Lung cancer screening: Computed tomography or chest radiographs?

Revolutionizing lung health: Exploring the latest breakthroughs and future prospects of synbiotic nanostructures in lung diseases

The escalating prevalence of lung diseases underscores the need for innovative therapies. Dysbiosis in human body microbiome has emerged as a significant factor in these diseases, indicating a potential role for synbiotics in restoring microbial equilibrium. However, effective delivery of synbiotics to the target site remains challenging. Here, we aim to explore suitable nanoparticles for encapsulating synbiotics tailored for applications in lung diseases. Nanoencapsulation has emerged as a prominent strategy to address the delivery challenges of synbiotics in this context. Through a comprehensive review, we assess the potential of nanoparticles in facilitating synbiotic delivery and their structural adaptability for this purpose. Our review reveals that nanoparticles such as nanocellulose, starch, and chitosan exhibit high potential for synbiotic encapsulation. These offer flexibility in structure design and synthesis, making them promising candidates for addressing delivery challenges in lung diseases. Furthermore, our analysis highlights that synbiotics, when compared to probiotics alone, demonstrate superior anti-inflammatory, antioxidant, antibacterial and anticancer activities. This review underscores the promising role of nanoparticle-encapsulated synbiotics as a targeted and effective therapeutic approach for lung diseases, contributing valuable insights into the potential of nanomedicine in revolutionizing treatment strategies for respiratory conditions, ultimately paving the way for future advancements in this field.

Deep learning-based age estimation from chest CT scans

PURPOSE

Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age.

METHODS

To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age.

RESULTS

Our analysis revealed a mean absolute error of 1.84 years and a Pearson's correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65-2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age.

CONCLUSION

Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.

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.

Lung Cancer Screening in Asia: An Expert Consensus Report

INTRODUCTION

The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

METHOD

A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

RESULTS

Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

CONCLUSIONS

Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Clinical Guidance on the Monitoring and Management of Trastuzumab Deruxtecan (T-DXd)-Related Adverse Events: Insights from an Asia-Pacific Multidisciplinary Panel

Trastuzumab deruxtecan (T-DXd)-an antibody-drug conjugate targeting the human epidermal growth factor receptor 2 (HER2)-improved outcomes of patients with HER2-positive and HER2-low metastatic breast cancer. Guidance on monitoring and managing T-DXd-related adverse events (AEs) is an emerging unmet need as translating clinical trial experience into real-world practice may be difficult due to practical and cultural considerations and differences in health care infrastructure. Thus, 13 experts including oncologists, pulmonologists and a radiologist from the Asia-Pacific region gathered to provide recommendations for T-DXd-related AE monitoring and management by using the latest evidence from the DESTINY-Breast trials, our own clinical trial experience and loco-regional health care considerations. While subgroup analysis of Asian (excluding Japanese) versus overall population in the DESTINY-Breast03 uncovered no major differences in the AE profile, we concluded that proactive monitoring and management are essential in maximising the benefits with T-DXd. As interstitial lung disease (ILD)/pneumonitis is a serious AE, patients should undergo regular computed tomography scans, but the frequency may have to account for the median time of ILD/pneumonitis onset and access. Trastuzumab deruxtecan appears to be a highly emetic regimen, and prophylaxis with serotonin receptor antagonists and dexamethasone (with or without neurokinin-1 receptor antagonist) should be considered. Health care professionals should be vigilant for treatable causes of fatigue, and patients should be encouraged to use support groups and practice low-intensity exercises. To increase treatment acceptance, patients should be made aware of alopecia risk prior to starting T-DXd. Detailed monitoring and management recommendations for T-DXd-related AEs are discussed further.

Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs

PURPOSE

Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer.

MATERIALS AND METHODS

Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs.

RESULTS

Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively.

CONCLUSION

This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Background and Objective

Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms’ clinical use cases.

Methods

we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques.

Key Content and Findings

DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients’ prognoses, and genetic mutations in lung cancers.

Conclusions

DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness.

An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

IMPORTANCE

Most early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs.

OBJECTIVE

To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control.

EXPOSURES

All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period.

MAIN OUTCOMES AND MEASURES

Each test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC).

RESULTS

Images from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, -1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, -2% to 9%) as compared with junior radiologists (4%; 95% CI, -3% to 5%).

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.

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.

Circulating Biomarkers for Early Stage Non-Small Cell Lung Carcinoma Detection: Supplementation to Low-Dose Computed Tomography

Lung cancer is currently the leading cause of cancer death in both developing and developed countries. Given that lung cancer has poor prognosis in later stages, it is essential to achieve an early diagnosis to maximize patients’ overall survival. Non-small cell lung cancer (NSCLC) is the most common form of primary lung cancer in both smokers and non-smokers. The current standard screening method, low‐dose computed tomography (LDCT), is the only radiological method that demonstrates to have mortality benefits across multiple large randomized clinical trials (RCT). However, these RCTs also found LDCT to have a significant false positive rate that results in unnecessary invasive biopsies being performed. Due to the lack of both sensitive and specific screening methods for the early detection of lung cancer, there is an urgent need for alternative minimally or non-invasive biomarkers that may provide diagnostic, and/or prognostic information. This has led to the identification of circulating biomarkers that can be readily detectable in blood and have been extensively studied as prognosis markers. Circulating microRNA (miRNA) in particular has been investigated for these purposes as an augmentation to LDCT, or as direct diagnosis of lung cancer. There is, however, a lack of consensus across the studies on which miRNAs are the most clinically useful. Besides miRNA, other potential circulating biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNAs) and non-coding RNAs (ncRNAs). In this review, we provide the current outlook of several of these biomarkers for the early diagnosis of NSCLC.

