Early Lung Cancer Screening Research Articles

Association of chronic obstructive pulmonary disease with risk of lung cancer in individuals aged 40 years and older: A cross-sectional study based on NHANES 2013-2018.

It remains unclear whether chronic obstructive pulmonary disease (COPD) is an independent risk factor for lung cancer after excluding confounding factors such as smoking, age, sex, body mass index (BMI), comorbidities, etc. Data from 11,440 participants (≥ 40 years old) in the National Health and Nutrition Examination Survey (NHANES) 2013-2018 were analyzed. Weighted multivariable logistic regression models were used to assess the association between COPD and lung cancer risk. Subgroup analyses were based on age, sex, BMI, and smoking. This study included 660 patients with COPD and 10,780 participants without COPD. The prevalence of lung cancer was significantly higher in patients with COPD compared to participants without COPD (3.39% vs 0.14%). After adjusting for confounding factors, COPD was associated with a significantly increased risk of lung cancer (OR, 12.24, 95% CI, 4.99-30.06, p < 0.001). This association remained significant in all subgroups, particularly in individuals aged > 65 years (OR, 20.05, 95% CI, 6.85-58.72, p < 0.001), smokers (OR, 19.38, 95% CI, 2.02-185.66, p = 0.010), males (OR, 17.39, 95% CI, 5.28-57.31, p < 0.001), individuals who quit smoking within 10 years (OR, 12.86, 95% CI, 2.59, 63.99, p = 0.002), and individuals with a BMI > 25 kg/m2 (OR, 14.56, 95% CI, 3.88-54.69, p < 0.001). COPD is an independent risk factor for lung cancer, especially in certain subgroups. The combination of COPD and smoking greatly amplifies the lung cancer risk. These findings highlight the importance of early lung cancer screening in patients with COPD.

Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023.

In recent years, the widespread use of computed tomography (CT) in early lung cancer screening has led to an increase in the detection rate of lung ground glass nodules (GGNs). The persistence of GGNs, which may indicate early lung adenocarcinoma, has been a focus of attention for scholars in the field of lung cancer prevention and treatment in recent years. Despite the rapid development of research into GGNs, there is a lack of intuitive content and trend analyses in this field, as well as a lack of detailed elaboration on possible research hotspots. The objective of this study was to conduct a comprehensive analysis of the knowledge structure and research hotspots of lung ground glass nodules over the past decade, employing bibliometric methods. The Web of Science Core Collection (WoSCC) database was searched for relevant ground-glass lung nodule literature published from 2013-2023. Bibliometric analyses were performed using VOSviewer, CiteSpace, and the R package "bibliometrix". A total of 2,218 articles from 75 countries and 2,274 institutions were included in this study. The number of publications related to GGNs has been high in recent years. The United States has led in GGNs-related research. Radiology has one of the highest visibilities as a selected journal and co-cited journal. Jin Mo Goo has published the most articles. Travis WD has been cited the most frequently. The main topics of research in this field are Lung Cancer, CT, and Deep Learning, which have been identified as long-term research hotspots. The GGNs-related marker is a major research trend in this field. This study represents the inaugural bibliometric analysis of applied research on ground-glass lung nodules utilizing three established bibliometric software. The bibliometric analysis of this study elucidates the prevailing research themes and trends in the field of GGNs over the past decade. It also furnishes pertinent recommendations for researchers to provide objective descriptions and comprehensive guidance for future related research.

1171MO AI-derived diagnostic criteria for early lung cancer screening using primary care health records

1171MO AI-derived diagnostic criteria for early lung cancer screening using primary care health records

ALKU-Net: Adaptive Large Kernel Attention Convolution Network for Lung Nodule Segmentation

