Lung cancer screening using low-dose computed tomography in at-risk individuals: The Toronto experience

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches

BACKGROUND

Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen.

METHODS

Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set.

RESULTS

The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95).

CONCLUSIONS

We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Circulating proteome for pulmonary nodule malignancy

BACKGROUND

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules.

METHODS

Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated.

RESULTS

We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001).

CONCLUSIONS

Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis

BACKGROUND

Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.

METHODS

MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test.

RESULTS

A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies.

CONCLUSIONS

Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.

NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022

The NCCN Guidelines for Lung Cancer Screening recommend criteria for selecting individuals for screening and provide recommendations for evaluation and follow-up of lung nodules found during initial and subsequent screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

Screening for Lung Cancer: CHEST Guideline and Expert Panel Report

BACKGROUND

Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part because of the results of the National Lung Screening Trial (NLST). Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, and increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not.

METHODS

Approved panelists reviewed previously developed key questions using the Population, Intervention, Comparator, Outcome format to address the benefit and harms of low-dose CT screening, and key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Meta-analyses were performed when enough evidence was available. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached.

RESULTS

The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in seven graded recommendations and nine ungraded consensus statements.

CONCLUSIONS

Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can impact this balance.

Global evolution of research on pulmonary nodules: a bibliometric analysis

Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.

Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Lung cancer is the leading cause of cancer-related death in the US.

OBJECTIVE

To review the evidence on screening for lung cancer with low-dose computed tomography (LDCT) to inform the US Preventive Services Task Force (USPSTF).

DATA SOURCES

MEDLINE, Cochrane Library, and trial registries through May 2019; references; experts; and literature surveillance through November 20, 2020.

STUDY SELECTION

English-language studies of screening with LDCT, accuracy of LDCT, risk prediction models, or treatment for early-stage lung cancer.

DATA EXTRACTION AND SYNTHESIS

Dual review of abstracts, full-text articles, and study quality; qualitative synthesis of findings. Data were not pooled because of heterogeneity of populations and screening protocols.

MAIN OUTCOMES AND MEASURES

Lung cancer incidence, lung cancer mortality, all-cause mortality, test accuracy, and harms.

RESULTS

This review included 223 publications. Seven randomized clinical trials (RCTs) (N = 86 486) evaluated lung cancer screening with LDCT; the National Lung Screening Trial (NLST, N = 53 454) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON, N = 15 792) were the largest RCTs. Participants were more likely to benefit than the US screening-eligible population (eg, based on life expectancy). The NLST found a reduction in lung cancer mortality (incidence rate ratio [IRR], 0.85 [95% CI, 0.75-0.96]; number needed to screen [NNS] to prevent 1 lung cancer death, 323 over 6.5 years of follow-up) with 3 rounds of annual LDCT screening compared with chest radiograph for high-risk current and former smokers aged 55 to 74 years. NELSON found a reduction in lung cancer mortality (IRR, 0.75 [95% CI, 0.61-0.90]; NNS to prevent 1 lung cancer death of 130 over 10 years of follow-up) with 4 rounds of LDCT screening with increasing intervals compared with no screening for high-risk current and former smokers aged 50 to 74 years. Harms of screening included radiation-induced cancer, false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, and increases in distress. For every 1000 persons screened in the NLST, false-positive results led to 17 invasive procedures (number needed to harm, 59) and fewer than 1 person having a major complication. Overdiagnosis estimates varied greatly (0%-67% chance that a lung cancer was overdiagnosed). Incidental findings were common, and estimates varied widely (4.4%-40.7% of persons screened).

CONCLUSIONS AND RELEVANCE

Screening high-risk persons with LDCT can reduce lung cancer mortality but also causes false-positive results leading to unnecessary tests and invasive procedures, overdiagnosis, incidental findings, increases in distress, and, rarely, radiation-induced cancers. Most studies reviewed did not use current nodule evaluation protocols, which might reduce false-positive results and invasive procedures for false-positive results.

Lung cancers and pulmonary nodules detected by computed tomography scan: a population-level analysis of screening cohorts

Background

An increasing number and proportion of younger lung cancer patients have been observed worldwide, raising concerns on the optimal age to begin screening. This study aimed to investigate the association between age and findings in initial CT scans.

Methods

We searched for low-dose CT screening cohorts from electronic databases. Single-arm syntheses weighted by sample size were performed to calculate the detection rates of pulmonary nodules, lung cancers (all stages and stage I), and the proportion of stage I diseases in lung cancers. In addition, we included patients who underwent chest CT in our center as a supplementary cohort. The correlation between the detection rates and age was evaluated by the Pearson Correlation Coefficient.

