by the radiologists. The nodules were further categorised as 2a, 2b, 3a, and 3b in 24, 14, 10, and 12 studies, respectively, with an overall positive predictive value of 35%. Missed pulmonary nodules were identified in 0.3% of all CT images, and one-third of these lesions were considered clinically significant.

CONCLUSIONS

Use of DLS-assisted automated detection as a second reader can identify missed pulmonary nodules, some of which may be clinically significant.

CLINICAL RELEVANCE/APPLICATION

Use of DLS to help radiologists detect pulmonary lesions may improve patient care.

KEY POINTS

• DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort. • Performance of combined radiologists and DLS was better than DLS or radiologists alone. • Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.

Characterization of Newly Detected Costal Pleura-attached Noncalcified Nodules at Annual Low-Dose CT Screenings

Background Solid costal pleura-attached noncalcified nodules (CP-NCNs) less than 10.0 mm with lentiform, oval, or semicircular (LOS) or triangular shapes and smooth margins on baseline low-dose CT scans from the Mount Sinai Early Lung and Cardiac Action Program (MS-ELCAP) were reviewed, and it was determined that they can be followed up at the first annual screening rather than having a shorter-term work-up. Purpose To determine whether the same criteria could be used for solid CP-NCNs newly identified at annual screening examinations. Materials and Methods With use of the same MS-ELCAP database, all new solid CP-NCNs measuring 30.0 mm or less were identified at 4425 annual screening examinations between 2010 and 2019. In addition, to ensure that no malignant CP-NCNs met the criteria, all solid malignant CP-NCNs of 30.0 mm or less in the International Early Lung Cancer Action Program, or I-ELCAP, database of 111 102 annual screening examinations from the 76 participating institutions between 1992 and 2019 were identified; Mount Sinai is one of these institutions. All identified solid CP-NCNs were reviewed-with the radiologists blinded to diagnosis-for shape (triangular, LOS, polygonal, round, or irregular), margin (smooth or nonsmooth), pleural attachment (broad or narrow), and the presence of emphysema and/or fibrosis within 10.0 mm of each CP-NCN. Intra- and interreader readings were performed, and agreements were determined by using the B-statistic. Results Of the 76 new solid CP-NCNs, 21 were lung cancers. Benign CP-NCNs were smaller than malignant ones (median diameter, 4.2 mm vs 11 mm; P < .001), had a different shape distributions, more frequently had smooth margins (67% vs 14%; P < .001), and less frequently had emphysema (38% vs 81%; P = .003) or fibrosis (3.6% vs 19%; P = .045) within a 10.0 mm radius. All 22 solid CP-NCNs less than 10.0 mm in average diameter with triangular or LOS shapes and smooth margins were benign, and none of the 21 solid malignant CP-NCNs had these characteristics. Intra- and interobserver agreement for triangular or LOS-shaped CP-NCNs with smooth margins was almost perfect (0.77 and 0.69, respectively). Conclusion The same follow-up recommendation developed for baseline costal pleura-attached noncalcified nodules (CP-NCNs) can be used for CP-NCNs newly identified at annual screening rounds. © RSNA, 2021.

False-Negative Results in Lung Cancer Screening-Evidence and Controversies

Identifying false-negative cases is an important quality metric in lung cancer screening, but it has been infrequently and variably reported in previous studies. Although as a proportion of all screening participants, false-negative cases are uncommon, such cases may constitute a substantial proportion of all lung cancers diagnosed (up to 15%) within a screening program. This article reviews the impact and causes of false-negative lung cancer screening tests, including those related to radiologic evaluation, nodule management protocols, and management decisions made by multidisciplinary teams. Following a review of data from international screening studies, this article discusses the controversies within the screening literature surrounding the definition and classification of a false-negative lung cancer screening test and how data on false-negative rates should be captured and recorded. Challenges, such as avoiding overly cautious surveillance of lung nodules while minimizing overdiagnosis and investigation of indolent or benign lesions, are considered. Finally, the advantages and disadvantages of different approaches to dealing with false-negative results in lung cancer screening are discussed.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

The Added Value of Computer-aided Detection of Small Pulmonary Nodules and Missed Lung Cancers

