Screening for lung cancer

Exploring the Role of Vitamin D, Vitamin D-Dependent Proteins, and Vitamin D Receptor Gene Variation in Lung Cancer Risk

Lung cancer has an unfavorable prognosis with a rate of low overall survival, caused by the difficulty of diagnosis in the early stages and resistance to therapy. In recent years, there have been new therapies that use specific molecular targets and are effective in increasing the survival chances of advanced cancer. Therefore, it is necessary to find more specific biomarkers that can identify early changes in carcinogenesis and allow the earliest possible treatment. Vitamin D (VD) plays an important role in immunity and carcinogenesis. Furthermore, the vitamin D receptor (VDR) regulates the expression of various genes involved in the physiological functions of the human organism. The genes encoding the VDR are extremely polymorphic and vary greatly between human populations. To date, there are significant associations between VDR polymorphism and several types of cancer, but the data on the involvement of VDR polymorphism in lung cancer are still conflicting. Therefore, in this review, our aim was to investigate the relationship between VDR single-nucleotide polymorphisms in humans and the degree of risk for developing lung cancer. The studies showcased different gene polymorphisms to be associated with an increased risk of lung cancer: TaqI, ApaI, BsmI, FokI, and Cdx2. In addition, there is a strong positive correlation between VD deficiency and lung cancer development. Still, due to a lack of awareness, the assessment of VD status and VDR polymorphism is rarely considered for the prediction of lung cancer evolution and their clinical applicability, despite the fact that studies have shown the highest risk for lung cancer given by TaqI gene polymorphisms and that VDR polymorphisms are associated with more aggressive cancer evolution.

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks

Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer.

High SNHG expression may predict a poor lung cancer prognosis based on a meta-analysis

BACKGROUND

An increasing number of small nucleolar RNA host genes (SNHGs) have been revealed to be dysregulated in lung cancer tissues, and abnormal expression of SNHGs is significantly correlated with the prognosis of lung cancer. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of lung cancer.

METHODS

A comprehensive search of six related databases was conducted to obtain relevant literature. Relevant information, such as overall survival (OS), progression-free survival (PFS), TNM stage, lymph node metastasis (LNM), and tumor size, was extracted. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled to evaluate the relationship between SNHG expression and the survival outcome of lung cancers. Sensitivity and publication bias analyses were performed to explore the stability and reliability of the overall results.

RESULTS

Forty publications involving 2205 lung cancer patients were included in this meta-analysis. The pooled HR and 95% CI values indicated a significant positive association between high SNHG expression and poor OS (HR: 1.890, 95% CI: 1.595-2.185), disease-free survival (DFS) (HR: 2.31, 95% CI: 1.57-3.39) and progression-free survival (PFS) (HR: 2.01, 95% CI: 0.66-6.07). The pooled odds ratio (OR) and 95% CI values indicated that increased SNHG expression may be correlated with advanced TNM stage (OR: 1.509, 95% CI: 1.267-1.799), increase risk of distant lymph node metastasis (OR: 1.540, 95% CI: 1.298-1.828), and large tumor size (OR: 1.509, 95% CI: 1.245-1.829). Sensitivity analysis and publication bias results showed that each result had strong reliability and robustness, and there was no significant publication bias or other bias.

CONCLUSION

Most SNHGs are upregulated in lung cancer tissues, and high expression of SNHGs predicts poor survival outcomes in lung cancer. SNHGs may be potential prognostic markers and promising therapeutic targets.

[Early detection of lung cancer - current status and implementation scenarios]

The prognosis of conventionally diagnosed lung cancer patients is still rather poor. Two large, randomized trials using screening by low dose CT could demonstrate that early detection in persons with smoking as risk factor can improve this prognosis. Early detection of lung cancer can be achieved by structured screening programs using low dose CT for persons at increased risk, but in addition also by consequent management of incidental pulmonary nodules, which are seen on imaging for other reasons. Integral part of these programs should be prevention measures, especially a consequent, repeated, low-threshold offer of a service for smoking cessation. Programs for lung cancer screening for persons at increased risk are only beneficial for the screenees and cost-effective, if the various parts of the program are optimally integrated and coordinated and all necessary disciplines (especially respiratory medicine, radiology, pathology, thoracic surgery, radiotherapy) are included in a multidisciplinary manner. For Germany the certified lung cancer centres in structured cooperation with physicians in private practice (respiratory physicians, radiologists, general practitioners) would be a good option. It is essential that there is a good perception for the need of early detection of lung cancer in politics and the public and that the persons at risk are reached, contacted and motivated by various methods.

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

BACKGROUND

In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis.

METHODS

We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value.

RESULTS

Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies.

CONCLUSIONS

The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient's probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

Lung Ultrasound and High-Resolution Computed Tomography in Suspected COVID-19 Patients Admitted to the Emergency Department: A Comparison

Objective

To analyze the diagnostic accuracy of lung ultrasonography (LUS) and high-resolution computed tomography (HRCT), to detect COVID-19.

Materials and Methods

This study recruited all patients admitted to the emergency medicine unit, due to a suspected COVID-19 infection, during the first wave of the COVID-19 pandemic. These patients also who underwent a standardized LUS examination and a chest HRCT. The signs detected by both LUS and HRCT were reported, as well as the sensitivity, specificity, positive predictive value, and negative predictive value for LUS and HRCT.

Results

This cohort included 159 patients, 101 (63%) were diagnosed with COVID-19. COVID-19 patients showed more often confluent subpleural consolidations and parenchymal consolidations in lower lung regions of LUS. They also had "ground glass" opacities and "crazy paving" on HRCT, while pleural effusion and pulmonary consolidations were more common in non-COVID-19 patients. LUS had a sensitivity of 0.97 (95% CI 0.92-0.99) and a specificity of 0.24 (95% CI 0.07-0.5) for COVID-19 lung infections. HRCT abnormalities resulted in a 0.98 sensitivity (95% CI 0.92-0.99) and 0.1 specificity (95% CI 0.04-0.23) for COVID-19 lung infections.

Conclusion

In this cohort, LUS proved to be a noninvasive, diagnostic tool with high sensitivity for lung abnormalities that were likewise detected by HRCT. Furthermore, LUS, despite its lower specificity, has a high sensitivity for COVID-19, which could prove to be as effective as HRCT in excluding a COVID-19 lung infection.