Results

A total of 37 studies involving 163,442 participants were included. We found the detection rates of pulmonary nodules and lung cancers increased with age. However, the proportion of stage I diseases in lung cancers declined with increased starting age and was significantly higher in the 40-year group than in other groups (40 vs. 45, 50, 55, P<0.001). In addition, the ratio of early-stage lung cancer to the number of nodules declined with age. Similarly, in our center, the detection rates of nodules (R²=0.86, P≤0.001), all lung cancer (R²=0.99, P≤0.001) and stage I diseases (R²=0.87, P=0.001) increased with age, while the proportion of stage I diseases consistently declined with age (R²=0.97, P≤0.001).

Conclusions

Starting lung cancer screening at an earlier age is associated with a higher probability of identifying a curable disease, urging future research to determine the optimal starting age.

The challenges of implementing low-dose computed tomography for lung cancer screening in low- and middle-income countries

Lung cancer accounts for an alarming human and economic burden in low- and middle-income countries (LMICs). Recent landmark trials from high-income countries (HICs) by demonstrating that low-dose computed tomography (LDCT) screening effectively reduces lung cancer mortality have engendered enthusiasm for this approach. Here we examine the effectiveness and affordability of LDCT screening from the viewpoint of LMICs. We consider resource-restricted perspectives and discuss implementation challenges and strategies to enhance the feasibility and cost-effectiveness of LDCT screening in LMICs.

Performance of low-dose computed tomography on lung cancer screening in high-risk populations: The experience over five screening rounds in Sichuan, China

OBJECTIVE

To evaluate the performance of low-dose computed tomography (LDCT) on lung cancer screening in high-risk populations in Sichuan.

METHODS

From April 2014 to July 2018, LDCT was performed annually on 3185 subjects aged 50-74 years who had smoked ≥ 20 pack-years (or subjects having quit smoking within 5 years). Information about all deaths and lung cancer diagnoses were obtained by active investigation, or passive matching to disease surveillance system.

RESULTS

The screening population had a median age of 60 years. 62.4 % of which were current smokers and had smoked 30 pack-years. After participating in the baseline screening, the compliance rates of subjects consecutively completing one round, two rounds, three rounds, and four rounds of annual screening were 67.22 %, 52.84 %, 43.24 %, and 40.04 %, respectively. The positive rates in baseline and annual screening were 6.53 % and 5.79 %, respectively. During the 5 rounds, a total of 9522 person-times were screened by LDCT with a screening sensitivity of 89.13 % (95 % CI: 76.96-95.27), specificity of 94.36 % (95 % CI: 93.88-94.81), positive predictive value of 7.13 % (95 % CI: 5.30-9.53), and negative predictive value of 99.94 % (95 % CI: 99.87-99.98). There were no statistically significant performance differences between baseline and annual screening. The difference in the proportion of screen-detected stage I lung cancer between baseline screening and annual screening was not statistically significant, neither.

CONCLUSION

The application of LDCT on lung cancer screening in high-risk populations shows favorable compliance and a high screening performance in the project area of Sichuan,China.

Urinary fluorescent metabolite O-aminohippuric acid is a useful biomarker for lung cancer detection

INTRODUCTION

Urine contains diagnostically important metabolites that can act as natural fluorophores. However, whether these fluorescent metabolites can be used in lung cancer diagnosis is unknown.

OBJECTIVES

This study was conducted to determine whether fluorescent urinary metabolites could be useful biomarkers for lung cancer detection.

METHODS

A total of 46 lung cancer patients and 185 volunteers without cancer were evaluated between November 2013 and November 2014. Samples of the first urine of the day were collected from lung cancer patients and diagnosed at the Hamamatsu University School of Medicine and the Hamamatsu Medical Center prior to cancer treatment, and from volunteers without cancer at the Hamamatsu Medical Imaging Center. Fluorescent urinary metabolites were screened by high-performance liquid chromatography and select effective fluorescent substances for distinguishing cancer from non-cancer status.

RESULTS

The fraction of patients at each stage of cancer severity were: 41.3% stage I, 8.7% stage II, 19.6% stage III, and 30.4% stage IV. A robust predictive biomarker for lung cancer was selected by the multivariate logistic analysis of fluorescent metabolites and identified to be O-aminohippuric acid (OAH). The area under the curve (AUC) data for OAH was 0.837 (95% CI 0.769-0.898, P < 0.001).

CONCLUSION

We identified a fluorescent urinary metabolite that can predict lung cancer. OAH exceeds the AUC (0.817) of lung cancer detection by AminoIndex® cancer screening, can be analyzed non-invasively without additional sample processing, and may be a valuable addition to existing lung cancer prediction models.

Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways

BACKGROUND

Micro(mi)RNAs, potent gene expression regulators associated with tumorigenesis, are stable, abundant circulating molecules, and detectable in plasma. Thus, miRNAs could potentially be useful in early lung cancer detection. We aimed to identify circulating miRNA signatures in plasma from patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), and to verify whether miRNAs regulate lung oncogenesis pathways.