Lung cancer at its earliest stage is typically manifested on computed tomography as a pulmonary nodule, which could be detected by low-dose multidetector computed tomography technology and the use of thinner collimation. Within the last 2 decades, computer-aided detection (CAD) of pulmonary nodules has been developed to meet the increasing demand for lung cancer screening computed tomography with a larger set of images per scan. This review introduced the basic techniques and then summarized the up-to-date applications of CAD systems in clinical and research programs and in the low-dose lung cancer screening trials, especially in the detection of small pulmonary nodules and missed lung cancers. Many studies have already shown that the CAD systems could increase the sensitivity and reduce the false-positive rate in the diagnosis of pulmonary nodules, especially for the small and isolated nodules. Further improvements to the current CAD schemes are needed to detect nodules accurately, particularly for subsolid nodules.

Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases

PURPOSE

To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies [e.g., computer aided detection (CAD) and segmentation algorithms].

ACQUISITION AND VALIDATION METHODS

This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams. One image series per patient exam was utilized based on coverage of the anatomical region of interest (either the thorax or abdomen). All image series were de-identified. Simulated lesions were derived from a library of anatomically informed digital lesions (93 lung and 50 liver lesions) where six and four digital lesions with nominal diameters ranging from 4 to 20 mm were inserted into lung and liver image series, respectively. Locations for lesion insertion were randomly chosen. A previously validated lesion simulation and virtual insertion technique were utilized. The resulting hybrid images were reviewed by three experienced radiologists to assure similarity with routine clinical imaging in a diverse adult population.

DATA FORMAT AND USAGE NOTES

The database is composed of four datasets that contain 100 patient cases each, for a total of 400 image series accompanied by Matlab.mat tables that provide descriptive information about the virtually inserted lesions (i.e., size, shape, opacity, and insertion location in physical (world) coordinates and voxel indices). All image and metadata are stored in DICOM format on the Quantitative Imaging Data Warehouse (https://qidw.rsna.org/#collection/57d463471cac0a4ec8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login), in two sets: (a) QIBA CT Hybrid Dataset I which contains Lung I and Liver I datasets, and (b) QIBA CT Hybrid Dataset II which contains Lung II and Liver II datasets. The QIDW is supported by the Radiological Society of North America (RSNA). Registration is required upon initial log in.

POTENTIAL APPLICATIONS

By simulating lesion opacity (full solid, part solid and ground glass), size, and texture, the relationship between lesion morphology and segmentation or CAD algorithm performance can be investigated without the need for repetitive patient exams. This database can also serve as a reference standard for device and reader performance studies.

Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers

Purpose

To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans.

Methods

A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004.

Results

Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002).

Conclusions

Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials

A North American trial reported a significant reduction of lung cancer mortality and overall mortality as a result of annual screening using low-dose computed tomography (LDCT). European trials prospectively tested a variety of possible screening strategies. The main topics of current discussion regarding the optimal screening strategy are pre-test selection of the high-risk population, interval length of LDCT rounds, definition of positive finding, and post-test apportioning of lung cancer risk based on LDCT findings. Despite the current lack of statistical evidence regarding mortality reduction, the European independent diverse strategies offer a multi-perspective view on screening complexity, with remarkable indications for improvements in cost-effectiveness and harm-benefit balance. The UKLS trial reported the advantage of a comprehensive and simple risk model for selection of patients with 5% risk of lung cancer in 5 years. Subjective risk prediction by biological sampling is under investigation. The MILD trial reported equal efficiency for biennial and annual screening rounds, with a significant reduction in the total number of LDCT examinations. The NELSON trial introduced volumetric quantification of nodules at baseline and volume-doubling time (VDT) for assessment of progression. Post-test risk refinement based on LDCT findings (qualitative or quantitative) is under investigation. Smoking cessation remains the most appropriate strategy for mortality reduction, and it must therefore remain an integral component of any lung cancer screening programme.

Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13mSv

OBJECTIVES

To determine the value of computer-aided detection (CAD) for solid pulmonary nodules in ultralow radiation dose single-energy computed tomography (CT) of the chest using third-generation dual-source CT at 100kV and fixed tube current at 70 mAs with tin filtration.

