Screening for lung cancer with low-dose computed tomography: a systematic review and meta-analysis of the baseline findings of randomized controlled trials

Transformers in medical imaging: A survey

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

BACKGROUND

Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology.

METHODS

The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model.

RESULTS

A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively.

CONCLUSIONS

It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.

Advances in liquid biopsy-based markers in NSCLC

Lung cancer is the second most-frequently occurring cancer and the leading cause of cancer-associated deaths worldwide. Non-small cell lung cancer (NSCLC), the most common type of lung cancer is often diagnosed in middle or advanced stages and have poor prognosis. Diagnosis of disease at an early stage is a key factor for improving prognosis and reducing mortality, whereas, the currently used diagnostic tools are not sufficiently sensitive for early-stage NSCLC. The emergence of liquid biopsy has ushered in a new era of diagnosis and management of cancers, including NSCLC, since analysis of circulating tumor-derived components, such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), cell-free RNAs (cfRNAs), exosomes, tumor-educated platelets (TEPs), proteins, and metabolites in blood or other biofluids can enable early cancer detection, treatment selection, therapy monitoring and prognosis assessment. There have been great advances in liquid biopsy of NSCLC in the past few years. Hence, this chapter introduces the latest advances on the clinical application of cfDNA, CTCs, cfRNAs and exosomes, with a particular focus on their application as early markers in the diagnosis, treatment and prognosis of NSCLC.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module

SIGNIFICANCE

X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy.

AIM

To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model.

APPROACH

A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments.

RESULTS

This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( > 94.1 % ), peak signal-to-noise ratio ( > 41.7 % ), and Pearson correlation ( > 19 % ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP.

CONCLUSIONS

This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

OBJECTIVE

To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting.

METHODS

The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs.

RESULTS

A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs.

CONCLUSION

Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

Malignancy risk stratification for solitary pulmonary nodule: A clinical practice guideline

CLINICAL QUESTION

The detection rate of the solitary pulmonary nodule (SPN) is increasing with the popularization of CT scanning. Malignancy risk stratification for SPN is a major clinical difficulty.

CURRENT PRACTICE

There have been several guidelines for SPN assessment. Inconsistency of these guidelines makes the clinical application difficult and confusing.

RECOMMENDATIONS

In this Rapid Recommendation, solid and subsolid SPNs are recommended to be evaluated respectively. Six factors, namely the combination of age with sex, smoking history, history of malignancy, family history of malignancy, and nodule size, are recommended for malignancy risk stratification for both kinds of SPNs; the border of nodules (spiculation and lobulation) is recommended for evaluating solid SPNs and the density of nodules (pure or mixed ground-glass nodule) is recommended for subsolid nodules. Among them, smoking history and radiologic features (nodule diameter, border, and density) are of relatively higher importance. A scoring system was proposed to assist malignancy risk stratification of SPNs, with a total score ranging from six points to 15 points (if solid) or 17 points (if subsolid). For each SPN, regardless of solid or subsolid in nature, a total score of ≤ 7 points suggested a low risk of being malignant, while 7 to 9 points suggested medium risk, and ≥ 9 points suggested high risk.

HOW THIS GUIDELINE WAS CREATED

This rapid recommendation was developed using the MAGIC (Making GRADE the Irresistible Choice) methodological framework. First, a clinical subcommittee identified the topic of recommendation and requested evidence. Then, an independent evidence synthesis subcommittee performed a comprehensive literature review and evaluated the evidence. Finally, based on findings from the systematic review and use of real-world data, the clinical subcommittee formulated recommendations, including the scoring system, through a consensus procedure.

THE EVIDENCE

A total of 13857 patients with SPNs were included in the meta-analysis and the association between 12 candidate factors and the risk of SPNs being malignant was studied. Eventually, seven factors were recommended for SPNs evaluation, and a scoring system was proposed.

UNDERSTANDING THE RECOMMENDATION

The parameters included are objective. Therefore, this recommendation is feasible in clinical practice. However, there are several uncertainties, such as a lack of further verification. It might be misclassified by the scoring system. Clinicians could choose the most suitable scheme according to the recommendation, along with their own experience in specific situations.

Serum RNAs can predict lung cancer up to 10 years prior to diagnosis

Lung cancer (LC) prognosis is closely linked to the stage of disease when diagnosed. We investigated the biomarker potential of serum RNAs for the early detection of LC in smokers at different prediagnostic time intervals and histological subtypes. In total, 1061 samples from 925 individuals were analyzed. RNA sequencing with an average of 18 million reads per sample was performed. We generated machine learning models using normalized serum RNA levels and found that smokers later diagnosed with LC in 10 years can be robustly separated from healthy controls regardless of histology with an average area under the ROC curve (AUC) of 0.76 (95% CI, 0.68-0.83). Furthermore, the strongest models that took both time to diagnosis and histology into account successfully predicted non-small cell LC (NSCLC) between 6 and 8 years, with an AUC of 0.82 (95% CI, 0.76-0.88), and SCLC between 2 and 5 years, with an AUC of 0.89 (95% CI, 0.77-1.0), before diagnosis. The most important separators were microRNAs, miscellaneous RNAs, isomiRs, and tRNA-derived fragments. We have shown that LC can be detected years before diagnosis and manifestation of disease symptoms independently of histological subtype. However, the highest AUCs were achieved for specific subtypes and time intervals before diagnosis. The collection of models may therefore also predict the severity of cancer development and its histology. Our study demonstrates that serum RNAs can be promising prediagnostic biomarkers in an LC screening setting, from early detection to risk assessment.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules

OBJECTIVES

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules.

