Early Diagnosis Of Lung Cancer Research Articles

Construction of test strips for lung cancer detection based on aptamers

Lung cancer is the most common malignancy worldwide. Early diagnosis helps to reduce mortality and improve survival. Aptamers are widely used in cancer screening because of their high specificity, good stability and low cost. In this study, using the specific aptamer of lung cancer serum, the sandwich method colloidal gold test strip was prepared by isothermal amplification technique and the principle of nucleic acid hybridisation for the early diagnosis of lung cancer. The results showed that the test strip was positive in 8 patients with lung cancer, which was consistent with the actual cases. The test strip can accurately identify lung cancer patients. The concentration range of nucleic acid detection is 1 × 10−4 - 7 × 10−4 mol/L, and the detection limit is 0.67 mM. The test strip detection method has low cost and simple operation, and provides a reference for the development of home portable tumor early detection.

Few-shot segmentation framework for lung nodules via an optimized active contour model.

Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge. Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings. In this paper, we propose a few-shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high-order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours. We compared our proposed method with state-of-the-art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators. Our approach utilizes the output of few-shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

Multiplex Detection of Single Nucleotide Polymorphisms by Liquid Chromatography for Nonsmall Cell Lung Cancer Staging.

In this work, an integrated strategy with excellent accuracy and high throughput is proposed for the precise indication of single nucleotide polymorphism (SNP) in nonsmall cell lung cancer diseases. Two types of point mutations (L858R and T790M) and the corresponding wild types could be identified together in a single high-performance liquid chromatographic run. Signal amplification was achieved through a series of enzyme ligation, primer extension, and enzyme cleavage strategies, and a large number of DNA probes with different fluorescence signals were finally generated. The factors affecting the spatiotemporal separation efficiency of four DNA probes were systematically investigated. The limits of detection of wild types (WTs) or mutant types (MTs) abbreviated as L858R-MT, L858R-WT, T790M-MT, and T790M-WT were 26, 24, 19, and 22 aM, respectively. In addition, the levels of mutant types and wild types in the serum of 40 nonsmall cell lung cancer patients at different stages were detected using the method, and the correlation between the mutation ratios and cancer stages was preliminarily verified. The proposed highly selective and sensitive method may serve as an alternative approach for early diagnosis and staging of nonsmall cell lung cancer.

Application Analysis of AI Technology Combined with Spiral CT Scanning in Early Lung Cancer Screening

At present, the incidence and fatality rate of lung cancer in China rank first among all malignant tumors. Despite the continuous development and improvement of China's medical level, the overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. A number of studies have confirmed that early diagnosis and treatment of early stage lung cancer is of great significance to improve the prognosis of patients. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. ai is used in cancer screening, clinical diagnosis, radiation therapy (image acquisition, at-risk organ segmentation, image calibration and delivery) and other aspects of rapid development. However, whether medical ai can be socialized depends on the public's attitude and acceptance to a certain extent. However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning. In view of this, this study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode and provide a reference for clinical diagnosis and treatment.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Dual-targets fluorescent nanoprobe for precise subtyping of lung cancer.

A Au-Se bond-based nanoprobe using 3',3-diselenopropionic acid to simultaneously link response chains for Pro-GRP protein and Cyfra21-1 was developed. Early diagnosis and subtyping of lung cancer can be achieved based on the nanoprobes' differential response of the probes to the two targets in lung cancer patients' serum.

Evaluation of Bronchoalveolar Lavage Cytology and Comparison with Endobronchial Biopsy