Serum and Bronchoalveolar Lavage Fluid Levels of Cytokines in Patients with Lung Cancer and Chronic Lung Disease: A Prospective Comparative Study

The role of chronic inflammation in the initiation and progression of carcinogenesis has been well-established in previous studies, particularly in the stages of malignant conversion, invasion, and metastasis. This study aimed to explore the potential correlation between the levels of cytokines in serum and bronchoalveolar lavage fluid (BALF) by comparing their levels between patients with lung cancer and those with benign lung diseases. The study measured the concentration of IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10, and IL-12p70, in venous blood and BALF of a total of 33 patients with lung cancer and 33 patients with benign lung diseases. Significant differences were found between the two groups in various clinical parameters. The cytokine levels were significantly higher among patients with malignant disease, while the BALF analysis revealed higher cytokine levels compared with serum analysis. It was discovered that the levels of cancer-specific cytokines in the lavage fluid increased significantly sooner and were present at a greater concentration than those in the peripheral blood. After one month of treatment, the serum markers decreased significantly but slower in the lavage fluid. The differences between serum and BALF markers remained significant. It was observed that the highest correlation was among IL-6 (serum) and IL-6 (lavage), with a coefficient of 0.774 (p-value < 0.001), and IL-1 (serum) and IL-1β (lavage), with a coefficient value of 0.610 (p-value < 0.001). Other significant correlations among serum and lavage cytokines were observed between IL-6 (lavage) and IL-1 (serum) (rho = 0.631, p-value < 0.001) and CRP (rho = 0.428, p-value = 0.001), respectively. This study revealed significant differences and correlations in clinical parameters, serum markers, and BALF inflammatory markers between patients with lung cancer and those with benign lung pathologies. The results highlight the importance of understanding the inflammatory profiles of these conditions and could contribute to the development of targeted therapies or diagnostic approaches in the future. Further research is needed to validate these findings, explore their implications for clinical practice, and determine the diagnostic and prognostic value of these cytokines for lung cancer.

Thoracic ultrasound combined with low-dose computed tomography may represent useful screening strategy in highly exposed population in the industrial city of Taranto (Italy)

Objectives

We validated a screening protocol in which thoracic ultrasound (TUS) acts as a first-line complementary imaging technique in selecting patients which may deserve a second-line low-dose high resolution computed tomography (HRCT) scan among a population of asymptomatic high-risk subjects for interstitial lung abnormalities (ILA) and lung cancer. Due to heavy environmental pollution burden, the district Tamburi of Taranto has been chosen as "case study" for this purpose.

Methods

From July 2018 to October 2020, 677 patients aged between 45 and 65 year and who had been living in the Tamburi district of Taranto for at least 10 years were included in the study. After demographic, clinical and risk factor exposition data were collected, each participant underwent a complete TUS examination. These subjects were then asked to know if they agreed to perform a second-level examination by low-dose HRCT scan.

Results

On a total of 167 subjects (24.7%) who agreed to undergo a second-level HRCT, 85 patients (50.9%) actually showed pleuro-pulmonary abnormalities. Interstitial abnormalities were detected in a total of 36 patients on HRCT scan. In particular, 34 participants presented subpleural ILAs, that were classified in the fibrotic subtype in 7 cases. The remaining 2 patients showed non-subpleural interstitial abnormalities. Subpleural nodules were observed in 46 patients. TUS showed an overall diagnostic accuracy of 88.6% in detecting pleuro-pulmonary abnormalities in comparison with HRCT scan, with a sensitivity of 95.3%, a specificity of 81.7%, a positive predictive value of 84.4% and a negative predictive value of 94.4%. The matched evaluation of specific pulmonary abnormalities on HRTC scan (i.e., interstitial abnormalities or pulmonary nodules) with determinate sonographic findings revealed a reduction in both TUS sensibility and specificity. Focusing TUS evaluation on the assessment of interstitial abnormalities, a thickened pleural line showed a sensitivity of 63.9% and a specificity of 69.5%, hypoechoic striae showed a sensitivity of 38.9% and a specificity of 90.1% and subpleural nodules showed a sensitivity of 58.3% and a specificity of 77.1%. Regarding to the assessment of subpleural nodules, TUS showed a sensitivity of 60.9% and a specificity of 81.0%. However, the combined employment of TUS examination and HRCT scans allowed to identify 34 patients with early subpleural ILA and to detect three suspicious pulmonary nodules (of which two were intraparenchymal and one was a large subpleural mass), which revealed to be lung cancers on further investigations.

Conclusion

A first-line TUS examination might aid the identification of subjects highly exposed to environmental pollution, who could benefit of a second-line low-dose HRCT scan to find early interstitial lung diseases as well as lung cancer.

Protocol registration code

PLEURO-SCREENING-V1.0_15 Feb, 17.

Lung hamartoma resembling lung cancer: a report of three cases

Pulmonary hamartoma is a benign lung tumor. However, it is difficult to distinguish this lesion from other diseases via imaging. Three patients with pathologically confirmed pulmonary hamartoma in our department were analyzed. We believe it is necessary to combine imaging, pathology, clinical testing, and individual patient assessments to enable an earlier and more definitive diagnosis of pulmonary hamartoma. Therefore, it is necessary to analyze and summarize the clinical manifestations and imaging features of patients with pulmonary hamartoma to improve the early recognition of the disease by clinicians.

LINC02389/miR-7-5p Regulated Cisplatin Resistance of Non-Small-Cell Lung Cancer via Promoting Oxidative Stress

Background

Non-small-cell lung cancer (NSCLC) is one of the most common malignancies worldwide, and cisplatin-based chemotherapy is the main treatment for NSCLC. However, cisplatin resistance of NSCLC cells is a major challenge for NSCLC treatment.

Materials and Methods

qRT-PCR and Western blot were performed to detect the expression of LINC02389 and miR-7-5p in NSCLC tissues and cell lines. Cell counting kit-8 (CCK-8) assay and flow cytometry assay were applied to exam cell proliferation and apoptosis rate of NSCLC cells. The interaction between LINC02389 and miR-7-5p was verified by dual luciferase reporter gene assay, RNA pull-down assay, and RNA immunoprecipitation (RIP) assay. Additionally, cisplatin-resistant NSCLC cells were generated to assess the biological function of LINC02389 and miR-7-5p in cisplatin resistance of NSCLC.