METHODS

A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve.

RESULTS

The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis.

CONCLUSIONS

A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules.

KEY POINTS

• A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

Use of Imaging and Diagnostic Procedures After Low-Dose CT Screening for Lung Cancer

BACKGROUND

Clinical trials have demonstrated a mortality benefit from lung cancer screening by low-dose CT (LDCT) in current or past tobacco smokers who meet criteria. Potential harms of screening mostly relate to downstream evaluation of abnormal screens. Few data exist on the rates outside of clinical trials of imaging and diagnostic procedures following screening LDCT. We describe rates in the community setting of follow-up imaging and diagnostic procedures after screening LDCT.

METHODS

We used Clinformatics Data Mart national database to identify enrollees age 55 to 80 year who underwent screening LDCT from January 1, 2016, to December 31, 2016. We assessed rates of follow-up imaging (diagnostic chest CT scan, MRI, and PET) and follow-up procedures (bronchoscopy, percutaneous biopsy, thoracotomy, mediastinoscopy, and thoracoscopy) in the 12 months following LDCT for lung cancer screening. We also assessed these rates in an age-, sex-, and number of comorbidities-matched population that did not undergo LDCT to estimate rates unrelated to the screening LDCT. We then reported the adjusted rate of follow-up testing as the observed rate in the screening LDCT population minus the rate in the non-LDCT population.

RESULTS

Among 11,520 enrollees aged 55 to 80 years who underwent LDCT in 2016, the adjusted rates of follow up 12 months after LDCT examinations were low (17.7% for imaging and 3.1% for procedures). Among procedures, the adjusted rates were 2.0% for bronchoscopy, 1.3% for percutaneous biopsy, 0.9% for thoracoscopy, 0.2% for mediastinoscopy, and 0.4% for thoracotomy. Adjusted rates of follow-up procedures were higher in enrollees undergoing an initial screening LDCT (3.3%) than in those after a second screening examination (2.2%).

CONCLUSIONS

In general, imaging and rates of procedures after screening LDCT was low in this commercially insured population.

A 10-year prediagnostic follow-up study shows that serum RNA signals are highly dynamic in lung carcinogenesis

The majority of lung cancer (LC) patients are diagnosed at a late stage, and survival is poor. Circulating RNA molecules are known to have a role in cancer; however, their involvement before diagnosis remains an open question. In this study, we investigated circulating RNA dynamics in prediagnostic LC samples, focusing on smokers, to identify if and when disease-related signals can be detected in serum. We sequenced small RNAs in 542 serum LC samples donated up to 10 years before diagnosis and 519 matched cancer-free controls coming from 905 individuals in the Janus Serum Bank. This sample size provided sufficient statistical power to independently analyze time to diagnosis, stage, and histology. The results showed dynamic changes in differentially expressed circulating RNAs specific to LC histology and stage. The greatest number of differentially expressed RNAs was identified around 7 years before diagnosis for early-stage LC and 1-4 years prior to diagnosis for locally advanced and advanced-stage LC, regardless of LC histology. Furthermore, NSCLC and SCLC histologies have distinct prediagnostic signals. The majority of differentially expressed RNAs were associated with cancer-related pathways. The dynamic RNA signals pinpointed different phases of tumor development over time. Stage-specific RNA profiles may be associated with tumor aggressiveness. Our results improve the molecular understanding of carcinogenesis. They indicate substantial opportunity for screening and improved treatment and will guide further research on early detection of LC. However, the dynamic nature of the RNA signals also suggests challenges for prediagnostic biomarker discovery.

Low-dose computed tomography for lung cancer screening in high-risk populations: a systematic review and economic evaluation

BACKGROUND

Diagnosis of lung cancer frequently occurs in its later stages. Low-dose computed tomography (LDCT) could detect lung cancer early.

OBJECTIVES

To estimate the clinical effectiveness and cost-effectiveness of LDCT lung cancer screening in high-risk populations.

DATA SOURCES

Bibliographic sources included MEDLINE, EMBASE, Web of Science and The Cochrane Library.

METHODS

Clinical effectiveness - a systematic review of randomised controlled trials (RCTs) comparing LDCT screening programmes with usual care (no screening) or other imaging screening programmes [such as chest X-ray (CXR)] was conducted. Bibliographic sources included MEDLINE, EMBASE, Web of Science and The Cochrane Library. Meta-analyses, including network meta-analyses, were performed. Cost-effectiveness - an independent economic model employing discrete event simulation and using a natural history model calibrated to results from a large RCT was developed. There were 12 different population eligibility criteria and four intervention frequencies [(1) single screen, (2) triple screen, (3) annual screening and (4) biennial screening] and a no-screening control arm.