Introduction: Early diagnosis of infective etiology and lung cancer plays a vital role in reducing mortality rate of lower respiratory tract disease. Different modalities can be applied for early diagnosis e.g. bronchoalveolar lavage and endobronchial biopsy. Cytological and histopathological diagnostic techniques are safer, economical and provide appropriate results.
 Aims: To find out diagnostic yield of bronchoalveolar lavage cytology in diagnosing lung pathology and to determine its sensitivity, and specificity in malignant cases considering endobronchial biopsy as the gold standard.
 Methods: This hospital based analytical study was carried out in the Department of Pathology at Nepalgunj medical college, Nepalgunj during the period from January 2023 to September 2023 with a total of 50 participants.
 Results: 29(58%) patients were male and 21(42%) female; the mean age was 53.16 ± 19.9 years. Considering histopathological findings, maximum patients of malignant cases had squamous cell carcinoma 3(6%), then adenocarcinoma 1(2%). The sensitivity, specificity, positive and negative predictive value of bronchoalveolar lavage in malignant cases were 75%, 100%, 100% and 50% respectively.
 Conclusion: Bronchoalveolar lavage fluid cytology is a useful tool for diagnosis of lung cancer. It has good sensitivity, and specificity, and shows nearly identical information as biopsy.

Developing and Setting Up a Lung Cancer Triage Phoneline For Rural West Wales

The incidence of people diagnosed with and living with cancer in the UK is increasing and by 2030, 1 in 2 people will develop cancer (The Cancer Delivery Plan 2016-2020 WAG). Health care providers must explore innovative ways to provide quality care both in terms of outcomes and patient experience set against the challenge of finite resources. Within Wales there are over 2,300 patients diagnosed with lung cancer each year, however less than 20% of these are diagnosed at early stage when they are eligible for curative treatment. It has been shown that early diagnoses leads to improved survival.
 More than 90% of patients with lung cancer are symptomatic at diagnosis. 
 Rates of lung cancer diagnosis have been drastically impacted by COVID, with referral rates and presentation to GPs at an all-time low in 2020-21. Screening of patients by triage nurse will help reduce GP workload by allowing patients to be referred, for Chest X-ray, and then to Secondary Care for onward management.
 To improve early diagnosis of lung cancer, LUMEN (Lung Cancer Symptom Assessment Line) Nurse Led Service was developed with the following aims:
 
 To deliver a triage service that is safe, effective, equitable and accessible.
 Develop and launch this service and integrate into current lung cancer pathways.
 Promote and deliver the service and determine acceptability, feasibility, safety, and cost-effectiveness of the pathway.
 
 By increasing patient access to diagnostic testing, we hope to increase early diagnosis and subsequent patient outcomes.
 Patient benefits
 The Nurse will make onward referrals to lung cancer team, support the person throughout this journey and offer smoking cessation advice, if needed.
 Staff Benefits
 Role is varied and includes raising awareness of lung cancer symptoms, promoting the service, and liaising with various teams to improve patient journey.

Analyzing CT Scan Images towards the Early Detection of Lung Cancer using Medical Images based Edge Feature Preserving CT Scan Medical Image Coder (EZWT - EFPIC)

With the current improvements in virtual image processing techniques have received several benefits. Today, all of the scientific techniques produce virtual scientific pictures, through healthcare specialists analyze and diagnose the abnormality. The frequent view of scientific picture processing might also additionally appear simpler; however, it entails many challenges. As the scientific pics are interconnected with human lives, the laptop-aided scientific image processing structures have to be overcautious, if we want to eliminate inaccuracy rates. The utility of medical image processing techniques for the analysis of CT scan images similar to lung cancer cells has been gaining momentum in recent years. This paper discusses the use of a Transform Edge Feature preserving CT scan Medical Image Coder (EZWT - EFPIC) using Computed Tomography (CT) images to help in the early diagnosis of lung cancer. We discuss and explore the design and significance of an EZWT-EFPIC-CT image-processed model in cancer diagnosis.

Segmentation of lung nodules based on a refined segmentation network.

Accurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules. As regards the use of deep learning to segment lung nodules, convolutional neural networks would gradually lead to errors accumulating at the network layer due to the presence of multiple upsampling and downsampling layers, resulting in poor segmentation results. In this study, we developed a refined segmentation network (RS-Net) for lung nodule segmentation to solve this problem. Accordingly, the proposed RS-Net was first used to locate the core region of the lung nodules and to gradually refine the segmentation results of the core region. In addition, to solve the problem of misdetection of small-sized nodules owing to the imbalance of positive and negative samples, we devised an average dice-loss function computed on nodule level. By calculating the loss of each nodule sample to measure the overall loss, the network can address the misdetection problem of lung nodules with smaller diameters more efficiently. Our method was evaluated based on 1055 lung nodules from Lung Image Database Consortium data and a set of 120 lung nodules collected from Shanghai Chest Hospital for additional validation. The segmentation dice coefficients of RS-Net on these two datasets were 85.90% and 81.13%, respectively. The analysis of the segmentation effect of different properties and sizes of nodules indicates that RS-Net yields a stable segmentation effect. The results show that the segmentation strategy based on gradual refinement can considerably improve the segmentation of lung nodules.