The accurate segmentation of lung nodules in computed tomography (CT) images is crucial for the early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and their similarity to other lung tissue features make this task more challenging. By using large receptive fields from large convolutional kernels, convolutional neural networks (CNNs) can achieve higher segmentation accuracies with fewer parameters. However, due to the fixed size of the convolutional kernel, CNNs still struggle to extract multi-scale features for lung nodules of varying sizes. In this study, we propose a novel network to improve the segmentation accuracy of lung nodules. The network integrates adaptive large kernel attention (ALK) blocks, employing multiple convolutional layers with variously sized convolutional kernels and expansion rates to extract multi-scale features. A dynamic selection mechanism is also introduced to aggregate the multi-scale features obtained from variously sized convolutional kernels based on selection weights. Based on this, we propose a lightweight convolutional neural network with large convolutional kernels, called ALKU-Net, which integrates the ALKA module in a hierarchical encoder and adopts a U-shaped decoder to form a novel architecture. ALKU-Net efficiently utilizes the multi-scale large receptive field and enhances the model perception capability through spatial attention and channel attention. Extensive experiments demonstrate that our method outperforms other state-of-the-art models on the public dataset LUNA-16, exhibiting considerable accuracy in the lung nodule segmentation task.

Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes.

Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model.

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Urinary Metabolite Diagnostic and Prognostic Liquid Biopsy Biomarkers of Lung Cancer in Nonsmokers and Tobacco Smokers.

Nonsmokers account for 10% to 13% of all lung cancer cases in the United States. Etiology is attributed to multiple risk factors including exposure to secondhand smoking, asbestos, environmental pollution, and radon, but these exposures are not within the current eligibility criteria for early lung cancer screening by low-dose CT (LDCT). Urine samples were collected from two independent cohorts comprising 846 participants (exploratory cohort) and 505 participants (validation cohort). The cancer urinary biomarkers, creatine riboside (CR) and N-acetylneuraminic acid (NANA), were analyzed and quantified using liquid chromatography-mass spectrometry to determine if nonsmoker cases can be distinguished from sex and age-matched controls in comparison with tobacco smoker cases and controls, potentially leading to more precise eligibility criteria for LDCT screening. Urinary levels of CR and NANA were significantly higher and comparable in nonsmokers and tobacco smoker cases than population controls in both cohorts. Receiver operating characteristic analysis for combined CR and NANA levels in nonsmokers of the exploratory cohort resulted in better predictive performance with the AUC of 0.94, whereas the validation cohort nonsmokers had an AUC of 0.80. Kaplan-Meier survival curves showed that high levels of CR and NANA were associated with increased cancer-specific death in nonsmokers as well as tobacco smoker cases in both cohorts. Measuring CR and NANA in urine liquid biopsies could identify nonsmokers at high risk for lung cancer as candidates for LDCT screening and warrant prospective studies of these biomarkers.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Association of the SHOX2 and RASSF1A methylation levels with the pathological evolution of early-stage lung adenocarcinoma

Background The methylation of SHOX2 and RASSF1A shows promise as a potential biomarker for the early screening of lung cancer, offering a solution to remedy the limitations of morphological diagnosis. The aim of this study is to diagnose lung adenocarcinoma by measuring the methylation levels of SHOX2 and RASSF1A, and provide an accurate pathological diagnosis to predict the invasiveness of lung cancer prior to surgery.Material and methods The methylation levels of SHOX2 and RASSF1A were quantified using a LungMe® test kit through methylation-specific PCR (MS-PCR). The diagnostic efficacy of SHOX2 and RASSF1A and the cutoff values were validated using ROC curve analysis. The hazardous factors influencing the invasiveness of lung adenocarcinoma were calculated using multiple regression.Results: The cutoff values of SHOX2 and RASSF1A were 8.3 and 12.0, respectively. The sensitivities of LungMe® in IA, MIA and AIS patients were 71.3% (122/171), 41.7% (15/36), and 16.1% (5/31) under the specificity of 94.1% (32/34) for benign lesions. Additionally, the methylation level of SHOX2, RASSF1A and LungMe® correlated with the high invasiveness of clinicopathological features, such as age, gender, tumor size, TNM stage, pathological type, pleural invasion and STAS. The tumor size, age, CTR values and LungMe® methylation levels were identified as independent hazardous factors influencing the invasiveness of lung adenocarcinoma.Conclusion: SHOX2 and RASSF1A combined methylation can be used as an early detection indicator of lung adenocarcinoma. SHOX2 and RASSF1A combined (LungMe®) methylation is significantly correlated to age, gender, tumor size, TNM stage, pathological type, pleural invasion and STAS. The SHOX2 and RASSF1A methylation levels, tumor size and CTR values could predict the invasiveness of the tumor prior to surgery, thereby providing guidance for the surgical procedure.