RESULTS

Clinical effectiveness - 12 RCTs were included, four of which currently contribute evidence on mortality. Meta-analysis of these demonstrated that LDCT, with ≤ 9.80 years of follow-up, was associated with a non-statistically significant decrease in lung cancer mortality (pooled relative risk 0.94, 95% confidence interval 0.74 to 1.19). The findings also showed that LDCT screening demonstrated a non-statistically significant increase in all-cause mortality. Given the considerable heterogeneity detected between studies for both outcomes, the results should be treated with caution. Network meta-analysis, including six RCTs, was performed to assess the relative clinical effectiveness of LDCT, CXR and usual care. The results showed that LDCT was ranked as the best screening strategy in terms of lung cancer mortality reduction. CXR had a 99.7% probability of being the worst intervention and usual care was ranked second. Cost-effectiveness - screening programmes are predicted to be more effective than no screening, reduce lung cancer mortality and result in more lung cancer diagnoses. Screening programmes also increase costs. Screening for lung cancer is unlikely to be cost-effective at a threshold of £20,000/quality-adjusted life-year (QALY), but may be cost-effective at a threshold of £30,000/QALY. The incremental cost-effectiveness ratio for a single screen in smokers aged 60-75 years with at least a 3% risk of lung cancer is £28,169 per QALY. Sensitivity and scenario analyses were conducted. Screening was only cost-effective at a threshold of £20,000/QALY in only a minority of analyses.

LIMITATIONS

Clinical effectiveness - the largest of the included RCTs compared LDCT with CXR screening rather than no screening. Cost-effectiveness - a representative cost to the NHS of lung cancer has not been recently estimated according to key variables such as stage at diagnosis. Certain costs associated with running a screening programme have not been included.

CONCLUSIONS

LDCT screening may be clinically effective in reducing lung cancer mortality, but there is considerable uncertainty. There is evidence that a single round of screening could be considered cost-effective at conventional thresholds, but there is significant uncertainty about the effect on costs and the magnitude of benefits.

FUTURE WORK

Clinical effectiveness and cost-effectiveness estimates should be updated with the anticipated results from several ongoing RCTs [particularly the NEderlands Leuvens Longkanker Screenings ONderzoek (NELSON) screening trial].

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016048530.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

BACKGROUND

Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.

METHODS AND FINDINGS

We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.

CONCLUSIONS

In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.

Low-dose computed tomography (LDCT) versus other cancer screenings in early diagnosis of lung cancer: A meta-analysis

BACKGROUND

Lung cancer is the leading cause of cancer mortality worldwide. It is often diagnosed at an advanced stage when treatment is no longer possible. Early population-based screening may provide an opportunity for early diagnosis and reduce mortality rates.

METHODS

Study characteristics were collected and outcome data (lung cancer diagnosis and mortality) were extracted and used for meta-analysis. Statistical analyses were performed using OpenMetaAnalyst-0.1503 software. The odds ratio (OR) and 95% confidence interval (CI) were used to assess LDCT compared to other screening methods under the random-effects model. The I2 statistic was used to assess heterogeneity.

RESULTS

Pooling data from 4 studies (64,129 patients) showed a higher incidence of diagnosed lung cancer with LDCT screening (OR = 1.86, 95% CI: 1.02-3.37), compared to other screening tools. However, no significant difference (OR = 1.13, 95% CI: 0.78-1.64) was found in lung cancer mortality between both groups.

CONCLUSIONS

Although no significant difference was found between LDCT and other control groups in terms of lung cancer mortality, this meta-analysis suggests an increased diagnosis of lung cancer with LDCT as compared with other screening modalities. This meta-analysis displays the potential but also the limitations of LDCT for early lung cancer detection.

MDC and BLC are independently associated with the significant risk of early stage lung adenocarcinoma

Background

This prospective study was designed to investigate the association between ten circulating inflammatory biomarkers and the risk for early stage lung adenocarcinoma.

Methods

All inflammatory biomarkers were measured in 228 patients with early stage (IA to IIB) lung adenocarcinoma and 228 age-, sex- and smoking-matched healthy controls by using the Luminex bead-based assay.

Results

Only two biomarkers were significantly associated with the risk of early stage lung adenocarcinoma after the Bonferroni correction: the multivariate odd ratio (OR) (95% confidence interval or CI) was 0.29 (0.16-0.53) for MDC and 4.17 (2.23-7.79) for BLC for the comparison of patients in the 4^th quartile with the 1^st quartile (both P<0.0001). When analysis was restricted to never smokers (196 patients/196 controls), MDC and BLC were still significantly associated with the risk of early stage lung adenocarcinoma (OR, 95% CI, P: 0.37, 0.21-0.66, P<0.0001 for MDC and 2.78, 1.48-5.22, P =0.001 for BLC). Furthermore, elevated BLC was associated with a 2.90-fold (95% CI: 1.03-8.17, P=0.037) increased risk of subcentimeter lung adenocarcinoma, and there was an increasing trend for BLC with the progression of subcentimeter lung adenocarcinoma.

Conclusion

Our findings demonstrated that MDC and BLC were independently associated with the significant risk of early stage lung adenocarcinoma, even in non-smokers and in stage IA patients. BLC was further identified to play a carcinogenic role in the progression of lung adenocarcinoma.