Role of liquid biopsy in non small cell lung cancer

Lung cancer is the leading cause of morbidity and mortality worldwide. It is usually diagnosed in advance stage. miRNA present in serum and pleural fluid can be studied for early diagnosis of lung cancer. Present study was carried out to evaluate whether miRNA can be used as biomarkers in diagnosis of non small cell lung cancer. The study was intended to find the non-invasive tumour biomarkers for presence of lung malignancy with the intent of instituting early diagnosis to reduce lung cancer related mortality. The aim of the study was to evaluate circulating microRNA expression in adenocarcinoma and squamous cell carcinoma lung in comparison with age and sex matched healthy controls. The expression of these miRNA was correlated with histopathology and/or immunohistochemistry. The circulating miRNA expression in age and sex matched non-smoking healthy controls was also noted. It was a Prospective observational study in which 50 cases of non small cell lung cancer was included. 50 healthy non smoker volunteers (control group, well adjusted to the patients according to the age and sex) were also included in the study. About 5 ml of serum and wherever possible pleural fluid was collected in the sterile container. The sample was allowed to stand at room temperature for one hour, and then samples were centrifuged at 1300g for ten minutes at room temperature.RNA was extracted using miRNeasy mini kit (Cat no. 217004) and quantative PCR was done. The patients age, sex, histopathological results, clinical staging, immunohistochemistry, presence of pleural effusion. Expression of mi RNA (miRNA 21, miRNA 17-92 cluster, miRNA 221/222, miRNA Let- 7, miRNA 34 and miRNA 200) were studied. Out of 50 patients of suspected lung cancer 17 were females (34%) and 33 (66%) were males. Mean age of presentation was 63.26 years. 37 patients gave history of smoking (74%) while 13 patients were non Smokers (26%). 29 patients (58%) showed histomorphological features suggestive of adenocarcinoma whereas 21 patients (42%) showed histomorphological features of squamous cell carcinoma. EGFR mutation was seen in 10 patients (34%). Pleural effusion was present in 20 cases.Statistically significant correlation was found between the expression of miRNA in healthy controls and in lung cancer patients. All the tested miRNAs were significantly correlated with the corresponding expression in the healthy control. As compared to healthy controls that let-7, miR-34 and miR-200 were downregulated in lung cancer patients whereas miRNA-221, miRNA 17-92, miRNA-21 were upregulated in lung cancer patients. miR 34, miR 200 and let 7 was detected in healthy controls also. No statically significant correlation of miRNA with age, sex, smoking, histopathological type, grade of tumor, stage of disease, EGFR mutation and IHC was found. Stastically significant correlation was found between miRNA 200 and pleural effusion patients. Present study concludes that miRNA can be a potential biomarker for diagnosing lung cancer. To date, there is convincing evidence supporting the potential role of miRNAs as biomarkers for lung cancer diagnosis and prognosis. However, further research is required in this aspect.

Exosomes in lung cancer metastasis, diagnosis, and immunologically relevant advances.

Lung cancer is a chronic wasting disease with insidious onset and long treatment cycle. Exosomes are specialized extracellular vesicles, at first exosomes were considered as a transporter of cellular metabolic wastes, but recently many studies have identified exosomes which contain a variety of biologically active substances that play a role in the regulation of cellular communication and physiological functions. Exosomes play an important role in the development of lung cancer and can promote metastasis through a variety of mechanisms. However, at the same time, researchers have also discovered that immune cells can also inhibit lung cancer through exosomes. In addition, researchers have discovered that some specific miRNAs in exosomes can be used as markers for early diagnosis of lung cancer. Engineering exosomes may be one of the strategies to enhance the clinical translational application of exosomes in the future, for example, strategies such as modifying exosomes to enhance targeting or utilizing exosomes as carriers for drug delivery have been explored. but more studies are needed to verify the safety and efficacy. This article reviews the latest research on exosomes in the field of lung cancer, from the mechanism of lung cancer development, the functions of immune cell-derived exosomes and tumor-derived exosomes, to the early diagnosis of lung cancer.