Preliminary results of the Lung Cancer Mutation Consortium LCMC4 evaluation of actionable drivers in early stage lung cancer (LEADER) screening trial.

Preliminary results of the Lung Cancer Mutation Consortium LCMC4 evaluation of actionable drivers in early stage lung cancer (LEADER) screening trial.

Results of Lung Cancer Screening with Low-dose Computed Tomography and Exploration of Risk Factors in Guangzhou

Both of lung cancer incidence and mortality rank first among all cancers in China. Previous lung cancer screening trials were mostly selective screening for high-risk groups such as smokers. Non-smoking women accounted for a considerable proportion of lung cancer cases in Asia. This study aimed to evaluate the outcome of community-based mass screening in Guangzhou and identify the high-risk factors for lung cancer. Residents aged 40-74 years in Guangzhou were screened with low-dose computed tomography (LDCT) for lung cancer and the pulmonary nodules were classified and managed according to China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2018 version). The detection rate of positive nodules was calculated. Before the LDCT examination, residents were required to complete a "lung cancer risk factors questionnaire". The risk factors of the questionnaire were analyzed by least absolute shrinkage and selection operator (LASSO) penalized Logistic regression analysis. A total of 6256 residents were included in this study. 1228 positive nodules (19.63%) and 117 lung cancers were confirmed, including 6 cases of Tis, 103 cases of stage I (accounting for 88.03% of lung cancer). The results of LASSO penalized Logistic regression analysis indicated that age ≥50 yr (OR=1.07, 95%CI: 1.06-1.07), history of cancer (OR=3.29, 95%CI: 3.22-3.37), textile industry (OR=1.10, 95%CI: 1.08-1.13), use coal for cooking in childhood (OR=1.14, 95%CI: 1.13-1.16) and food allergy (OR=1.10, 95%CI: 1.07-1.13) were risk factors of lung cancer for female in this district. This study highlighted that numerous early stages of lung cancer cases were detected by LDCT, which could be applied to screening of lung cancer in women. Besides, age ≥50 yr, personal history of cancer, textile industry and use coal for cooking in childhood are risk factors for women in this district, which suggested that it's high time to raise the awareness of early lung cancer screening in this group.

Serum IL-24 combined with CA125 as screening and prognostic biomarkers for NSCLC.

Noninvasive and effective methods for early screening of non-small cell lung cancer(NSCLC) still need to be developed. At present, a reasonable conclusion is that a combination of tumor markers is a superior predictor of screening. Cytokines, as important regulators of cancer development, have great potential for the screening and prognosis of NSCLC. This study screened novel biomarkers related to the early screening and prognosis of NSCLC. In the present study, the biological significance and immunoregulation of interleukin-24(IL-24) were analyzed based on The Cancer Genome Atlas data. Next, 150 serum samples from initially treated patients with NSCLC and 70 controls were collected, and we obtained pathological sections from 60 patients with NSCLC. The ELISA and immunohistochemistryresults showed the differential expression of IL-24 and carbohydrate antigen 125(CA125). The results show that IL-24 is an important tumor suppressor in NSCLCthat helps to improve the poor prognosis of these patients. A significantly negative correlation between IL-24 and CA125 levels was also found. Notably, serum IL-24 levels were significantly negatively correlated with the TNM stage of patients with NSCLC, consistent with an important role for tumor suppressors in NSCLC. The receiver operating characteristic curve analysis showed that a combination of IL-24 and CA125 was an effective panel for discriminating patients with NSCLC from HD, and individuals with other lung diseases. Serum IL-24 and CA125 levels were identified as independent prognostic markers for NSCLC. The IL-24 and CA125 panel exhibited good performance in the screening of NSCLC.

SCREENING AND EARLY DETECTION OF LUNG CANCER: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer-related deaths globally, accounting for the greatest economic and public health burden of all cancers. In 2020, lung cancer accounted for approximately 2.2 million cases and nearly 1.8 million deaths worldwide. The aim: The aim of this study to show about screening and early detection of lung cancer. Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Sciencedirect were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: Four publications were found to be directly related to our ongoing systematic examination after a rigorous three-level screening approach. Subsequently, a comprehensive analysis of the complete text was conducted, and additional scrutiny was given to these articles. Conclusion: Early screening and diagnostic methods of lung cancer, x-ray screening is not recommended. LDCT has obvious advantages and is the most promising imaging method in early screening of lung cancer.