METHODS

202 consecutive patients undergoing clinically indicated standard dose chest CT (1.8±0.7 mSv) were prospectively included and scanned with an additional ultralow dose CT (0.13±0.01 mSv) in the same session. Standard of reference (SOR) was established by consensus reading of standard dose CT by two radiologists. CAD was performed in standard dose and ultralow dose CT with two different reconstruction kernels. CAD detection rate of nodules was evaluated including subgroups of different nodule sizes (<5, 5-7, >7mm). Sensitivity was further analysed in multivariable mixed effects logistic regression.

RESULTS

The SOR included 279 solid nodules (mean diameter 4.3±3.4mm, range 1-24mm). There was no significant difference in per-nodule sensitivity of CAD in standard dose with 70% compared to 68% in ultralow dose CT both overall and in different size subgroups (all p>0.05). CAD led to a significant increase of sensitivity for both radiologists reading the ultralow dose CT scans (all p<0.001). In multivariable analysis, the use of CAD (p<0.001), and nodule size (p<0.0001) were independent predictors for nodule detection, but not BMI (p=0.933) and the use of contrast agents (p=0.176).

CONCLUSIONS

Computer-aided detection of solid pulmonary nodules using ultralow dose CT with chest X-ray equivalent radiation dose has similar sensitivities to those from standard dose CT. Adding CAD in ultralow dose CT significantly improves the sensitivity of radiologists.

Risk factors assessment and risk prediction models in lung cancer screening candidates

From February 2015, low-dose computed tomography (LDCT) screening entered the armamentarium of diagnostic tools broadly available to individuals at high-risk of developing lung cancer. While a huge number of pulmonary nodules are identified, only a small fraction turns out to be early lung cancers. The majority of them constitute a variety of benign lesions. Although it entails a burden of the diagnostic work-up, the undisputable benefit emerges from: (I) lung cancer diagnosis at earlier stages (stage shift); (II) additional findings enabling the implementation of a preventive action beyond the realm of thoracic oncology. This review presents how to utilize the risk factors from distinct categories such as epidemiology, radiology and biomarkers to target the fraction of population, which may benefit most from the introduced screening modality.

Spectrum of early lung cancer presentation in low-dose screening CT: a pictorial review

The typical presentation of early stage lung cancers on low-dose CT screening are non-calcified pulmonary nodules. However, there is a wide spectrum of unusual focal abnormalities that can be early presentations of lung cancer. These abnormalities include, for example, cancers associated with 'cystic airspaces' or scar-like cancers. The detection of lung cancer with low-dose CT can be affected by the absence of intravenous contrast medium. As a consequence, endobronchial and central lesions can be difficult to recognize, raising the potential for missed cancers. Focal lesions arising within pre-existing lung disease, such as lung fibrosis or apical scars, can also be early lung cancer manifestations and deserve particular consideration as recognition of these lesions may be hindered by the underlying disease. Furthermore, the unpredictable growth rate of lung cancer, which ranges from indolent to aggressive cancers, necessitates attention to the wide spectrum of progression in lung cancer appearance on serial low-dose CT scans. In this pictorial review we discuss the spectrum of early lung cancer presentation in low-dose CT screening, highlighting typical as well as unusual radiological features and the varied growth rates of early lung cancer. Teaching Points • There is a wide spectrum of early presentations of lung cancer on LDCT. • Low radiation dose and the absence of contrast medium injection can affect lung cancer detection. • Lung cancer growth shows various behaviours, ranging from indolent to aggressive cancers. • Familiarity with LDCT technique can improve CT screening effectiveness and avoid missed diagnosis.

The importance of the regimen of screening in maximizing the benefit and minimizing the harms

BACKGROUND

In CT screening for lung cancer, the regimen of screening is critical in diagnosing lung cancer early while limiting unnecessary tests and invasive procedures. The International Early Lung Cancer Action Program (I-ELCAP) has developed a regimen based on evidence collected in the I-ELCAP cohort of more than 70,000 participants.

METHODS

Important in the development of the regimen is the recognition of the profound difference between the first, baseline round of screening and all subsequent rounds of repeat screening. For each person undergoing screening, the baseline round happens only once while repeat rounds will be performed annually for many years. This difference needs to be clearly recognized as it is these annual rounds which allow for identification of small, early, yet aggressive, lung cancers which have high cure rates despite their aggressiveness. The importance of nodule consistency and size are key factors in the regimen. The regimen needs to be continuously updated by incorporating advances in technology and knowledge.