A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer

Introduction

Circulating microRNAs are promising biomarkers for diagnosis, predication and prognostication of diseases. Lung cancer is the cancer disease accountable for most cancer deaths, largely due to being diagnosed at late stages. Therefore, diagnosing lung cancer patients at an early stage is crucial for improving the outcome. The purpose of this study was to identify circulating microRNAs for detection of early stage lung cancer, capable of discriminating lung cancer patients from those with chronic obstructive pulmonary disease (COPD) and healthy volunteers.

Results

We identified 7 microRNAs separating lung cancer patients from controls. By using RT-qPCR, we validated 6 microRNAs (miR-429, miR-205, miR-200b, miR-203, miR-125b and miR-34b) with a significantly higher abundance in serum from NSCLC patients. Furthermore, the 6 miRNAs were validated in a different dataset, revealing an area under the receiver operating characteristic curve of 0.89 for stage I-IV and 0.88 for stage I/II.

Materials and Methods

We profiled the expression of 754 unique microRNAs by TaqMan Low Density Arrays, and analyzed serum from 38 patients with NSCLC, 16 patients suffering from COPD and 16 healthy volunteers from Norway, to explore their potential as diagnostic biomarkers. For validation, we analyzed serum collected from high-risk individuals enrolled in the Valencia branch of the International Early Lung Cancer Action Program screening trial (n=107) in addition to 51 lung cancer patients.

Conclusion

Considering the accessibility and stability of circulating miRNAs, these 6 microRNAs are promising biomarkers as a supplement in future screening studies.

Small pulmonary nodules in baseline and incidence screening rounds of low-dose CT lung cancer screening

Currently, lung cancer screening by low-dose computed tomography (LDCT) is widely recommended for high-risk individuals by US guidelines, but there still is an ongoing debate concerning respective recommendations for European countries. Nevertheless, the available data regarding pulmonary nodules released by lung cancer screening studies could improve future screening guidelines, as well as the clinical practice of incidentally detected pulmonary nodules on routine CT scans. Most lung cancer screening trials present results for baseline and incidence screening rounds separately, clustering pulmonary nodules initially found at baseline screening and newly detected pulmonary nodules after baseline screening together. This approach does not appreciate possible differences among pulmonary nodules detected at baseline and firstly detected at incidence screening rounds and is heavily influenced by methodological differences of the respective screening trials. This review intends to create a basis for assessing non-calcified pulmonary nodules detected during LDCT lung cancer screening in a more clinical relevant manner. The aim is to present data of non-calcified pulmonary baseline nodules and new non-calcified pulmonary incident nodules without clustering them together, thereby also simplifying translation to the clinical practice of incidentally detected pulmonary nodules. Small pulmonary nodules newly detected at incidence screening rounds of LDCT lung cancer screening may possess a greater lung cancer probability than pulmonary baseline nodules at a smaller size, which is essential for the development of new guidelines.

Predicting Malignant Nodules from Screening CT Scans

OBJECTIVES

The aim of this study was to determine whether quantitative analyses ("radiomics") of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.

METHODS

Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.

RESULTS

The best models used 23 stable features in a random forests classifier and could predict nodules that would become cancerous 1 and 2 years hence with accuracies of 80% (area under the curve 0.83) and 79% (area under the curve 0.75), respectively. Radiomics outperformed the Lung Imaging Reporting and Data System and volume-only approaches. The performance of the McWilliams risk assessment model was commensurate.

CONCLUSIONS

The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.

Low-dose computed tomography screening for lung cancer in populations highly exposed to tobacco: A systematic methodological appraisal of published randomised controlled trials

Low-dose computed tomography (LDCT) screening recommendations for lung cancer are contradictory. The French National Authority for Health commissioned experts to carry a systematic review on the effectiveness, acceptability and safety of lung cancer screening with LDCT in subjects highly exposed to tobacco. We used MEDLINE and Embase databases (2003-2014) and identified 83 publications representing ten randomised control trials. Control arms and methodology varied considerably, precluding a full comparison and questioning reproducibility of the findings. From five trials reporting mortality results, only the National Lung Screening Trial found a significant decrease of disease-specific and all-cause mortality with LDCT screening compared to chest X-ray screening. None of the studies provided all information needed to document the risk-benefit balance. The lack of statistical power and the methodological heterogeneity of European trials question on the possibility of obtaining valid results separately or by pooling. We conclude, in regard to the lack of strong scientific evidence, that LDCT screening should not be recommended in subjects highly exposed to tobacco.

To Screen or not to Screen: Low Dose Computed Tomography in Comparison to Chest Radiography or Usual Care in Reducing Morbidity and Mortality from Lung Cancer