Results

LINC02389 was highly expressed in NSCLC tissues and was correlated with poor prognosis of NSCLC patients. Knockdown of LINC02389 inhibited cell proliferation and promoted cell apoptosis of NSCLC, whereas miR-7-5p knockdown exerted the opposite effects. Moreover, LINC02389 negatively regulated the expression of miR-7-5p. In addition, LINC02389 was overexpressed, yet miR-7-5p was downregulated in cisplatin-resistant NSCLC cells compared with their parental cells. Moreover, oxidative stress biomarkers were overexpressed in cisplatin-resistant cells and were regulated by LINC02389. Besides, LINC02389 could reverse the inhibitory effect of cisplatin on NSCLC cells, which was partially reversed by attenuating the expression of miR-7-5p.

Conclusion

Our research firstly demonstrated that lncRNA LINC02389 acted as an oncogene to promote progression, oxidative stress, and cisplatin resistance through sponging miR-7-5p and may provide therapeutic targets for NSCLC.

Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method

We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50-0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.

Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study

BACKGROUND

We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors.

METHODS

Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers' assessments were calculated.

RESULTS

In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader's sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14-1.30), 1.00 (1.00-1.01), 1.03 (1.02-1.04), 1.07 (1.03-1.11), and 1.02 (1.01-1.03) by using the CAD, respectively.

CONCLUSION

The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer

Lung cancer remains the leading cause of cancer deaths worldwide. Although low-dose spiral computed tomography (LDCT) screening is used for the detection of lung cancer in a high-risk population, false-positive results of LDCT remain a clinical problem. Here, we developed a blood test of a novel panel of three established lung cancer methylation biomarkers for lung cancer detection. Short stature homeobox 2 gene (SHOX2), ras association domain family 1A gene (RASSF1A), and prostaglandin E receptor 4 gene (PTGER4) methylation was analyzed in a training cohort of 351 individuals (197 controls, 154 cases) and validated from an independent cohort of 149 subjects (89 controls, 60 cases). The novel panel biomarkers distinguished between malignant and benign lung disease at high sensitivity and specificity: 87.0% sensitivity [95% CI 80.2–91.5%], 98.0% specificity [95% CI 94.9–99.4%]. Sensitivity in adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and other lung cancer was 89.0%, 87.5%, 85.7%, and 77.8%, respectively. Notably, cancer patients in stage I and II showed high diagnostic sensitivity at 82.5% and 90.5%, respectively. Moreover, the diagnostic efficiency did not show bias toward age, gender, smoking, and the presence of other (nonlung) cancers. The performance of the panel in the validation cohort confirmed the diagnostic value. These findings clearly showed that this panel of DNA methylation biomarkers was effective in detecting lung cancer noninvasively and may provide clinical utility in stand-alone or in combination with current imaging techniques to improve the diagnosis of lung cancer.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.

Protocol and Rationale for the International Lung Screening Trial

Rationale: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCO_m2012 (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.

Objectives: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: 1) Compare the accuracy of the PLCO_m2012 model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and 2) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.

Methods: ILST will recruit 4,500 participants who meet USPSTF and/or PLCO_m2012 risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.

Conclusions: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.

Clinical trial registered with www.clinicaltrials.gov (NCT02871856).

EGFR-specific single-chain variable fragment antibody-conjugated Fe3O4/Au nanoparticles as an active MRI contrast agent for NSCLC

Overexpression of epidermal growth factor receptor (EGFR) is closely associated with a poor prognosis in non-small cell lung cancer (NSCLC), thus making it a promising biomarker for NSCLC diagnosis. Here, we conjugated a single-chain antibody (scFv) targeting EGFR with Fe₃O₄/Au nanoparticles to form an EGFR-specific molecular MRI bioprobe (scFv@Fe₃O₄/Au) to better detect EGFR-positive NSCLC tumors in vivo. In vitro, we demonstrated that the EGFR-specific scFv could specifically deliver Fe₃O₄/Au to EGFR-positive NSCLC cells. In vivo experiments showed that the accumulation of scFv@Fe₃O₄/Au in tumor tissue was detectable by magnetic resonance imaging (MRI) at the indicated time points after systemic injection. The T2W signal-to-noise ratio (SNR) of EGFR-positive SPC-A1 tumors was significantly decreased after scFv@Fe₃O₄/Au injection, which was not observed in the tumors of mice injected with BSA@Fe₃O₄/Au. Furthermore, transmission electron microscopy (TEM) analysis showed the specific localization of scFv@Fe₃O₄/Au in the SPC-A1 tumor cell cytoplasm. Collectively, the results of our study demonstrated that scFv@Fe₃O₄/Au might be a useful probe for the noninvasive diagnosis of EGFP-positive NSCLC.

Radiomics: an overview in lung cancer management-a narrative review

Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.

Early diagnosis and screening in lung cancer

Lung cancer is the third most diagnosed cancer, but the first cause of cancer-related deaths worldwide. This rather high death rate is due mainly to the fact that most patients are diagnosed with advanced-stage cancer, for which the conventional treatment does not work. The most used screening method for lung cancer is a low-dose CT scan, but it is recommended for specific age populations and it also started different debates on its advantages for lung cancer diagnosis. Over the year, several new techniques have been developed that are less invasive, have lower side effect, and can be implemented at all types of populations. This article aimed to present the advantages and disadvantages of using several methods for lung cancer diagnosis, including analysis of volatile organic compounds, exhaled breath condensate analysis and specific genomic approaches.

Psychosocial consequences of false positives in the Danish Lung Cancer CT Screening Trial: a nested matched cohort study

OBJECTIVES

Lung cancer CT screening can reduce lung cancer mortality, but high false-positive rates may cause adverse psychosocial consequences. The aim was to analyse the psychosocial consequences of false-positive lung cancer CT screening using the lung cancer screening-specific questionnaire, Consequences of Screening in Lung Cancer (COS-LC).

DESIGN AND SETTING

This study was a matched cohort study, nested in the randomised Danish Lung Cancer Screening Trial (DLCST).