METHODS

RNA isolated from 139 plasma samples (40 LUAD, 38 LUSC; 61 healthy/non-diseased individuals) were divided into discovery (38 patients; 21 controls for expression quantification using an 800-miRNA panel; Nanostring nCounter®) and validation (40 patients; 40 controls; TaqMan® RT-qPCR) cohorts. Elastic net, Maximizing-R-Square Analysis (MARSA), and C-Statistics were applied for miRNA signature identification.

RESULTS

When compared to healthy individuals, 580 of 606 deregulated miRNAs in LUAD and 221 of 226 deregulated miRNAs in LUSC had significantly increased levels. Among the 10 most significantly overexpressed miRNAs, 6 were common to patients with LUAD and LUSC. Further analysis identified three signatures composed of 12 miRNAs. Signatures included miRNAs commonly overexpressed in patient plasma. Enriched pathways included target genes modulated by three miRNAs in the C-Statistics signature: miR-16-5p, miR-92a-3p, and miR-451a.

CONCLUSIONS

The 3-miRNA signature (miR-16-5p, miR-92a-3p, miR-451a) had high specificity (100%) and sensitivity (84%) to predict cancer (LUAD and LUSC). These miRNAs are predicted to modulate genes and pathways with known roles in lung tumorigenesis, including EGFR, K-RAS, and PI3K/AKT signaling, suggesting that the 3-miRNA signature is biologically relevant in adenocarcinoma and squamous cell carcinoma of the lung.

Comparison between radiofrequency ablation and sublobar resections for the therapy of stage I non-small cell lung cancer: a meta-analysis

Background

Sublobar resection (SLR) and radiofrequency ablation (RFA) are the two minimally invasive procedures performed for treating stage I non-small cell lung cancer (NSCLC). This study aimed to compare SLR and RFA for the treatment of stage I NSCLC using the meta-analytical method.

Methods

We searched PubMed and Embase for articles published till December 2019 to evaluate the comparative studies and assess the survival and progression-free survival rates and postoperative complications (PROSPERO registration number: CRD42018087587). A meta-analysis was performed by combining the outcomes of the reported incidences of short-term morbidity and long-term mortality. The fixed or random effects model was utilized to calculate the pooled odds ratios (OR) and the 95% confidence intervals.

Results

Four retrospective studies were considered in the course of this study. The studies included a total of 309 participants; 154 were assigned to the SLR group, and 155 were assigned to the RFA group. Moreover, there were statistically significant differences between the one- and three-year survival rates and one- and three-year progression-free survival rates for the two groups, which were in favor of the SLR group. Among the post-surgical complications, pneumothorax and pleural effusion were more common for the SLR group, while cardiac abnormalities were prevalent in the RFA group. There was no difference in prevalence of hemoptysis between SLR and RFA groups, which might be attributed to the limited study sample size.

Conclusion

Considering the higher survival rates and disease control in the evaluated cases, surgical resection is the preferred treatment method for stage I NSCLC. RFA can be considered a valid alternative in patients not eligible for surgery and in high-risk patients as it is less invasive and requires shorter hospital stay.

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

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.

Current Practice in the Management of Pulmonary Nodules Detected on Computed Tomography Chest Scans

Lung cancer is associated with high mortality. It can present as one or more pulmonary nodules identified on computed tomography (CT) chest scans. The National Lung Screening Trial has shown that the use of low-dose CT chest screening can reduce deaths due to lung cancer. High adherence to appropriate follow-up of positive results, including imaging or interventional approaches, is an important aspect of pulmonary nodule management. Our study is one of the first to evaluate the current practice in managing pulmonary nodules and to explore potential causes for nonadherence to follow-up. This is a retrospective analysis at St. Paul's Hospital, a tertiary healthcare center in Vancouver, British Columbia, Canada. We first identified CT chest scans between January 1 to June 30, 2014, that demonstrated one or more pulmonary nodules equal to or greater than 6 mm in diameter. We then looked for evidence of interventional (surgical resection or biopsy, or bronchoscopy for transbronchial biopsy and cytology) and radiological follow-up of the pulmonary nodule by searching on the province-wide CareConnect eHealth Viewer patient database. A total of 1614 CT reports were analyzed and 139 (8.6%) had a positive finding. Out of the 97 patients who received follow-up, 54.6% (N = 53) was referred for a repeat CT chest scan and 36.1% (N = 35) and 9.3% (N = 9) were referred for interventional biopsy and surgical resection, respectively. In our study, 30.2% (N = 42) of the patients with pulmonary nodules were nonadherent to follow-up. Despite the radiologist's recommendation for follow-up within a certain time interval, only 36% had repeat imaging in a timely manner. Our findings reflect the current practice in the management of pulmonary nodules and suggest that there is a need for improvement at our academic center. Adherence to follow-up is important for the potentially near-future implementation of lung cancer screening.