Lung cancer has the highest mortality rate of all cancers. This paper seeks to address the question: Can the mortality of lung cancer be decreased by screening with low-dose computerized tomography (LDCT) in higher risk patients compared to chest X-rays (CXR) or regular patient care? Currently, CXR screening is recommended for certain high-risk patients. Several recent trials have examined the effectiveness of LDCT versus chest radiography or usual care as a control. These trials include National Lung Screening Trial (NLST), Detection And screening of early lung cancer with Novel imaging TEchnology (DANTE), Lung Screening Study (LSS), Depiscan, Italian Lung (ITALUNG), and Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study). NLST, the largest trial (n=53, 454), demonstrated a decrease in mortality from lung cancer in the LDCT group (RRR=20%, P=0.004). LSS demonstrated a greater sensitivity in detecting both early stage and any stage of lung cancer in comparison to traditional CXR. Although the DANTE trial yielded data consistent with findings in LSS, it also showed that via LDCT screening a greater proportion of patients were placed under unnecessary surgical procedures. The Depiscan trial yielded a high nodule detection rate at the cost of a high false-positive rate compared to CXR screening. The ITALUNG and NELSON trials demonstrated the early detection capabilities of LDCT for lung cancers compared to usual care without surveillance imaging. False-positive findings with unnecessary workup, intervention, and radiation exposure remain significant concerns for routine LDCT screening. However, current data suggests LDCT may provide a highly sensitive and specific means for detecting lung cancers and reducing mortality.

Levels of circulating MMP-7 degraded elastin are elevated in pulmonary disorders

OBJECTIVES

Elastin is a signature protein of the lungs. Matrix metalloproteinase-7 (MMP-7) is important in lung defence mechanisms and degrades elastin. However, MMP-7 activity in regard to elastin degradation has never been quantified serologically in patients with lung diseases. An assay for the quantification of MMP-7 generated elastin fragments (ELM7) was therefore developed to investigate MMP-7 derived elastin degradation in pulmonary disorders such as idiopathic pulmonary fibrosis (IPF) and lung cancer.

DESIGN AND METHODS

Monoclonal antibodies (mABs) were raised against eight carefully selected MMP-7 cleavage sites on elastin. After characterisation and validation of the mABs, one mAB targeting the ELM7 fragment was chosen. ELM7 fragment levels were assessed in serum samples from patients diagnosed with IPF (n=123, baseline samples, CTgov reg. NCT00786201), and lung cancer (n=40) and compared with age- and sex-matched controls.

RESULTS

The ELM7 assay was specific towards in vitro MMP-7 degraded elastin and the ELM7 neoepitope but not towards other MMP-7 derived elastin fragments. Serum ELM7 levels were significantly increased in IPF (113%, p<0.0001) and lung cancer (96%, p<0.0001) compared to matched controls.

CONCLUSIONS

MMP-7-generated elastin fragments can be quantified in serum and may reflect pathological lung tissue turnover in several important lung diseases.

A meta-analysis: is low-dose computed tomography a superior method for risky lung cancers screening population?

BACKGROUND AND AIMS

Low-dose computed tomography (LDCT) has been proposed to be a new screening method to discover lung cancers in an early stage, especially those patients who are in a high risk of lung cancer. The primary objective of this meta-analysis is to systematically review the effect of LDCT on screening for lung cancers among the risky population who are older than 49 years old and with smoking exposure.

METHODS

We searched randomized controlled clinical trials (RCTs) about comparing LDCT and chest X-ray or usual caring from MEDLINE, EMBASE, and the Cochrane Library, Web of Knowledge and SpringerLink databases (January 1994 to September 2013).

RESULTS

Nine RCTs met criteria for inclusion. Screening for lung cancer using LDCT resulted in a significantly higher number of stage I lung cancers [odds ratio (OR) 2.15, 95% confidence interval (CI) 1.88-2.47], higher number of total lung cancers (OR 1.31, 95% CI 1.20-1.43) than the control. Four of the nine studies indicated that the screening method did not decrease all-cause mortality (OR 0.96, 95% CI 0.90-1.02), but decreased lung cancer-specific mortality (OR 0.84, 95% CI 0.74-0.96). Five studies showed that LDCT had higher false-positive rates (OR 8.7, 95% CI 7.43-10.19) than the group of control.

CONCLUSION

Among the risky population, LDCT screening find out more stage I lung cancers and total lung cancers compared with chest X-ray or no screening, and also shows advantages in decreasing lung cancer-specific mortality, but the screening method does not decrease all-cause mortality and have a higher false-positive rates in diagnosis.

Serum biomarkers reflecting specific tumor tissue remodeling processes are valuable diagnostic tools for lung cancer

Extracellular matrix (ECM) proteins, such as collagen type I and elastin, and intermediate filament (IMF) proteins, such as vimentin are modified and dysregulated as part of the malignant changes leading to disruption of tissue homeostasis. Noninvasive biomarkers that reflect such changes may have a great potential for cancer. Levels of matrix metalloproteinase (MMP) generated fragments of type I collagen (C1M), of elastin (ELM), and of citrullinated vimentin (VICM) were measured in serum from patients with lung cancer (n = 40), gastrointestinal cancer (n = 25), prostate cancer (n = 14), malignant melanoma (n = 7), chronic obstructive pulmonary disease (COPD) (n = 13), and idiopathic pulmonary fibrosis (IPF) (n = 10), as well as in age-matched controls (n = 33). The area under the receiver operating characteristics (AUROC) was calculated and a diagnostic decision tree generated from specific cutoff values. C1M and VICM were significantly elevated in lung cancer patients as compared with healthy controls (AUROC = 0.98, P < 0.0001) and other cancers (AUROC = 0.83 P < 0.0001). A trend was detected when comparing lung cancer with COPD+IPF. No difference could be seen for ELM. Interestingly, C1M and VICM were able to identify patients with lung cancer with a positive predictive value of 0.9 and an odds ratio of 40 (95% CI = 8.7–186, P < 0.0001). Biomarkers specifically reflecting degradation of collagen type I and citrullinated vimentin are applicable for lung cancer patients. Our data indicate that biomarkers reflecting ECM and IMF protein dysregulation are highly applicable in the lung cancer setting. We speculate that these markers may aid in diagnosing and characterizing patients with lung cancer.