PARTICIPANTS

Our study included all 130 participants in the DLCST with positive CT results in screening rounds 2-5, who had completed the COS-LC questionnaire. Participants were split into a true-positive and a false-positive group and were then matched 1:2 with a control group (n=248) on sex, age (±3 years) and the time of screening for the positive CT groups or clinic visit for the control group. The true positives and false positives were also matched 1:2 with participants with negative CT screening results (n=252).

PRIMARY OUTCOMES

Primary outcomes were psychosocial consequences measured at five time points.

RESULTS

False positives experienced significantly more negative psychosocial consequences in seven outcomes at 1 week and in three outcomes at 1 month compared with the control group and the true-negative group (mean ∆ score >0 and p<0.001). True positives experienced significantly more negative psychosocial consequences in one outcome at 1 week (mean ∆ score 2.86 (95% CI 1.01 to 4.70), p=0.0024) and in five outcomes at 1 month (mean ∆ score >0 and p<0.004) compared with the true-negative group and the control group. No long-term psychosocial consequences were identified either in false positives or true positives.

CONCLUSIONS

Receiving a false-positive result in lung cancer screening was associated with negative short-term psychosocial consequences. These findings contribute to the evidence on harms of screening and should be taken into account when considering implementation of lung cancer screening programmes.

TRIAL REGISTRATION NUMBER

NCT00496977.

Accuracy of a 3-Dimensionally Printed Navigational Template for Localizing Small Pulmonary Nodules: A Noninferiority Randomized Clinical Trial

Importance

Localization of small lung nodules are challenging because of the difficulty of nodule recognition during video-assisted thoracoscopic surgery. Using 3-dimensional (3-D) printing technology, a navigational template was recently created to assist percutaneous lung nodule localization; however, the efficacy and safety of this template have not yet been evaluated.

Objective

To assess the noninferiority of the efficacy and safety of a 3-D-printed navigational template guide for localizing small peripheral lung nodules.

Design, Setting, and Participants

This noninferiority randomized clinical trial conducted between October 2016 and October 2017 at Shanghai Pulmonary Hospital, Shanghai, China, compared the safety and precision of lung nodule localization using a template-guided approach vs the conventional computed tomography (CT)-guided approach. In total, 213 surgical candidates with small peripheral lung nodules (<2 cm) were recruited to undergo either CT- or template-guided lung nodule localization. An intention-to-treat analysis was conducted.

Interventions

Percutaneous lung nodule localization.

Main Outcomes and Measures

The primary outcome was the accuracy of lung nodule localization (localizer deviation), and secondary outcomes were procedural duration, radiation dosage, and complication rate.

Results

Of the 200 patients randomized at a ratio of 1:1 to the template- and CT-guided groups, most were women (147 vs 53), body mass index ranged from 15.4 to 37.3, the mean (SD) nodule size was 9.7 (2.9) mm, and the mean distance between the outer edge of target nodule and the pleura was 7.8 (range, 0.0-43.9) mm. In total, 190 patients underwent either CT- or template-guided lung nodule localization and subsequent surgery. Among these patients, localizer deviation did not significantly differ between the template- and CT-guided groups (mean [SD], 8.7 [6.9] vs 9.6 [5.8] mm; P = .36). The mean (SD) procedural durations were 7.4 (3.2) minutes for the template-guided group and 9.5 (3.6) minutes for the CT-guided group (P < .001). The mean (SD) radiation dose was 229 (65) mGy × cm in the template-guided group and 313 (84) mGy × cm in CT-guided group (P < .001).

Conclusions and Relevance

The use of the 3-D-printed navigational template for localization of small peripheral lung nodules showed efficacy and safety that were not substantially worse than those for the CT-guided approach while significantly simplifying the localization procedure and decreasing patient radiation exposure.

Trial Registration

ClinicalTrials.gov identifier: NCT02952261.

Direct and indirect healthcare costs of lung cancer CT screening in Denmark: a registry study

INTRODUCTION

A study based on the Danish Randomised Controlled Lung Cancer Screening Trial (DLCST) calculated the healthcare costs of lung cancer screening by comparing costs in an intervention group with a control group. Participants in both groups, however, experienced significantly increased negative psychosocial consequences after randomisation. Substantial participation bias has also been documented: The DLCST participants reported fewer negative psychosocial aspects and experienced better living conditions compared with the random sample.

OBJECTIVE

To comprehensively analyse the costs of lung cancer CT screening and to determine whether invitations to mass screening alter the utilisation of the healthcare system resulting in indirect costs. Healthcare utilisation and costs are analysed in the primary care sector (general practitioner psychologists, physiotherapists, other specialists, drugs) and the secondary care sector (emergency room contacts, outpatient visits, hospitalisation days, surgical procedures and non-surgical procedures).

DESIGN

To account for bias in the original trial, the costs and utilisation of healthcare by participants in DLCST were compared with a new reference group, selected in the period from randomisation (2004-2006) until 2014.

SETTING

Four Danish national registers.

PARTICIPANTS

DLCST included 4104 current or former heavy smokers, randomly assigned to the CT group or the control group. The new reference group comprised a random sample of 535 current or former heavy smokers in the general Danish population who were never invited to participate in a cancer screening test.

MAIN OUTCOME MEASURES

Total healthcare costs including costs and utilisation of healthcare in both the primary and the secondary care sector.

RESULTS

Compared with the reference group, the participants in both the CT group (offered annual CT screening, lung function test and smoking counselling) and the control group (offered annual lung function test and smoking counselling) had significantly increased total healthcare costs, calculated at 60% and 48% respectively. The increase in costs was caused by increased use of healthcare in both the primary and the secondary sectors.

CONCLUSION

CT screening leads to 60% increased total healthcare costs. Such increase would raise the expected annual healthcare cost per participant from EUR 2348 to EUR 3756. Cost analysis that only includes costs directly related to the CT scan and follow-up procedures most likely underestimates total costs. Our data show that the increased costs are not limited to the secondary sector.

TRIAL REGISTRATION NUMBER

NCT00496977.

Sequential screening for lung cancer in a high-risk group: randomised controlled trial: LungSEARCH: a randomised controlled trial of Surveillance using sputum and imaging for the EARly detection of lung Cancer in a High-risk group

BACKGROUND

Low-dose computed tomography (LDCT) screening detects early-stage lung cancer and reduces mortality. We proposed a sequential approach targeted to a high-risk group as a potentially efficient screening strategy.