Content-based retrieval for lung nodule diagnosis using learned distance metric

Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a vector of 26 texture features. We then propose a content-based image retrieval (CBIR) scheme to classify between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then computed to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Screening for Lung Cancer: CHEST Guideline and Expert Panel Report

BACKGROUND

Low-dose chest CT screening for lung cancer has become a standard of care in the United States in the past few years, in large part due to the results of the National Lung Screening Trial. The benefit and harms of low-dose chest CT screening differ in both frequency and magnitude. The translation of a favorable balance of benefit and harms into practice can be difficult. Here, we update the evidence base for the benefit, harms, and implementation of low radiation dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not.

METHODS

Approved panelists developed key questions using the PICO (population, intervention, comparator, and outcome) format to address the benefit and harms of low-dose CT screening, as well as key areas of program implementation. A systematic literature review was conducted by using MEDLINE via PubMed, Embase, and the Cochrane Library. Reference lists from relevant retrievals were searched, and additional papers were added. The quality of the evidence was assessed for each critical or important outcome of interest using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached.

RESULTS

The systematic literature review identified 59 studies that informed the response to the 12 PICO questions that were developed. Key clinical questions were addressed resulting in six graded recommendations and nine ungraded consensus based statements.

CONCLUSIONS

Evidence suggests that low-dose CT screening for lung cancer results in a favorable but tenuous balance of benefit and harms. The selection of screen-eligible patients, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can affect this balance. Additional research is needed to optimize the approach to low-dose CT screening.

Screen-detected multiple primary lung cancers in the ITALUNG trial

Occurrence of multiple primary lung cancers (MPLC) in individuals undergoing low-dose computed tomography (LDCT) screening has not been thoroughly addressed. We investigated MPLC in subjects recruited in the ITALUNG randomized clinical trial. Cases of cytologically/histologically proven MPLC detected at screening LDCT or follow-up CT were selected and pathologically re-evaluated according to the WHO 2015 classification. Overall 16 MPLC were diagnosed at screening LDCT (n=14, all present at baseline) or follow-up CT (n=2) in six subjects (4 in one subject, 3 in two and 2 in three subjects), representing 0.43% of the 1,406 screenees and 15.8% of the 38 subjects with at least one screen-detected primary lung cancer. MPLC included 9 adenocarcinomas in three subjects and a combination of 7 different tumour histotypes in three subjects. MPLC, mostly adenocarcinomas, are not uncommon in smokers and ex-smokers with at least one LDCT screen detected primary lung cancer.

The relationship between demands for lung cancer screening and the constructs of health belief model: a cross-sectional survey in Hefei, China

The aim of investigation is to explore the relationship between demands for lung cancer screening (LCS) and the constructs derived from the health belief model (HBM) in Hefei. The study collected data about socio-demographics, health beliefs in and demands for LCS during early June to later July 2015. By constructing a LCS demands HBM constructs, it calculated indices of demands for LCS (DSI) and HBM constructs, which include perceived risk (PR) and seriousness (PS) of the cancers; and perceived effectiveness (PE), benefits (PB) and difficulties (PD) of the screening. It also performed descriptive and multivariate regression analysis of the demands and the HBM constructs. The amount of 823 respondents participated and completed the survey. 6.4% of them had ever undertaken LCS, whereas 60.1% of them expressed willingness to accept the service of LCS if it is free. In multiple regression analysis which used weights in calculating the HBM construct indices, education displayed significant positive associations with DSI (p = .044), and most of HBM constructs indices (PSI, PRI, PBI, and PDI) were statistically significant with DSI (p < .05). HBM-based constructs regarding LCS have important effects on demands for the service, and may provide effective paths to cancer screening promotion.

[Lung Nodules Assessment--Analysis of Four Guidelines]

近20年来，随着计算机断层扫描（computed tomography, CT）技术的提高和肺癌高危人群筛查的普及，越来越多的肺部小结节被发现，然而肺结节的定性诊断仍有很多困难。肺结节是临床上一种常见的现象，恶性结节早期发病比较隐匿，如果不进行早期干预，其病程迅速、恶性程度强、预后差。如果能在早期阶段对病灶进行手术切除，将会明显改善肺癌患者的预后。目前针对肺结节的处理指南层出不穷，但各大指南均未达成统一的共识。本文拟对在国内影响最大的四个指南：美国国家综合癌症网络非小细胞肺癌（non-small cell lung cancer, NSCLC）临床实践指南、美国胸科医师协会肺癌诊疗指南、Fleischner-Society肺结节处理策略指南、肺结节的评估亚洲共识指南所推荐的肺结节诊断和处理策略进行介绍和分析。

Thoracic muscle cross-sectional area is associated with hospital length of stay post lung transplantation: a retrospective cohort study