Diagnostic Potential of Serum Glycome Analysis in Lung Cancer: A Glycopattern Study.

Lung cancer is the leading cause of cancer-related death, with high morbidity and mortality rates due to the lack of reliable methods for diagnosing lung cancer at an early stage. Low-dose computed tomography can help detect abnormal areas in the lungs, but only 16% of cases are diagnosed early. Tests for lung cancer markers are often employed to determine genetic expression or mutations in lung carcinogenesis. Serum glycome analysis is a promising new method for early lung cancer diagnosis as glycopatterns exhibit significant differences in lung cancer patients. In this study, we employed a solid-phase chemoenzymatic method to systematically compare glycopatterns in benign cases, adenocarcinoma before and after surgery, and advanced stages of adenocarcinoma. Our findings indicate that serum high-mannose levels are elevated in both benign cases and adenocarcinoma, while complex N-glycans, including fucose and 2,6-linked sialic acid, are downregulated in the serum. Subsequently, we developed an algorithm that utilizes 16 altered N-glycans, 7 upregulated and 9 downregulated, to generate a score based on their intensity. This score can predict the stages of cancer progression in patients through glycan characterization. This methodology offers a potential means of diagnosing lung cancer through serum glycome analysis.

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study.

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

Abstract C110: Mobile low dose computerized tomographic (LDCT) scanning program identifies pre-malignant lung cancer lesions in a Native American population: A study of Indian Health Service and Levine Cancer Institute

Abstract BACKGROUND: Native Americans (NA) are an under-served population for healthcare, including lung cancer screening. Randomized trials have shown LDCT screening for lung cancer improves survival but with severe under-representation of NA due to financial and insurance constraints, geography and lack of access. They are over-represented in national statistics of presentation with advanced disease and lung cancer deaths. In our region, the Indian Health Service (IHS) has recorded less than 5% of eligible heavy smokers from the Catawba and other tribes have undergone LDCT lung screening. We previously reported that a free, mobile LDCT unit achieved a shift to earlier diagnosis in African Americans (AAs) and other under-served groups with improved 4 year survival and reduced costs of care (1).We adapted our initial trial to include a structured evaluation of efficacy and utility of mobile LDCT in heavy smokers in our NA population. We hypothesized that mobile LDCT would overcome key obstructions to lung cancer screening in NA. METHODS: We used a coach fitted with a portable 32 slice low-dose CT scanner; all films were reviewed by a central panel via the LUNGRADS protocol (1). Eligible subjects were invited to participate by IHS staff. Levine Cancer Institute committed to treat any identified cancers, irrespective of insurance status to avoid delay. Technical details of the unit have been reported (1). RESULTS: 91 heavy smokers have been screened, representing 60% participation rate, with the following characteristics: M:F=1:1; median age 61 years (44-77 years); 68% had some form of health insurance which did not cover lung cancer screening (termed "under-insured") and 32% were uninsured; 73% still smoking; median pack year history 48 (range 22-98). No lung cancers were identified but 10% had LUNGRADS 3-4 (moderate-high risk) lesions, 36% had LUNGRADS 2 lesions and 20% had scattered, non-diagnostic lung lesions; patients with LUNGRADS 4 lesions were offered biopsy to define whether cancer was present. All subjects have been recruited into ongoing follow up LDCT and smoking cessation programs. DISCUSSION: NA traditionally have had the highest level of advanced lung cancer at presentation with high mortality rate from lung cancer. We have increased participation in LDCT lung cancer screening from 5% to 60% of eligible heavy smokers, and have identified a unique sub-population of 10% with potentially premalignant disease that will require meticulous follow-up to achieve early diagnosis of lung cancer. Our previous studies in impoverished AAs and other under-served groups have identified a shift from 20% to 60% early stage cancer at diagnosis; similar benefits are expected in NAs. This is the first such study in NAs and suggests mobile LDCT should be considered by health services responsible for their care. Studies of the science of cancer in the under-served should also focus on pragmatic solutions with potential for early improvement in outcome. REFERENCES: 1. Raghavan D et al, The Oncologist, 2020, 25: e777-e781. Citation Format: Kia Dungan, Hollis Reed, Darcy Doege, Daniel Carrizosa, Melissa Wheeler, Derek Raghavan. Mobile low dose computerized tomographic (LDCT) scanning program identifies pre-malignant lung cancer lesions in a Native American population: A study of Indian Health Service and Levine Cancer Institute [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C110.