A systematic review of the characteristics associated with recall rates, detection rates and positive predictive values of computed tomography screening for lung cancer

BACKGROUND

Low-dose computed tomography (LDCT) screening has been shown to reduce mortality from lung cancer but at a substantial cost in diagnostic activity. The objective of this study was to investigate the characteristics of screening programmes associated with recall rates, detection rates and positive predictive values (PPVs).

DESIGN

We conducted a systematic review of randomised trials and observational studies on LDCT screening for lung cancer. A meta-regression using random-effect logistic regressions was carried out to assess factors influencing recall rates for further investigation, cancer detection rates and PPVs of recall.

RESULTS

We used data from 63 372 prevalent screens from 16 studies of LDCT screening for lung cancer and 79 302 incident screens from nine studies. In univariable analysis, the use of a cut-off size to define nodules warranting further investigation at prevalent screens reduced recall rates [odds ratio (OR) = 0.44, 95% confidence interval (CI) 0.24-0.82 and OR = 0.42, 95% CI 0.21-0.84 for cut-off sizes of 3-4 and 5-8 mm, respectively], without significant changes in detection rates and PPVs. The number of readers (1 or ≥2) was not associated with changes in recall rates, detection rates and PPVs at prevalent and incident screens. Using the volumetry software at incident screens significantly increased the PPV (OR = 5.02, 95% CI 1.65-15.28) as a result of a decrease in recall rates (OR = 0.25, 95% CI 0.12-0.51), without significant changes in detection rates.

CONCLUSION

These results highlight the value of using a cut-off size for nodules warranting further investigation with lower recall rates at prevalent screens, whereas the volumetric assessment software at incident screens results in lower recall rates and higher PPVs. The presence of positron emission tomography in the work-up protocol might be associated with lower rates of surgical procedures for benign findings, although this hypothesis deserves further investigation.

[Survey and analysis of awareness of lung cancer prevention and control in a LDCT lung cancer screening project in Tianjin Dagang Oilfield of China]

背景与目的

已有的研究表明提高人群对肺癌防治知识的认识水平，有助于肺癌高危人群肺癌筛查项目的参与度。本研究的目的是调查卫生部肺癌早诊早治大港项目点低剂量螺旋（low dose CT, LDCT）肺癌筛查人群肺癌防治知识知晓情况及个体因素对其的影响，为肺癌高发现场的综合防治提供依据。

方法

通过整群抽样和自愿参加方法对参加LDCT筛查的大港油田肺癌高发现场职工进行问卷调查。

结果

本次调查共获得有效问卷1, 633份，调查对象的平均年龄为60.08±6.58，男性1, 343人（82.2%），女性290（17.8%）。对肺癌的知晓率、危险因素、筛查方法，体检意愿以及治疗的知晓率分别为：64.5%、77.1%、43.7%、49.6%、52.8%。多因素Logistic回归分析结果表明：教育、年龄、吸烟包年、疾病史是调查对象肺癌防治知识知晓的影响因素，教育和年龄的OR值分别为0.567（95%CI: 0.439-0.733）和1.373（95%CI: 1.084-1.739）。调查人群中80.3%的人群能接受1年1次的体检，人群体检费用承受能力不高。对被调查者体检意愿进行多因素分析得出，性别、年龄、癌症知识知晓情况以及家庭年平均收入是筛查意愿的影响因素。

结论

教育程度和吸烟影响人群对肺癌防治知识的认知情况，应加强对低教育水平人群的癌症健康教育。在肺癌高发现场，肺癌的筛查应与戒烟和健康教育紧密结合，实行肺癌的综合防治。

[Advance of lung cancer screening with low-dose spiral CT]

Lung cancer has become the leading cause of cancer mortality globally, and 5-year survival rate is very poor. Screening and early detection are vital to improve survival and decrease mortality of lung cancer. In recent 20 years, low-dose spiral CT (LDCT) screening has become a research focus in this area. Randomized controlled trials have confirmed that LDCT can decrease lung cancer mortality. However, there are still some problems of LDCT. In this paper, we summarized the controversy that whether low-dose helical CT screening can reduce lung cancer mortality or not before its effectiveness was been confirmed, the results and problems in the randomized controlled trials and gave a prospect of low-dose helical CT screening's future application.

Lung cancer screening with computer aided detection chest radiography: design and results of a randomized, controlled trial

Introduction

The sensitivity of CT based lung cancer screening for the detection of early lung cancer is balanced by the high number of benign lung nodules identified, the unknown consequences of radiation from the test, and the potential costs of a CT based screening program. CAD chest radiography may improve the sensitivity of standard chest radiography while minimizing the risks of CT based screening.