Low muscle mass is common in lung transplant (LTx) candidates; however, the clinical implications have not been well described. The study aims were to compare skeletal muscle mass in LTx candidates with controls using thoracic muscle cross-sectional area (CSA) from computed tomography and assess the association with pre- and post-transplant clinical outcomes. This was a retrospective, single-center cohort study of 527 LTx candidates [median age: 55 IQR (42-62) years; 54% male]. Thoracic muscle CSA was compared to an age- and sex-matched control group. Associations between muscle CSA and pre-transplant six-minute walk distance (6MWD), health-related quality of life (HRQL), delisting/mortality, and post-transplant hospital outcomes and one-year mortality were evaluated using multivariable regression analysis. Muscle CSA for LTx candidates was about 10% lower than controls (n = 38). Muscle CSA was associated with pre-transplant 6MWD, but not HRQL, delisting or pre- or post-transplant mortality. Muscle CSA (per 10 cm² difference) was associated with shorter hospital stay [0.7 median days 95% CI (0.2-1.3)], independent of 6MWD. In conclusion, thoracic muscle CSA is a simple, readily available estimate of skeletal muscle mass predictive of hospital length of stay, but further study is needed to evaluate the relative contribution of muscle mass versus functional deficits in LTx candidates.

Cost-Effectiveness of a Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer

INTRODUCTION

The use of a bronchial genomic classifier has been shown to improve the diagnostic accuracy of bronchoscopy for suspected lung cancer by identifying patients who may be more suitable for radiographic surveillance as opposed to invasive procedures. Our objective was to assess the cost-effectiveness of bronchoscopy plus a genomic classifier versus bronchoscopy alone in the diagnostic work-up of patients at intermediate risk for lung cancer.

METHODS

A decision-analytic Markov model was developed to project the costs and effects of two competing strategies by using test performance from the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer-1 and Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer-2 studies. The diagnostic accuracy of noninvasive and invasive follow-up, as well as associated adverse event rates, were derived from published literature. Procedure costs were based on claims data and 2016 inpatient and outpatient reimbursement amounts. The model projected the number of invasive follow-up procedures, 2-year costs and quality-adjusted life-years (QALYs) by strategy, and resulting incremental cost-effectiveness ratio discounted at 3% per annum.

RESULTS

Use of the genomic classifier reduced invasive procedures by 28% at 1 month and 18% at 2 years, respectively. Total costs and QALY gain were similar with classifier use ($27,221 versus $27,183 and 1.512 versus 1.509, respectively), resulting in an incremental cost-effectiveness ratio of $15,052 per QALY.

CONCLUSIONS

Our analysis suggests that the use of a genomic classifier is associated with meaningful reductions in invasive procedures at about equal costs and is therefore a high-value strategy in the diagnostic work-up of patients at intermediate risk of lung cancer.

Similarity measurement of lung masses for medical image retrieval using kernel based semisupervised distance metric

PURPOSE

To develop a new algorithm to measure the similarity between the query lung mass and reference lung mass data set for content-based medical image retrieval (CBMIR).

METHODS

A lung mass data set including 746 mass regions of interest (ROIs) was assembled. Among them, 375 ROIs depicted malignant lesions and 371 depicted benign lesions. Each mass ROI is represented by a vector of 26 texture features. A kernel function was employed to map the original data in input space to a feature space. In this space, a semisupervised distance metric was learned, which used differential scatter discriminant criterion to represent the semantic relevance, and the regularization term to represent the visual similarity. The learned distance metric can measure the similarity of the query mass and reference mass data set. The clustering accuracy is used to configure the parameters. The retrieval accuracy and classification accuracy are used as the performance assessment index.

RESULTS

After configuring the parameters, a mean clustering accuracy of 0.87 can be achieved. For retrieval accuracy, our algorithm achieves better performance than other state-of-the-art retrieval algorithms when applying a leave-one-out validation method to the testing data set. For classification accuracy, the area under the ROC curve of our algorithm can be achieved as 0.941 ± 0.006. The running times of 346 query images with the proposed algorithm are 5.399 and 6.0 s, respectively.

CONCLUSIONS

The study results demonstrated the proposed algorithm outperforms the compared algorithms, when taking the semantic relevant and visual similarity into account in kernel space. The algorithm can be used in a CBMIR system for a query mass to retrieve similarity masses, which can help doctors make better decisions.

Localizing small lung lesions in video-assisted thoracoscopic surgery via radiofrequency identification marking

BACKGROUND

To facilitate accurate localization of small lung lesions in thoracoscopic surgery, we employed a micro-radiofrequency identification tag designed to be delivered through the 2-mm working channel of a flexible bronchoscope. This report presents the results of preclinical studies of our novel localizing technique in a canine model.