Microbes for lung cancer detection: feasibility and limitations.

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Dynamic mutation late acceptance hill climbing aided red fox optimization for metabolomic biomarkers selection from lung cancer patient sera

The accurate selection of serum metabolomic biomarkers for early lung cancer screening remains a significant challenge in the clinical context. Consequently, this study introduces the Red Fox Optimization (RFO) that integrates Dynamic Mutation Late Acceptance Hill Climbing (DM-LAHC), with the aim of selecting a panel of serum metabolomic biomarkers suitable for distinguishing between benign and malignant pulmonary nodules. The key innovation is the dynamic adjustment of the mutation probability in the Late Acceptance Hill Climbing algorithm, which greatly enhances the local search capabilities. And the RFO's reproduction mechanism has been improved through the utilization of a more efficient interpolation form. The biomarker selection model employs a multi-objective fitness function that takes into account both accuracy and quantity. After this, the optimal model yielded a biomarker panel, including Inosine, Hippuric acid, Alanine, and other metabolites. This model demonstrates outstanding performance on an independent test dataset, achieving a fitness value of 0.9136, an AUC (Area Under the Curve) of 0.9926, a sensitivity of 0.9643, and a specificity of 0.9412. Furthermore, the clinical net benefit is highlighted across various risk thresholds by decision curve analysis. These results underscore the significance of DM-LAHC aided RFO in the selection of serum metabolomic biomarkers for lung cancer. The supporting source codes of this work can be found at: https://github.com/zzl2022/DM-LAHC-aided-RFO.

Detection of Single Nucleotide Polymorphisms of Circulating Tumor DNA by Strand Displacement Amplification Coupled with Liquid Chromatography.

The detection of multiple single nucleotide polymorphisms (SNPs) of circulating tumor DNA (ctDNA) is still a great challenge. In this study, we designed enzyme-assisted nucleic acid strand displacement amplification combined with high-performance liquid chromatography (HPLC) for the simultaneous detection of three ctDNA SNPs. First, the trace ctDNA could be hybridized to the specially designed template strand, which initiated the strand displacement nucleic acid amplification process under the synergistic action of DNA polymerase and restriction endonuclease. Then, the targets would be replaced with G-quadruplex fluorescent probes with different tail lengths. Finally, the HPLC-fluorescence assay enabled the separation and quantification of multiple signals. Notably, this method can simultaneously detect both the wild type (WT) and mutant type (MT) of multiple ctDNA SNPs. Within a linear range of 0.1 fM-0.1 nM, the detection limits of BRAF V600E-WT, EGFR T790M-WT, and KRAS 134A-WT and BRAF V600E-MT, EGFR T790M-MT, and KRAS 134A-MT were 29, 31, and 11 aM and 22, 29, and 33 aM, respectively. By using this method, the mutation rates of multiple ctDNA SNPs in blood samples from patients with lung or breast cancer can be obtained in a simple way, providing a convenient and highly sensitive analytical assay for the early screening and monitoring of lung cancer.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.

Diagnosis, treatment, and prognosis of stage IB non-small cell lung cancer with visceral pleural invasion.

With the increasing implementation of early lung cancer screening and the increasing emphasis on physical examinations, the early-stage lung cancer detection rate continues to rise. Visceral pleural invasion (VPI), which denotes the tumor's breach of the elastic layer or reaching the surface of the visceral pleura, stands as a pivotal factor that impacts the prognosis of patients with non-small cell lung cancer (NSCLC) and directly influences the pathological staging of early-stage cases. According to the latest 9th edition of the TNM staging system for NSCLC, even when the tumor diameter is less than 3 cm, the final T stage remains T2a if VPI is present. There is considerable controversy within the guidelines regarding treatment options for stage IB NSCLC, especially among patients exhibiting VPI. Moreover, the precise determination of VPI is important in guiding treatment selection and prognostic evaluation in individuals with NSCLC. This article aims to provide a comprehensive review of the current status and advancements in studies pertaining to stage IB NSCLC accompanied by VPI.

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.