RESULTS

The use of the I-ELCAP regimen reduces the workup of participants in the screening program to less than 10% in the baseline round and less than 6% in the annual repeat rounds. By use of this regimen, estimated cure rate of lung cancers diagnosed under screening is 80% or higher in both baseline and annual repeat rounds.

CONCLUSIONS

The I-ELCAP collaboration provides a new paradigm that answers the 2002 NCI call for multiple approaches to address relevant questions about screening and the Institute of Medicine (IOM) Roundtable on Evidence-based Medicine from the National Academy of Science's call for a "new clinical research paradigm that takes better advantage of data generated in the course of healthcare delivery would speed and improve the development of evidence for real-world decision making".

Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers

Purpose To update information regarding the usefulness of computer-aided detection (CAD) systems with a focus on the most critical category, that of missed cancers at earlier imaging, for cancers that manifest as a solid nodule. Materials and Methods By using a HIPAA-compliant institutional review board-approved protocol where informed consent was obtained, 50 lung cancers that manifested as a solid nodule on computed tomographic (CT) scans in annual rounds of screening (time 1) were retrospectively identified that could, in retrospect, be identified on the previous CT scans (time 0). Four CAD systems were compared, which were referred to as CAD 1, CAD 2, CAD 3, and CAD 4. The total number of accepted CAD-system-detected nodules at time 0 was determined by consensus of two radiologists and the number of CAD-system-detected nodules that were rejected by the radiologists was also documented. Results At time 0 when all the cancers had been missed, CAD system detection rates for the cancers were 56%, 70%, 68%, and 60% (κ = 0.45) for CAD systems 1, 2, 3, and 4, respectively. At time 1, the rates were 74%, 82%, 82%, and 78% (κ = 0.32), respectively. The average diameter of the 50 cancers at time 0 and time 1 was 4.8 mm and 11.4 mm, respectively. The number of CAD-system-detected nodules that were rejected per CT scan for CAD systems 1-4 at time 0 was 7.4, 1.7, 0.6, and 4.5 respectively. Conclusion CAD systems detected up to 70% of lung cancers that were not detected by the radiologist but failed to detect about 20% of the lung cancers when they were identified by the radiologist, which suggests that CAD may be useful in the role of second reader. (©) RSNA, 2016.

Lung nodule and cancer detection in computed tomography screening

Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.

Long-Term Follow-up Results of the DANTE Trial, a Randomized Study of Lung Cancer Screening with Spiral Computed Tomography

RATIONALE

Screening for lung cancer with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by 20% compared with screening with chest X-ray (CXR) in the National Lung Screening Trial, but uncertainty remains concerning the efficacy of LDCT screening in a community setting.

OBJECTIVES

To explore the effect of LDCT screening on lung cancer mortality compared with no screening. Secondary endpoints included incidence, stage, and resectability rates.

METHODS

Male smokers of 20+ pack-years, aged 60 to 74 years, underwent a baseline CXR and sputum cytology examination and received five screening rounds with LDCT or a yearly clinical review only in a randomized fashion.

MEASUREMENTS AND MAIN RESULTS

A total of 1,264 subjects were enrolled in the LDCT arm and 1,186 in the control arm. Their median age was 64.0 years (interquartile range, 5), and median smoking exposure was 45.0 pack-years. The median follow-up was 8.35 years. One hundred four patients (8.23%) were diagnosed with lung cancer in the screening arm (66 by CT), 47 of whom (3.71%) had stage I disease; 72 control patients (6.07%) were diagnosed with lung cancer, with 16 (1.35%) being stage I cases. Lung cancer mortality was 543 per 100,000 person-years (95% confidence interval, 413-700) in the LDCT arm versus 544 per 100,000 person-years (95% CI, 410-709) in the control arm (hazard ratio, 0.993; 95% confidence interval, 0.688-1.433).

CONCLUSIONS

Because of its limited statistical power, the results of the DANTE (Detection And screening of early lung cancer with Novel imaging TEchnology) trial do not allow us to make a definitive statement about the efficacy of LDCT screening. However, they underline the importance of obtaining additional data from randomized trials with intervention-free reference arms before the implementation of population screening.