Methods

Study subjects were age 40–75 years with 10+ pack-years of smoking and/or an additional risk for developing lung cancer. Subjects were randomized to receive a PA view chest radiograph or placebo control (went through the process of being imaged but were not imaged). Images were reviewed first without then with the assistance of CAD. Actionable nodules were reported and additional evaluation was tracked. The primary outcome was the rate of developing symptomatic advanced stage lung cancer.

Results

1,424 subjects were enrolled. 710 received a CAD chest radiograph, 29 of whom were found to have an actionable lung nodule on prevalence screening. Of the 15 subjects who had a chest CT performed for additional evaluation, a lung nodule was confirmed in 4, 2 of which represented lung cancer. Both of the cancers were seen by the radiologist unaided and were identified by the CAD chest radiograph. The cumulative incidence of symptomatic advanced lung cancer was 0.42 cases per 100 person-years in the control arm; there were no events in the screening arm.

Conclusions

Further evaluation is necessary to determine if CAD chest radiography has a role as a lung cancer screening tool.

ClinicalTrials.gov identifier NCT01663155

Lung cancer screening update

Lung cancer is the leading cause of cancer-related mortality globally and the American cancer society estimates approximately 226,160 new cases and 160,340 deaths from lung cancer in the USA in the year 2012. The majority of lung cancers are diagnosed in the later stages which impacts the overall survival. The 5-year survival rate for pathological st age IA lung cancer is 73% but drops to only 13% for stage IV. Thus, early detection through screening and prevention are the keys to reduce the global burden of lung cancer. This article discusses the current state of lung cancer screening, including the results of the National Lung Cancer Screening Trial, the consideration of implementing computed tomography screening, and a brief overview of the role of bronchoscopy in early detection and potential biomarkers that may aid in the early diagnosis of lung cancer.

Reliable categorisation of visual scoring of coronary artery calcification on low-dose CT for lung cancer screening: validation with the standard Agatston score

OBJECTIVES

To validate the reliability of the visual coronary artery calcification score (VCACS) on low-dose CT (LDCT) for concurrent screening of CAC and lung cancer.

METHODS

We enrolled 401 subjects receiving LDCT for lung cancer screening and ECG-gated CT for the Agatston score (AS). LDCT was reconstructed with 3- and 5-mm slice thickness (LDCT-3mm and LDCT-5mm respectively) for VCACS to obtain VCACS-3mm and VCACS-5mm respectively. After a training session comprising 32 cases, two observers performed four-scale VCACS (absent, mild, moderate, severe) of 369 data sets independently, the results were compared with four-scale AS (0, 1-100, 101-400, >400).

RESULTS

CACs were present in 39.6 % (146/369) of subjects. The sensitivity of VCACS-3mm was higher than for VCACS-5mm (83.6 % versus 74.0 %). The median of AS of the 24 false-negative cases in VCACS-3mm was 2.3 (range 1.1-21.1). The false-negative rate for detecting AS ≥ 10 on LDCT-3mm was 1.9 %. VCACS-3mm had higher concordance with AS than VCACS-5mm (k = 0.813 versus k = 0.685). An extended test of VCACS-3mm for four junior observers showed high inter-observer reliability (intra-class correlation = 0.90) and good concordance with AS (k = 0.662-0.747).

CONCLUSIONS

This study validated the reliability of VCACS on LDCT for lung cancer screening and showed that LDCT-3mm was more feasible than LDCT-5mm for CAD risk stratification.

KEY POINTS

• Low-dose computed tomography (LDCT) rarely misses significant coronary artery calcification (CAC). • Visual scoring of CAC on LDCT is highly concordant with Agatston scoring. • LDCT-3mm is more feasible than LDCT-5mm for CAD risk stratification. • CAC assessment enriched the screening information for LDCT lung cancer screening.

Early lung cancer detection using the self-evaluation scoring questionnaire and chest digital radiography: a 3-year follow-up study in China

The standard definition of high-risk individuals for lung cancer was not uniform and the value of chest digital radiography (DR) in lung cancer screening was still unproven. The aim of this study was to assess whether the original questionnaire named as "Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer" combined with DR examinations could detect early stage of lung cancer effectively. The Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer had been designed in previous studies. Subjects with scores over 116 points were regarded as high-risk individuals and underwent the current DR scans at least once a year from 2007 to 2009. Noncalcified nodules with a diameter over 30 mm, along with enlarged pulmonary hilus and atelectasis, were considered to be positive and subjected to further special examinations. Efficacy of the scoring questionnaire combined with DR scans was estimated by 3-year results. Among 1,537 subjects, 13, 11, and 7 were diagnosed with lung cancer in the first, second, and third year, respectively, indicating the detection rate of 2.02 % (31/1,537). In addition, 77.42 % (24/31) of the patients were in stage I and 51.61 % (16/31) were adenocarcinomas. For the 31 cases, 28 were defined as detected cancers, while the other three were interval ones, only accounting for 0.20 % (3/1,504) of individuals with negative judgments. The protocol of Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer combined with DR scans is a cost-effective and safe approach to detect early stage of lung cancer.