Revolutionizing Lung Cancer Diagnosis: A Comprehensive Review of Image Processing Techniques for Early Detection and Precision Medicine

According to World Health Organisation (WHO), lung cancer is the leading cause of cancer-related fatalities in both genders and has the highest fatality rate. Early detection of pulmonary nodules is essential to improving the significant survival rate of lung cancer due to the typical proliferation of lung cells. Studies on lung cancer indicate that smoking is the primary cause of this disease, which is more common in women nowadays and causes more deaths than breast cancer. Age, gender, race, socioeconomic status, exposure to the environment, air pollution, alcohol consumption, and second-hand smoking are a few more factors that could be significant in causing lung cancer. Early detection of lung cancer is achieved through a variety of image processing techniques, such as computed tomography (CT), bone scanning, magnetic resonance imaging (MRI), Positron Emission Tomography, PET-CT, and X-ray scanning. These techniques are combined with machine learning algorithms, data mining, and artificial intelligence-based detection techniques, which improve detection through efficient computing systems known as computer assisted diagnosis (CAD). Since practically all lung cancer screening and detection is dependent on image processing, this article will serve as a reference for aspiring researchers to understand the many detection strategies in effectively identifying lung cancer. Additionally, five distinct methods are evaluated and critically analysed, along with their benefits and drawbacks, taking into account the present and potential future developments in early lung cancer diagnosis for human survival.

BIOMARKER DISCOVERIES AND POTENTIAL BIOMARKERS IN LUNG CANCER : A NARRATIVE REVIEW

Lung cancer is the second most common cause of death worldwide after breast cancer. This is associated with late diagnosis, which reduces the survival rate. Early diagnosis is one approach that may be employed to improve outcome for the patient with lung cancer. When a small level of cancer is present in the body, a biomarker can be utilized as a diagnostic marker. Liquid biopsy is minimal invasive which can be done to obtain multi-biomarkers in lung cancer. In this article, we will discuss and explore about potential biomarkers for lung cancer early diagnosis.

An immunoassay sensor based on ionic liquid modified carbon nanotubes for highly sensitive detection of lung cancer biomarker CA-125

An electrochemical immunosensor based on ionic liquid (IL) functionalized multi-walled carbon nanotubes (MWCNTs) was developed for sensitive detection of cancer antigen 125 (CA-125) lung cancer biomarker. The IL-MWCNT nanocomposite was prepared by free radical polymerization and characterized by electron microscopy and spectroscopy. The immunosensor was fabricated by immobilizing anti-CA-125 antibodies onto IL-MWCNT modified glassy carbon electrode. Cyclic voltammetry and differential pulse voltammetry techniques were applied to optimize experimental conditions and evaluate analytical performance. The immunosensor exhibited a wide linear detection range (0.1–500 U/mL), low limit of detection (0.033 U/mL) and high reproducibility and selectivity. Testing in spiked serum samples demonstrated the sensor's potential for clinical applications. The novel IL-MWCNT nanocomposite provides an ideal interface for CA-125 capture while enhancing electrochemical properties. This electrochemical immunoassay offers a simple, rapid and ultrasensitive approach for early diagnosis of non-small cell lung cancer.

The Early Diagnosis of Lung Cancer: Critical Gaps in the Discovery of Biomarkers

The Early Diagnosis of Lung Cancer: Critical Gaps in the Discovery of Biomarkers

Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee

Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.