METHODS

To evaluate functional placement, three types of tags [Group A, tag alone (n = 18); Group B, tag + resin anchor (n = 15); and Group C, tag + NiTi coil anchor (n = 15)] were bronchoscopically placed in subpleural areas and subsegmental bronchi via our new delivery device; tags were examined radiographically on days 0-7 and day 14. In addition, eight tags, which were placed at a mean depth of 13.3 mm (range 9-15.7 mm) from visceral pleura in bronchi with a mean diameter of 1.46 mm (range 0.9-2.3 mm), were recovered by partial lung resection under video-assisted thoracoscopic surgery using a 13.56-MHz wand-shaped probe with a 30-mm communication range.

RESULTS

Peripheral airway placement: Group C had a significantly higher retention rate than the other two groups (retention rate at day 14: Group A, 11.1 %; Group B, 26.7 %; Group C, 100.0 %; P < 0.0001). Central airway placement: Overall retention rate was 73.3 % in Group C, and placement was possible in bronchi of up to 3.3 mm in diameter. Outcomes of partial resection: Tag recovery rate was 100 %, mean time required for tag detection was 10.8 s (range 8-15 s), and mean surgical margin from the delivered tag was 9.13 mm (range 6-13 mm).

CONCLUSION

Radiofrequency identification marking enabled accurate localization with depth, which could ensure effective deep resection margins.

Can CT Screening Give Rise to a Beneficial Stage Shift in Lung Cancer Patients? Systematic Review and Meta-Analysis

OBJECTIVES

To portray the stage characteristics of lung cancers detected in CT screenings, and explore whether there's universal stage superiority over other methods for various pathological types using available data worldwide in a meta-analysis approach.

MATERIALS AND METHODS

EMBASE and MEDLINE were searched for studies on lung cancer CT screening in natural populations through July 2015 without language or other filters. Twenty-four studies (8 trials and 16 cohorts) involving 1875 CT-detected lung cancer patients were enrolled and assessed by QUADAS-2. Pathology-confirmed stage information was carefully extracted by two reviewers. Stage I or limited stage proportions were pooled by random effect model with Freeman-Tukey double arcsine transformation.

RESULTS

Pooled stage I cancer proportion in CT screenings was 73.2% (95% confidence interval: 68.6%, 77.5%), with a significant rising trend (Ptrend<0.05) from baseline (64.7%) to ≥5 repeat rounds (87.1%). Relative to chest radiograph and usual care, the increased stage I proportions in CT were 12.2% (P>0.05), and 46.5% (P<0.05), respectively. Pathology-specifically, adenocarcinomas (66%) and squamous cell lung cancers (17%) composed the majority of CT-detected lung cancers, and had significantly higher stage I proportions relative to chest radiograph (bronchioloalveolar adenocarcinomas, 80.9% vs 51.4%; other adenocarcinomas, 58.8% vs 38.3%; squamous cell lung cancers, 52.3% vs 38.3%; all P<0.05). However, the percentage of small cell lung cancer was lower using CT than other detection routes, and no significant difference in limited stage proportion was observed (6.8% vs 10.8%, P>0.05).

CONCLUSION

CT screening can detect more early stage non-small cell lung cancers, but not all of them could be beneficial as there are a considerable number of indolent ones such as bronchioloalveolar adenocarcinomas. Still, current evidence is lacking regarding small cell lung cancers.

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

RATIONALE AND OBJECTIVES

Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density.

MATERIALS AND METHODS

Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared.

RESULTS

The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI.

CONCLUSIONS

The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

Screening for lung cancer: A systematic review and meta-analysis

OBJECTIVES

To examine evidence on benefits and harms of screening average to high-risk adults for lung cancer using chest radiology (CXR), sputum cytology (SC) and low-dose computed tomography (LDCT).

METHODS

This systematic review was conducted to provide up to date evidence for Canadian Task Force on Preventive Health Care (CTFPHC) lung cancer screening guidelines. Four databases were searched to March 31, 2015 along with utilizing a previous Cochrane review search. Randomized trials reporting benefits were included; any design was included for harms. Meta-analyses were performed if possible. PROSPERO #CRD42014009984.

RESULTS

Thirty-four studies were included. For lung cancer mortality there was no benefit of CXR screening, with or without SC. Pooled results from three small trials comparing LDCT to usual care found no significant benefits for lung cancer mortality. One large high quality trial showed statistically significant reductions of 20% in lung cancer mortality over a follow-up of 6.5years, for LDCT compared with CXR. LDCT screening was associated with: overdiagnosis of 10.99-25.83%; 11.18 deaths and 52.03 patients with major complications per 1000 undergoing invasive follow-up procedures; median estimate for false positives of 25.53% for baseline/once-only screening and 23.28% for multiple rounds; and 9.74 and 5.28 individuals per 1000 screened, with benign conditions underwent minor and major invasive follow-up procedures.