Lung cancer in HIV Infection

Lung cancer is the most prevalent non-AIDS-defining malignancy in the highly active antiretroviral therapy era. Smoking plays a significant role in the development of HIV-associated lung cancer, but the cancer risk is two to four times greater in HIV-infected persons than in the general population, even after adjusting for smoking intensity and duration. Lung cancer is typically diagnosed a decade or more earlier among HIV-infected persons (mean age, 46 years) compared to those without HIV infection. Adenocarcinoma is the most common histological subtype, and the majority of patients are diagnosed with locally advanced or metastatic carcinoma. Because pulmonary infections are common among HIV-infected individuals, clinicians may not suspect lung cancer in this younger patient population. Surgery with curative intent remains the treatment of choice for early-stage disease. Although there is increasing experience in using radiation and chemotherapy for HIV-infected patients who do not have surgical options, there is a need for prospective studies because this population is frequently excluded from participating in cancer trials. Evidence-based treatments for smoking-cessation with demonstrated efficacy in the general population must be routinely incorporated into the care of HIV-positive smokers.

Systematic reviews and meta-analysis of published studies: an overview and best practices

Systematic reviews and meta-analytic approaches are widely used in the clinical arena to integrate outcome data from published studies in a patient population that address a set of related research hypotheses. The credibility of this line of research is dependent on how the studies are chosen, how the data are assembled, and how the results are reported. In this brief report, we provide an overview of the minimum set of reporting requirements for systematic reviews and meta-analyses based on the Preferred Reporting Items of Systematic reviews and Meta-Analyses guidelines. As with any research, following a set of established guidelines is essential for quality and consistency of the findings across studies and for assessment of clinical utility.

Cutaneous metastasis as the initial manifestation of lung adenocarcinoma

We report the case of a 58-year-old male patient who was referred for oncology consultation due to an epigastric mass that had been growing rapidly for three months. Diagnostic investigation revealed that the mass was a metastasis of stage IV lung adenocarcinoma. The patient received five cycles of chemotherapy with cisplatin and gemcitabine as a first-line treatment, which was interrupted due to major adverse events. Although the pulmonary disease stabilized, the cutaneous disease progressed. The patient then received pemetrexed as a second-line chemotherapy, together with concurrent external radiotherapy, which was well tolerated. There was complete remission of the epigastric mass. However, the patient died three months after the treatment. Here, we emphasize the importance of a multidisciplinary approach and of its role in individualizing the treatment.

In vitro effect on cancer cells: synthesis and preparation of polyurethane membranes for controlled delivery of curcumin

Urethane polymers (PU) have been prepared from low-molecular weight polylactic acid (PLA) and hexamethylene diisocyanate (HMDI) using polydimethylsiloxane (PDMS) as a chain extender. These formed the supporting polymeric matrix of curcumin-containing PU membranes which were prepared using a solvent evaporation technique. FTIR and XRD data indicated the molecular-level dispersion and random distribution of curcumin in the polymer matrix, and data were consistent with observations from tensile-strength measurements and from AFM imaging. Determination of water vapor permeability and moisture uptake measurements have indicated that the PU membrane were appropriate for use on human skin. Skin permeation studies of curcumin were consistent with zero order (R² = 0.9874) and with Korsmeyer-Peppas (R² = 0.9978) kinetics-analytical data pointed to permeation by a combination of diffusion and erosion processes, with the latter dominating. The biocompatibility of these PU membranes was indicated by in vitro cytotoxicity studies using 3T3-L1-murine fibroblast cell. The in vitro therapeutic potential of the patches was demonstrated against A549 human lung cancer cells.

European and North American lung cancer screening experience and implications for pulmonary nodule management

The potential for low dose computed tomography (LDCT) to act as an effective tool in screening for lung cancer is currently the subject of several randomised control trials. It has recently been given prominence by interim results released by the North American National Lung Screening Trial (NLST). Several other trials assessing LDCT as a screening tool are currently underway in Europe, and are due to report their final results in the next few years. These include the NELSON, DLSCT, DANTE, ITALUNG, MILD and LUSI trials. Although slow to instigate a trial of its own, the UK Lung Screen (UKLS) trial will shortly commence. The knowledge gained from the newer trials has mostly reinforced and refined previous concepts that have formed the basis of existing nodule management guidelines. This article takes the opportunity to summarise the main aspects and initial results of the trials presently underway, assess the status of current collaborative efforts and the scope for future collaboration, and analyse observations from these studies that may usefully inform the management of the indeterminate pulmonary nodule. Key Points • Low dose CT screening for lung cancer is promising. • The effect of LDCT screening on mortality is still uncertain. • Several European randomised controlled trials for LDCT are underway. • The trials vary in methodology but most compare LDCT to no screening. • Preliminary results have reinforced existing nodule management concepts.

Screening and early detection of lung cancer

Lung cancer with an estimated 342,000 deaths in 2008 (20% of total) is the most common cause of death from cancer, followed by colorectal cancer (12%), breast cancer (8%), and stomach cancer (7%) in Europe. In former smokers, the absolute lung cancer risk remains higher than in never-smokers; these data therefore call for effective secondary preventive measures for lung cancer in addition to smoking cessation programs. This review presents and discusses the most recent advances in the early detection and screening of lung cancer.An overview of randomized controlled computerized tomography-screening trials is given, and the role of bronchoscopy and new techniques is discussed. Finally, the approach of (noninvasive) biomarker testing in the blood, exhaled breath, sputum, and bronchoscopic specimen is reviewed.