Comparison of conventional chest x ray with a novel projection technique for ultra-low dose CT

PURPOSE

To compare a novel thick-slab projection technique for ultra-low dose computed tomography (CT; thoracic tomogram) with conventional chest x ray with respect to 13 diagnostic categories.

METHODS

With the approval of the institutional ethics board, a dataset was retrospectively collected of 22 consecutive patients who had undergone a clinically requested emergency room conventional chest x ray (CXR) and a same-day standard-of-care non-contrast CT. Scanner specific noise was added to the CT images to simulate a target dose of 0.18 mSv. A novel algorithm was used to post-process CT images as coronal isotropic reformats by applying a voxel-based, locally normalized weighted-intensity projection to generate 2 cm thick slabs with 1 cm overlap. Three chest radiologists with no prior training for the study reviewed the CXR and thoracic tomogram for each case and assessed each diagnostic category (pneumonic infiltrates, pulmonary edema, interstitial lung disease, nodules > 5 mm, nodules < 5 mm, pleural effusion, pericardial effusion, heart size, acute bone fractures, foreign bodies, pneumothorax, mediastinal vessel diameter, free abdominal air) on a Likert scale from -4 (definitely absent/normal) to +4 (definitely present/abnormal). MRMC ROC curves were generated for each category. Time for interpretation and subjective image quality score (0-10) were also assessed.

RESULTS

For focal lung disease (pneumonic infiltrates, nodules < 5 mm, nodules > 5mm), the area under the ROC curve (AUC) was significantly higher for thoracic tomograms than CXR (0.803 vs 0.648, respectively, P = 0.02). For non-focal lung disease (pulmonary edema, interstitial lung disease) and effusions (pulmonary, pericardial), the AUC was larger for thoracic tomograms than CXR but the difference did not reach significance (0.870 vs 0.833, P = 0.141; and 0.823 vs 0.752, P = 0.296, respectively). For acute bone fractures and foreign bodies, the AUC was smaller for thoracic tomograms than CXR, the difference was however not significant (0.491 vs 0.532, P = 0.42; and 0.871 vs 0.971, P = 0.39, respectively). Other diagnostic categories had no true positive cases in the dataset. The mean time for interpretation for each was 36.9 and 24.0 s with standard deviations of 0.857 and 5.977. The image quality score for each was 8.2 and 7.8 with standard deviations of 0.970 and 1.614.

CONCLUSION

Thoracic tomograms were found to be diagnostically superior to CXR for focal lung disease, at no increased radiation dose. The thoracic tomogram presents an opportunity to improve the standard-of-care for patients who would otherwise receive a conventional CXR.

Low positive predictive value of computed tomography screening for lung cancer irrespective of commonly employed definitions of target population

Screening for lung cancer (LC) by low-dose computed tomography (LDCT) has been demonstrated to reduce LC mortality in randomized clinical trials (RCTs), and its implementation is in preparation in many countries. However, definition of the target population, which was based on various combinations of age ranges and definitions of heavy smoking in the RCTs, is subject to ongoing debate. Using epidemiological data from Germany, we aimed to estimate prevalence of preclinical LC and positive predictive value (PPV) of LDCT in potential target populations defined by age and smoking history. Populations aged 50 to 69, 55 to 69, 50 to 74 and 55 to 79 years were considered in this analysis. Sex-specific prevalence of preclinical LC was estimated using LC incidence data within those age ranges and annual transition rates from preclinical to clinical LC obtained by meta-analysis. Prevalence of preclinical LC among heavy smokers (defined by various pack-year thresholds) within those age ranges was estimated by combining LC prevalence in the general population with proportions of heavy smokers and relative risks for LC among them derived from epidemiological studies. PPVs were calculated by combining these prevalences with sensitivity and specificity estimates of LDCT. Estimated prevalence of LC was 0.3% to 0.5% (men) and 0.2% to 0.3% (women) in the general population and 0.8% to 1.7% in target populations of heavy smokers. Estimates of PPV of LDCT were <20% for all definitions of target populations of heavy smokers. Refined preselection of target populations would be highly desirable to increase PPV and efficiency of LDCT screening and to reduce numbers of false-positive LDCT findings.

Primary care and pulmonary physicians' knowledge and practice concerning screening for lung cancer in Lebanon, a middle-income country

Background

Screening for lung cancer with low‐dose computed tomography (LDCT) was shown to reduce lung cancer incidence and overall mortality, and it has been recently included in international guidelines. Despite the rising burden of lung cancer in low and middle‐income countries (LMICs) such as Lebanon, little is known about what primary care physicians or pulmonologists know and think about LDCT as a screening procedure for lung cancer, and if they recommend it.