CONCLUSION

The evidence does not support CXR screening with or without sputum cytology for lung cancer. High quality evidence showed that in selected high-risk individuals, LDCT screening significantly reduced lung cancer mortality and all-cause mortality. However, for its implementation at a population level, the current evidence warrants the development of standardized practices for screening with LDCT and follow-up invasive testing to maximize accuracy and reduce potential associated harms.

Lung cancer screening with low-dose computed tomography: an analysis of the MEDCAC decision

Lung cancer is the leading cause of cancer death in the United States and worldwide. However, among the top 4 deadliest cancers, lung cancer is the only one not subject to routine screening. Optimism for an effective lung cancer-screening examination soared after the release of the National Lung Screening Trial results in November 2011. Since then, nearly 40 major medical societies and organizations have endorsed low-dose computed tomography (LDCT) screening. In December 2013, the United States Preventive Services Task Force also endorsed LDCT. However, the momentum for LDCT screening slowed in April 2014 when the Medicare Evidence Development and Coverage Advisory Committee (MEDCAC) panel concluded that there was not enough evidence to justify the annual use of LDCT scans for the detection of early lung cancer. This article briefly reviews the epidemiology of lung cancer, the National Lung Screening Trial study results, and the growing national endorsement of LDCT from a variety of key stakeholder organizations. We subsequently analyze and offer our evidence-based counterpoints to the major assumptions underlying the MEDCAC decision.

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.

Characterization of Individuals Taking Part in Low Dose Computed Tomography (LDCT) Screening Program

In the past years the participation rate in conventional voluntary x-ray lung screening has been around 22 % in Somogy County in Hungary. Due to the high morbidity and mortality rates of lung cancer, low participation rate of the high risk individuals on the screening is a primary question in Hungary. To obtain an effectively high level of participation in our ongoing low dose CT screening program, we had to emphasize the benefits of participation for the targeted individuals. As a first step, our aim was to gather information on the aspects affecting the individuals' will for participation. We used the most accessible source of information: individuals over the age of 50, who attended the conventional voluntary lung screening, were approached to fill a questionnaire on their habits relating to smoking, health issues and their prior participation of lung screening. 1080 adults anonymously completed the questionnaire. Analyzing the results, beside other findings, we found a unique variable factor, which altered negatively the compliance for the screening: older individuals, who started participating in the screening in obligation to the health regulations, took part in the voluntary screening programs at a significantly lower rate. Our findings led us to better understanding the complexity of decision making affecting the individual's participation and attitudes toward health issues.

TRIAL REGISTRATION

IG/03833/2012.

Chest CT scan screening for lung cancer in asbestos occupational exposure: a systematic review and meta-analysis

OBJECTIVE

Lung cancer is the most frequent malignant asbestos-related pathology and remains the most fatal cancer of industrialized countries. In heavy smokers, early detection of lung cancer with chest CT scan leads to a 20% mortality reduction. However, the use of CT scan screening for early detection of lung cancer in asbestos-exposed workers requires further investigation. This study aimed to determine whether CT scan screening in asbestos-exposed workers is effective in detecting asymptomatic lung cancer using a systematic review and meta-analysis.

METHODS

We reviewed all cohort studies involving chest CT scan screening in former asbestos-exposed workers. The search strategy used the following keywords: "asbestos," "lung cancer," "screening," and "occupation*" or "work." Databases were PubMed, Cochrane Library, Science Direct, and Embase.

RESULTS

Seven studies matched our inclusion criteria. Baseline screening detected 49 asymptomatic lung cancers among 5,074 asbestos-exposed workers. Of the 49 reported lung cancers, at least 18 were in the earliest stage (stage I), accessible to complete removal surgery. The prevalence of all lung cancers detected by CT scan screening in asbestos-exposed workers was 1.1% (95% CI, 0.6%-1.8%).

CONCLUSIONS

CT scan screening in asbestos-exposed workers is effective in detecting asymptomatic lung cancer. Detection of lung cancer in asbestos-exposed workers using CT scanning is at least equal to the prevalence in heavy smokers (1%; 95% CI, 0.09%-1.1%) and also shared a similar proportion of stage I diagnoses. Screening asbestos-exposed workers could reduce mortality in proportions previously observed among heavy smokers and, thus, should not be neglected, particularly for individuals combining both exposures.

Missed cancers in lung cancer screening--more than meets the eye

In lung cancer, early detection and diagnosis is of paramount importance. In 2011 the National Lung Screening Trial (NLST) demonstrated the effectiveness of computed tomography (CT) screening for lung cancer in reducing mortality, and results from other ongoing trials are expected to be published in the near future. A topic that has not been widely researched to date, however, is the cause for screening failure and missed lung cancers. In this issue of European Radiology, Scholten et al. describe a number of causes for false-negative screens. Some of the implications for CT screening and nodule management raised by this report are discussed. Key Points • Many causes exist for missed lung cancers in CT screening trials • Endobronchial structures, the hila and mediastinum are blind spots on screening CTs • The management of atypical nodular opacities on thoracic CT may be challenging.