METHODS

LungSEARCH was a national multicentre randomised trial. Current/ex-smokers with mild/moderate chronic obstructive pulmonary disease (COPD) were allocated (1:1) to have 5 years surveillance or not. Screened participants provided annual sputum samples for cytology and cytometry, and if abnormal were offered annual LDCT and autofluorescence bronchoscopy (AFB). Those with normal sputum provided annual samples. The primary end-point was the percentage of lung cancers diagnosed at stage I/II (nonsmall cell) or limited disease (small cell).

RESULTS

1568 participants were randomised during 2007-2011 from 10 UK centres. 85.2% of those screened provided an adequate baseline sputum sample. There were 42 lung cancers among 785 screened individuals and 36 lung cancers among 783 controls. 54.8% (23 out of 42) of screened individuals versus 45.2% (14 out of 31) of controls with known staging were diagnosed with early-stage disease (one-sided p=0.24). Relative risk was 1.21 (95% CI 0.75-1.95) or 0.82 (95% CI 0.52-1.31) for early-stage or advanced cancers, respectively. Overall sensitivity for sputum (in those randomised to surveillance) was low (40.5%) with a cumulative false-positive rate (FPR) of 32.8%. 55% of cancers had normal sputum results throughout. Among sputum-positive individuals who had AFB, sensitivity was 45.5% and cumulative FPR was 39.5%; the corresponding measures for those who had LDCT were 100% and 16.1%, respectively.

CONCLUSIONS

Our sequential strategy, using sputum cytology/cytometry to select high-risk individuals for AFB and LDCT, did not lead to a clear stage shift and did not improve the efficiency of lung cancer screening.

Autoantibodies as diagnostic biomarkers for lung cancer: A systematic review

Lung cancer (LC) accounts for the largest number of tumor-related deaths worldwide. As the overall 5-year survival rate of LC is associated with its stages at detection, development of a cost-effective and noninvasive cancer screening method is necessary. We conducted a systematic review to evaluate the diagnostic values of single and panel tumor-associated autoantibodies (TAAbs) in patients with LC. This review included 52 articles with 64 single TAAbs and 19 with 20 panels of TAAbs. Enzyme-linked immunosorbent assays (ELISA) were the most common detection method. The sensitivities of single TAAbs for all stages of LC ranged from 3.1% to 92.9% (mean: 45.2%, median: 37.1%), specificities from 60.6% to 100% (mean: 88.1%, median: 94.9%), and AUCs from 0.416 to 0.990 (mean: 0.764, median: 0.785). The single TAAb with the most significant diagnostic value was the autoantibody against human epididymis secretory protein (HE4) with the maximum sensitivity 91% for NSCLC. The sensitivities of the panel of TAAbs ranged from 30% to 94.8% (mean: 76.7%, median: 82%), specificities from 73% to 100% (mean: 86.8%, median: 89.0%), and AUCs from 0.630 to 0.982 (mean: 0.821, median: 0.820), and the most significant AUC value in a panel (M13 Phage 908, 3148, 1011, 3052, 1000) was 0.982. The single TAAb with the most significant diagnostic calue for early stage LC, was the autoantibody against Wilms tumor protein 1 (WT1) with the maximum sensitivity of 90.3% for NSCLC and its sensitivity and specificity in a panel (T7 Phage 72, 91, 96, 252, 286, 290) were both above 90.0%. Single or TAAbs panels may be useful biomarkers for detecting LC patients at all stages or an early-stage in high-risk populations or health people, but the TAAbs panels showed higher detection performance than single TAAbs. The diagnostic value of the panel of six TAAbs, which is higher than the panel of seven TAAbs, may be used as potential biomarkers for the early detection of LC and can probably be used in combination with low-dose CT in the clinic.

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.

Circulating MicroRNA Biomarkers for Lung Cancer Detection in East Asian Populations

BACKGROUND

Lung cancer (LC) is the leading cause of cancer-related death in Eastern Asia. The prognosis of LC highly depends on tumor stages and early detection could substantially reduce LC mortality. Accumulating evidence suggested that circulating miRNAs in plasma or serum may have applications in early LC detection. We thus conducted a systematic literature review on the diagnostic value of miRNAs markers for LC in East Asian populations.

METHODS

PubMed and ISI Web of Knowledge were searched to retrieve relevant articles published up to 17 September 2018. Information on study design, population characteristics, investigated miRNAs and diagnostic accuracy (including sensitivity, specificity and area under the curve (AUC)) were independently extracted by two reviewers.

RESULTS

Overall, 46 studies that evaluated a total of 88 miRNA markers for LC diagnosis in East Asian populations were identified. Sixteen of the 46 studies have incorporated individual miRNA markers as panels (with 2⁻20 markers). Three promising miRNA panels with ≥90% sensitivity and ≥90% specificity were discovered, two of which were externally validated. Diagnostic performance of circulating miRNAs in East Asian populations was comparable to previously summarized performance in Western populations. Forty-four miRNAs were reported in both populations. No major differences in diagnostic performance by ethnicity of the same miRNA was observed.

CONCLUSIONS

Circulating miRNAs or miRNA panels, possibly in combination with other promising molecular markers including epigenetic and genetic markers, may be promising candidates for noninvasive LC early detection. However, large studies with samples collected prospectively in true screening settings are required to validate the promising markers or marker panels.

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Do we know enough about the effect of low-dose computed tomography screening for lung cancer on survival to act? A systematic review, meta-analysis and network meta-analysis of randomised controlled trials

BACKGROUND

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

METHODS

Our objective was to estimate the effect of LDCT lung cancer screening on mortality in high-risk populations. A systematic review of randomised controlled trials (RCTs) comparing LDCT screening programmes with usual care (no screening) or other imaging screening programme (such as chest X-ray (CXR)) was conducted. RCTs of CXR screening were additionally included in the network meta-analysis. Bibliographic sources including MEDLINE, Embase, Web of Science and the Cochrane Library were searched to January 2017. All key review steps were done by two persons. Quality assessment used the Cochrane Risk of Bias tool. Meta-analyses were performed.