Objectives

Evaluate the knowledge about LDCT and implementation of international guidelines for lung cancer screening among Lebanese primary care physicians (PCPs) and pulmonary specialists.

Methodology

PCPs and PUs based in Lebanon were surveyed concerning knowledge and practices related to lung cancer screening by self‐administered paper questionnaires.

Results

73.8% of PCPs and 60.7% of pulmonary specialists recognized LDCT as an effective tool for lung cancer screening, with 63.6% of PCPs and 71% of pulmonary specialists having used it for screening. However, only 23.4% of PCPs and 14.5% of pulmonary specialists recognized the eligibility criteria for screening. Chest X‐ray was recognized as ineffective by only 55.8% of PCPs and 40.7% of pulmonary specialists; indeed, 30.2% of PCPs and 46% of pulmonary specialists continue using it for screening. The majority have initiated a discussion about the risks and benefits of lung cancer screening.

Conclusion

PCPs and pulmonary specialists are initiating discussions and ordering LDCT for lung cancer screening. However, a significant proportion of both specialties are still using a non‐recommended screening tool (chest x‐ray); only few PCPs and pulmonary specialists recognized the population at risk for which screening is recommended. Targeted provider education is needed to close the knowledge gap and promote proper implementation of guidelines for lung cancer screening.

Is there an advantage to using computer aided detection for the early detection of pulmonary nodules within chest X-Ray imaging?

OBJECTIVE

Using published literature, this research examines whether Computer-aided Detection (CAD) identifies more Pulmonary Nodules (PN) within Chest X-ray (CXR) systems, compared to radiologist diagnosis without CAD.

KEY FINDINGS

Although the primary papers were pointing to CAD being a beneficial system in the diagnosis of PN detection, a regression analysis of the data available within these papers showed no correlation between the higher sensitivity of CAD against the detrimental high False Positives (FP) of CAD. Findings of the studies were deemed inconclusive.

CONCLUSION

Further research is recommended to review the potential of CAD on CXR PN detection.

IMPLICATIONS FOR PRACTICE

CAD acting as a second reader could potentially reduce interpreter error rate.

Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection

Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.

Smoking control: challenges and achievements

Smoking is the most preventable and controllable health risk. Therefore, all health care professionals should give their utmost attention to and be more focused on the problem of smoking. Tobacco is a highly profitable product, because of its large-scale production and great number of consumers. Smoking control policies and treatment resources for smoking cessation have advanced in recent years, showing highly satisfactory results, particularly in Brazil. However, there is yet a long way to go before smoking can be considered a controlled disease from a public health standpoint. We can already perceive that the behavior of our society regarding smoking is changing, albeit slowly. Therefore, pulmonologists have a very promising area in which to work with their patients and the general population. We must act with greater impetus in support of health care policies and social living standards that directly contribute to improving health and quality of life. In this respect, pulmonologists can play a greater role as they get more involved in treating smokers, strengthening anti-smoking laws, and demanding health care policies related to lung diseases.

Differential diagnosis of human lung tumors using surface desorption atmospheric pressure chemical ionization imaging mass spectrometry

Differential diagnosis of human lung cancer in untreated tissue is achieved by DAPCI-MSI combined with multivariate statistical analysis.

Thoracic ultrasound: An adjunctive and valuable imaging tool in emergency, resource-limited settings and for a sustainable monitoring of patients

Imaging workup of patients referred for elective assessment of chest disease requires an articulated approach: Imaging is asked for achieving timely diagnosis. The concurrent or subsequent use of thoracic ultrasound (TUS) with conventional (chest X-rays-) and more advanced imaging procedures (computed tomography and magnetic resonance imaging) implies advantages, limitations and actual problems. Indeed, despite TUS may provide useful imaging of pleura, lung and heart disease, emergency scenarios are currently the most warranted field of application of TUS: Pleural effusion, pneumothorax, lung consolidation. This stems from its role in limited resources subsets; actually, ultrasound is an excellent risk reducing tool, which acts by: (1) increasing diagnostic certainty; (2) shortening time to definitive therapy; and (3) decreasing problems from blind procedures that carry an inherent level of complications. In addition, paediatric and newborn disease are particularly suitable for TUS investigation, aimed at the detection of congenital or acquired chest disease avoiding, limiting or postponing radiological exposure. TUS improves the effectiveness of elective medical practice, in resource-limited settings, in small point of care facilities and particularly in poorer countries. Quality and information provided by the procedure are increased avoiding whenever possible artefacts that can prevent or mislead the achievement of the correct diagnosis. Reliable monitoring of patients is possible, taking into consideration that appropriate expertise, knowledge, skills, training, and even adequate equipment’s suitability are not always and everywhere affordable or accessible. TUS is complementary imaging procedure for the radiologist and an excellent basic diagnostic tool suitable to be shared with pneumologists, cardiologists and emergency physicians.