The assessment of the role of baseline low-dose CT scan in patients at high risk of lung cancer

BACKGROUND

Despite the progress in contemporary medicine comprising diagnostic and therapeutic methods, lung cancer is still one of the biggest health concerns in many countries of the world. The main purpose of the study was to evaluate the detection rate of pulmonary nodules and lung cancer in the initial, helical low-dose CT of the chest as well as the analysis of the relationship between the size and the histopathological character of the detected nodules.

MATERIAL/METHODS

We retrospectively evaluated 1999 initial, consecutive results of the CT examinations performed within the framework of early lung cancer detection program initiated in Szczecin. The project enrolled persons of both sexes, aged 55-65 years, with at least 20 pack-years of cigarette smoking or current smokers. The analysis included assessment of the number of positive results and the evaluation of the detected nodules in relationship to their size. All of the nodules were classified into I of VI groups and subsequently compared with histopathological type of the neoplastic and nonneoplastic pulmonary lesions.

RESULTS

Pulmonary nodules were detected in 921 (46%) subjects. What is more, malignant lesions as well as lung cancer were significantly, more frequently discovered in the group of asymptomatic nodules of the largest dimension exceeding 15 mm.

CONCLUSIONS

The initial, low-dose helical CT of the lungs performed in high risk individuals enables detection of appreciable number of indeterminate pulmonary nodules. In most of the asymptomatic patients with histopathologically proven pulmonary nodules greater than 15 mm, the mentioned lesions are malignant, what warrants further, intensified diagnostics.

Current status of lung cancer screening

Low-dose CT (LDCT) is effective in the early detection of lung cancer, providing higher resectability and long-term survival rates. The National Lung Screening Trial shows a statistically significant mortality reduction in LDCT compared with chest radiography. The efficacy and safety of annual LDCT screening in heavy smokers must be explored, and the magnitude of benefit compared with the cost of large-scale screening. Trials in Europe have different study designs and an observational arm. Strategies to reduce lung cancer mortality should combine early detection with primary prevention and innovative biologic approaches.

Diagnostic work-up and surgery in participants of the Gdansk lung cancer screening programme: the incidence of surgery for non-malignant conditions

OBJECTIVES

Low-dose computed tomography (LDCT) screening improves lung cancer prognosis but also results in diagnostic work-up and surgical treatment in many individuals without cancer. Therefore, we analysed the procedures that screening participants underwent to better understand the extent of overdiagnosis.

METHODS

Between 2009 and 2011, 8649 healthy volunteers aged 50-75 years with a 20 pack-year smoking history underwent LDCT screening, of whom individuals with detected lung nodules had 2 years control. Participants with a nodule >10 mm in diameter or with suspected tumour morphology underwent diagnostic work-up: 283 (6%)/4694 (54%) screened participants had detected lung nodules. One hundred and four individuals underwent surgery, 27 underwent oncological treatment and 152 without a cancer diagnosis underwent further follow-up with LDCT.

RESULTS

In 75% of participants accepted for diagnostic work-up and 25% of surgical patients, the procedures were unnecessary. In 70 (24.7%) participants, a specific diagnosis was obtained mainly due to the low efficacy of fine needle aspiration biopsy [sensitivity, 65.2%; negative predictive value (NPV), 95.9%] and bronchofiberoscopy (sensitivity, 71.4%; NPV, 50%) caused by overinterpretation of LDCT [positive predictive value (PPV), 2%]. Of 104 (36.7%) surgical patients, 43 (41.4%) had a preoperative cancer diagnosis, and 61 (58.6%) underwent surgery without pathological examination. In the latter group, intervention was justified in 35 (57.3%) patients. Complications occurred in 49 (17.3%) participants subjected to diagnostic work-up. In surgical patients, 67 (64.4%) malignant and 37 (35.6%) benign lesions were resected. In the latter group, intervention was justified in only 11 (29.7%) patients. No patient died because of diagnostic or treatment procedures during the study. The complication rate was 14.5% in the malignant and 10.8% in the benign groups. A neoplasm was found in 94 screening participants, of whom 67 (71.3%) underwent surgery; the remaining 27 (28.7%) patients were not surgical candidates. Adenocarcinoma accounted for 49/67 (73%) patients who underwent surgery for non-small-cell lung cancer (NSCLC); 56/67 (84%) patients had stage I NSCLC, and 26/67 (38%) underwent video-assisted thoracoscopic surgery lobectomy.

CONCLUSIONS

Futile diagnostic work-ups and operations must be reduced before LDCT screening can be broadly used. Stage I adenocarcinoma dominated in the NSCLC patients who underwent surgery.