RESULTS

Four RCTs were included. More will provide data in the future. Meta-analysis demonstrated that LDCT screening with up to 9.80 years of follow-up was associated with a statistically non-significant decrease in lung cancer mortality (pooled relative risk (RR) 0.94, 95% confidence interval (CI) 0.74 to 1.19; p = 0.62). There was a statistically non-significant increase in all-cause mortality. Given the considerable heterogeneity for both outcomes, the results should be treated with caution.Network meta-analysis including the four original RCTs plus two further RCTs assessed the relative effectiveness of LDCT, CXR and usual care. The results showed that in terms of lung cancer mortality reduction LDCT was ranked as the best screening strategy, CXR screening as the worst strategy and usual care intermediate.

CONCLUSIONS

LDCT screening may be effective in reducing lung cancer mortality but there is considerable uncertainty: the largest of the RCTs compared LDCT with CXR screening rather than no screening; there is imprecision of the estimates; and there is important heterogeneity between the included study results. The uncertainty about the effect on all-cause mortality is even greater. Maturing trials may resolve the uncertainty.

Is symptom-based diagnosis of lung cancer possible? A systematic review and meta-analysis of symptomatic lung cancer prior to diagnosis for comparison with real-time data from routine general practice

BACKGROUND

Lung cancer is a good example of the potential benefit of symptom-based diagnosis, as it is the commonest cancer worldwide, with the highest mortality from late diagnosis and poor symptom recognition. The diagnosis and risk assessment tools currently available have been shown to require further validation. In this study, we determine the symptoms associated with lung cancer prior to diagnosis and demonstrate that by separating prior risk based on factors such as smoking history and age, from presenting symptoms and combining them at the individual patient level, we can make greater use of this knowledge to create a practical framework for the symptomatic diagnosis of individual patients presenting in primary care.

AIM

To provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis.

DESIGN AND SETTING

Systematic review and meta-analysis of primary and secondary care data.

METHOD

Seven databases were searched (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, Web of Science, British Nursing Index and Cochrane Library). Thirteen studies were selected based on predetermined eligibility and quality criteria for diagnostic assessment to establish the value of symptom-based diagnosis using diagnosistic odds ratio (DOR) and summary receiver operating characteristic (SROC) curve. In addition, routinely collated real-time data from primary care electronic health records (EHR), TransHis, was analysed to compare with our findings.

RESULTS

Haemoptysis was found to have the greatest diagnostic value for lung cancer, diagnostic odds ratio (DOR) 6.39 (3.32-12.28), followed by dyspnoea 2.73 (1.54-4.85) then cough 2.64 (1.24-5.64) and lastly chest pain 2.02 (0.88-4.60). The use of symptom-based diagnosis to accurately diagnose lung cancer cases from non-cases was determined using the summary receiver operating characteristic (SROC) curve, the area under the curve (AUC) was consistently above 0.6 for each of the symptoms described, indicating reasonable discriminatory power. The positive predictive value (PPV) of diagnostic symptoms depends on an individual's prior risk of lung cancer, as well as their presenting symptom pattern. For at risk individuals we calculated prior risk using validated epidemiological models for risk factors such as age and smoking history, then combined with the calculated likelihood ratios for each symptom to establish posterior risk or positive predictive value (PPV).

CONCLUSION

Our findings show that there is diagnostic value in the clinical symptoms associated with lung cancer and the potential benefit of characterising these symptoms using routine data studies to identify high-risk patients.

Circulating microRNA biomarkers for lung cancer detection in Western populations

Lung cancer (LC) is a leading cause of cancer-related death in the Western world. Patients with LC usually have poor prognosis due to the difficulties in detecting tumors at early stages. Multiple studies have shown that circulating miRNAs might be promising biomarkers for early detection of LC. We aimed to provide an overview of published studies on circulating miRNA markers for early detection of LC and to summarize their diagnostic performance in Western populations. A systematic literature search was performed in PubMed and ISI Web of Knowledge to find relevant studies published up to 11 August 2017. Information on study design, population characteristics, miRNA markers, and diagnostic accuracy (including sensitivity, specificity, and AUC) were independently extracted by two reviewers. Overall, 17 studies evaluating 35 circulating miRNA markers and 19 miRNA panels in serum or plasma were included. The median sensitivity (range) and specificity (range) were, respectively, 78.4% (51.7%-100%) and 78.7% (42.9%-93.5%) for individual miRNAs, and 83.0% (64.0%-100%) and 84.9% (71.0%-100%) for miRNA panels. Most studies incorporated individual miRNA markers as panels (with 2-34 markers), with multiple miRNA-based panels generally outperforming individual markers. Two promising miRNA panels were discovered and verified in prospective cohorts. Of note, both studies exclusively applied miRNA ratios when building up panels. In conclusion, circulating miRNAs may bear potential for noninvasive LC screening, but large studies conducted in screening or longitudinal settings are needed to validate the promising results and optimize the marker panels.

Valuing Healthcare Improvement: Implicit Norms, Explicit Normativity, and Human Agency

I argue that greater attention to human agency and normativity in both researching and practicing service improvement may be one strategy for enhancing improvement science, illustrating with examples from cancer screening. Improvement science tends to deliberately avoid explicit normativity, for paradigmatically coherent reasons. But there are good reasons to consider including explicit normativity in thinking about improvement. Values and moral judgements are central to social life, so an adequate account of social life must include these elements. And improvement itself is unavoidably normative: it assumes that things could and should be better than they are. I seek to show that normativity will always be implicated in the creation of evidence, the design of programs, the practice of healthcare, and in citizens' judgements about that care, and to make a case that engaging with this normativity is worthwhile.

Clinical Usefulness of Human Epididymis Protein 4 in Lung Cancer

Human epididymis protein 4 (HE4) has been suggested as a useful new biomarker of lung cancer; however, few relevant large-scale studies have been published. In this study, we evaluated the utility of serum HE4 for lung cancer detection. HE4 levels were measured in serum samples from 100 lung cancer patients, 57 patients with benign lung diseases, and 274 healthy controls by using a chemiluminescent immunoassay, and variations in HE4 levels were analyzed by clinical status such as lung cancer, benign lung disease, and healthy condition, Tumor, Lymph Nodes, Metastasis (TNM) stage, tumor score, and histological cancer type. Lung cancer patients had significantly higher serum HE4 levels than patients with benign lung diseases and healthy controls (P<0.0001). The area under the ROC curve for HE4 was 0.84 (95% confidence interval, 0.78–0.89; P<0.0001) between lung cancer patients and healthy controls. Serum HE4 levels were significantly higher in patients with advanced disease (according to TNM stage) than in healthy controls (P<0.0001). HE4 levels were significantly elevated in patients with tumors of all types, those of different histological subgroups, and those with the smallest tumors (P=0.002). This report supports the potential of serum HE4 as an ancillary diagnostic marker for lung cancer detection.

Lung cancer screening with low-dose spiral computed tomography: evidence from a pooled analysis of two Italian randomized trials

The benefits and harms of lung cancer (LC) screening with low-dose computed tomography (LDCT) are debatable. Positive results from the US National Lung Screening Trial were not evident in the European trials, possibly due to their smaller sample sizes. To address this issue, we conducted a patient-level pooled analysis of two Italian randomized controlled trials. Data from DANTE and MILD trials were combined for a total of 3640 individuals in the LDCT arm and 2909 in the control arm. LC and overall mortality were analyzed using multivariate hazard ratios (HRs) and log-rank tests stratified by study. The median follow-up was 8.2 years, with a total of 30 480 person-years in the LDCT arm and 22 157 in the control arm. A total of 192 patients developed LC in the LDCT arm and 105 in the control arm. Half of the LC cases in the LDCT arm had stage IA or IB cancer, as compared with 21% in the control arm. Overall mortality rates/100 000 person-years were 925 in the LDCT arm and 1074 in the control arm, and LC mortality rates were 299 and 357, respectively. The multivariate pooled overall mortality HR was 0.89 (95% confidence interval: 0.74-1.06) and the LC mortality HR was 0.83 (95% confidence interval: 0.61-1.12) for the LDCT arm as compared with the control arm. The present pooled analysis shows a nonsignificant 11% reduction in overall mortality in individuals undergoing LDCT screening as compared with the control arm. A pooled analysis of all European trials would be a useful contribution to assess the real benefit of LDCT screening.

The fraction of cancer attributable to modifiable risk factors in England, Wales, Scotland, Northern Ireland, and the United Kingdom in 2015

BACKGROUND

Changing population-level exposure to modifiable risk factors is a key driver of changing cancer incidence. Understanding these changes is therefore vital when prioritising risk-reduction policies, in order to have the biggest impact on reducing cancer incidence. UK figures on the number of risk factor-attributable cancers are updated here to reflect changing behaviour as assessed in representative national surveys, and new epidemiological evidence. Figures are also presented by UK constituent country because prevalence of risk factor exposure varies between them.

METHODS

Population attributable fractions (PAFs) were calculated for combinations of risk factor and cancer type with sufficient/convincing evidence of a causal association. Relative risks (RRs) were drawn from meta-analyses of cohort studies where possible. Prevalence of exposure to risk factors was obtained from nationally representative population surveys. Cancer incidence data for 2015 were sourced from national data releases and, where needed, personal communications. PAF calculations were stratified by age, sex and risk factor exposure level and then combined to create summary PAFs by cancer type, sex and country.

RESULTS

Nearly four in ten (37.7%) cancer cases in 2015 in the UK were attributable to known risk factors. The proportion was around two percentage points higher in UK males (38.6%) than in UK females (36.8%). Comparing UK countries, the attributable proportion was highest in Scotland (41.5% for persons) and lowest in England (37.3% for persons). Tobacco smoking contributed by far the largest proportion of attributable cancer cases, followed by overweight/obesity, accounting for 15.1% and 6.3%, respectively, of all cases in the UK in 2015. For 10 cancer types, including two of the five most common cancer types in the UK (lung cancer and melanoma skin cancer), more than 70% of UK cancer cases were attributable to known risk factors.

CONCLUSION

Tobacco and overweight/obesity remain the top contributors of attributable cancer cases. Tobacco smoking has the highest PAF because it greatly increases cancer risk and has a large number of cancer types associated with it. Overweight/obesity has the second-highest PAF because it affects a high proportion of the UK population and is also linked with many cancer types. Public health policy may seek to mitigate the level of harm associated with exposure or reduce exposure levels-both approaches may effectively impact cancer incidence. Differences in PAFs between countries and sexes are primarily due to varying prevalence of exposure to risk factors and varying proportions of specific cancer types. This variation in turn is affected by socio-demographic differences which drive differences in exposure to theoretically avoidable 'lifestyle' factors. PAFs at UK country level have not been available previously and they should be used by policymakers in devolved nations. PAFs are estimates based on the best available data, limitations in those data would generally bias toward underestimation of PAFs. Regular collection of risk factor exposure prevalence data which corresponds with epidemiological evidence is vital for analyses like this and should remain a priority for the UK Government and devolved Administrations.

Necessity of organized low-dose computed tomography screening for lung cancer: From epidemiologic comparisons between China and the Western nations

OBJECTIVES

To compare the proportion of stage I lung cancer and population mortality in China to those in U.S. and Europe where lung cancer screening by low-dose computed tomography (LDCT) has been already well practiced.

METHODS

The proportions of stage I lung cancer in LDCT screening population in U.S. and Europe were retrieved from NLST and NELSON trials. The general proportion of stage I lung cancer in China was retrieved from a rapid meta-analysis, based on a literature search in the China National Knowledge Infrastructure database. The lung cancer mortality and prevalence of China, U.S. and Europe was retrieved from Globocan 2012 fact sheet. Mortality-to-prevalence ratio (MPR) was applied to compare the population survival outcome of lung cancer.

RESULTS

The estimated proportion of stage I lung cancer in China is merely 20.8% among hospital-based cross-sectional population, with relative ratios (RRs) being 2.40 (95% CI 2.18-2.65) and 2.98 (95% CI 2.62-3.38) compared by LDCT-screening population in U.S. and Europe trials, respectively. MPR of lung cancer is as high as 58.9% in China, with RRs being 0.46 (95% CI 0.31-0.67) and 0.58 (95% CI 0.39-0.85) compared by U.S. and Europe, respectively.

CONCLUSIONS

By the epidemiological inference, the LDCT mass screening might be associated with increasing stage I lung cancer and therefore improving population survival outcome. How to translate the experiences of lung cancer screening by LDCT from developed counties to China in a cost-effective manner needs to be further investigated.

Lung cancer: a brief review of epidemiology and screening

The global burden of lung cancer has been increasing over the past years, and is still a major threat to public health worldwide, leading to disabilities and premature mortality. Despite multifactorial cause, smoking remains as the major etiological factor, followed by occupational exposure to carcinogens, genetic predisposition and other concomitant diseases. In order to reduce the individual and social burden due to the direct and indirect costs related to the lung cancer treatment, accurate methods of screening are needed. Among those, x-ray with cytological analysis of sputum was first proposed. Nowadays, more sensitive methods such as low-dose computed tomography are being used to improve the early detection. In the future, molecular biomarkers may complement low-dose computed tomography and improve the robustness of early lung cancer detection.

Tobacco smoking and methylation of genes related to lung cancer development

Lung cancer is a leading cause of cancer-related mortality worldwide, and cigarette smoking is the major environmental hazard for its development. This study intended to examine whether smoking could alter methylation of genes at lung cancer risk loci identified by genome-wide association studies (GWASs). By systematic literature review, we selected 75 genomic candidate regions based on 120 single-nucleotide polymorphisms (SNPs). DNA methylation levels of 2854 corresponding cytosine-phosphate-guanine (CpG) candidates in whole blood samples were measured by the Illumina Infinium Human Methylation450 Beadchip array in two independent subsamples of the ESTHER study. After correction for multiple testing, we successfully confirmed associations with smoking for one previously identified CpG site within the KLF6 gene and identified 12 novel sites located in 7 genes: STK32A, TERT, MSH5, ACTA2, GATA3, VTI1A and CHRNA5 (FDR <0.05). Current smoking was linked to a 0.74% to 2.4% decrease of DNA methylation compared to never smoking in 11 loci, and all but one showed significant associations (FDR <0.05) with life-time cumulative smoking (pack-years). In conclusion, our study demonstrates the impact of tobacco smoking on DNA methylation of lung cancer related genes, which may indicate that lung cancer susceptibility genes might be regulated by methylation changes in response to smoking. Nevertheless, this mechanism warrants further exploration in future epigenetic and biomarker studies.

Physician's awareness of lung cancer screening and its related medical radiation exposure in Korea

OBJECTIVES

Through a survey on perception of lung cancer screening and accompanying medical radiation exposure in Korea, the present study was to investigate its current situations and evaluate various perception of physicians regarding it in order to propose measures for improvements.

METHODS

Medical specialists in national cancer screening institutions selected through stratified random sampling were subjected to face-to-face interview using a structured questionnaire. We investigated physicians' perception on effectiveness of lung cancer screening depending on screening modality, selection criteria for subjects of screening, types of equipment used to screen, and perception for seriousness of adverse effects following the test. In addition, odds ratios to underestimate risk of radiation exposure from screening were calculated through logistic regression analysis.

RESULTS

Each response that chest X-ray is effective for lung cancer screening and that smoking history is not considered prior to screening recommendation accounted for more than 60% of respondents, suggesting the chance of unnecessary screening tests. Regarding adverse effects of lung cancer screening, about 85% of respondents replied that false positive, radiation exposure, and overdiagnosis could be ignored. About 70% of respondents underestimated radiation dose from lung cancer screening, and a low proportion of physicians informed patients of radiation exposure risk.

CONCLUSIONS

It was found that most physicians underestimated harms of lung cancer screening including radiation exposure and were lack of awareness regarding lung cancer screening. It should be noted that physicians need to have proper perceptions about screening recommendation and accompanying possible harms, for successful implementation of the screening program.

Early Detection and Chemoprevention of Lung Cancer

Despite advances in targeted treatments, lung cancer remains a common and deadly malignancy, in part owing to its typical late presentation. Recent developments in lung cancer screening and ongoing efforts aimed at early detection, treatment, and prevention are promising areas to impact the mortality from lung cancer. In the past several years, lung cancer screening with low-dose chest computed tomography (CT) was shown to have mortality benefit, and lung cancer screening programs have been implemented in some clinical settings. Biomarkers for screening, diagnosis, and monitoring of response to therapy are under development. Prevention efforts aimed at smoking cessation are as crucial as ever, and there have been encouraging findings in recent clinical trials of lung cancer chemoprevention. Here we review advancements in the field of lung cancer prevention and early malignancy and discuss future directions that we believe will result in a reduction in the mortality from lung cancer.

Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): protocol of a prospective multicentre cohort study in France

INTRODUCTION

Lung cancer (LC) is the leading cause of death from cancer. Early diagnosis of LC is of paramount importance in terms of prognosis. The health authorities of most countries do not accept screening programmes based on low-dose chest CT (LDCT), especially in Europe, because they are flawed by a high rate of false-positive results, leading to a large number of invasive diagnostic procedures. These authorities advocated further research, including companion biological tests that could enhance the effectiveness of LC screening. The present project aims to validate early diagnosis of LC by detection and characterisation of circulating tumour cells (CTCs) in a peripheral blood sample taken from a prospective cohort of persons at high-risk of LC.

METHODS AND ANALYSIS

The AIR Project is a prospective, multicentre, double-blinded, cohort study conducted by a consortium of 21 French university centres. The primary objective is to determine the operational values of CTCs for the early detection of LC in a cohort of asymptomatic participants at high risk for LC, that is, smokers and ex-smokers (≥30 pack-years, quitted ≤15 years), aged ≥55 years, with chronic obstructive pulmonary disease (COPD). The study participants will undergo yearly screening rounds for 3 years plus a 1-year follow-up. Each round will include LDCT plus peripheral blood sampling for CTC detection. Assuming 5% prevalence of LC in the studied population and a 10% dropout rate, a total of at least 600 volunteers will be enrolled.

ETHICS AND DISSEMINATION

The study sponsor is the University Hospital of Nice. The study was approved for France by the ethical committee CPP Sud-Méditerranée V and the ANSM (Ministry of Health) in July 2015. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.

TRIAL REGISTRATION NUMBER

NCT02500693.

Lung Cancer Screening

Lung cancer is the leading cause of cancer death in the United States. More than 80% of these deaths are attributed to tobacco use, and primary prevention can effectively reduce the cancer burden. The National Lung Screening Trial showed that low-dose computed tomography (LDCT) screening could reduce lung cancer mortality in high-risk patients by 20% compared with chest radiography. The US Preventive Services Task Force recommends annual LDCT screening for persons aged 55 to 80 years with a 30-pack-year smoking history, either currently smoking or having quit within 15 years.