Lung cancer screening

Non-smoking women need more attention in lung cancer screening: A real-world study from China

BACKGROUND

Seniors with a history of heavy smoking were defined as high-risk populations for lung cancer according to the National Comprehensive Cancer Network (NCCN) Lung Cancer Screening Guideline. We did a real-world study to explore the efficacy of the current NCCN Lung Cancer Screening Guidelines in the context of the real-life Chinese populations and possible directions for improvement.

METHODS

We collected hospital data from 2595 consecutive patients first diagnosed with lung cancer, comprising 1288 men and 1307 women. Analysing the distribution of lung cancer tumour characteristics and risk factors in different age and sex groups. Additionally, the number and proportion of high-risk populations in different sexes were calculated based on the NCCN Lung Cancer Screening Guidelines.

RESULTS

There were significant differences in the distribution of tumour characteristics and risk factors across sex and age groups. The proportions of lung adenocarcinoma and non-smokers were significantly higher in female patients compared with male patients. Older male patients showed significantly higher proportions of squamous cell carcinoma and heavy smokers than younger male patients. According to the NCCN screening criteria, there were 649 high-risk men and only 12 high-risk women. The proportion of high-risk populations in the aged lung cancer patients was 58.6 % in men and 1.2 % in women.

CONCLUSIONS

In China, only a minority of female lung cancer patients meet the definition of the high-risk populations for the NCCN lung cancer screening guidelines. Future lung cancer screening strategies for the Chinese population should focus more on younger populations and pay greater attention to non-smoking women to avoid high rates of underdiagnosis.

SETD5 facilitates stemness and represses ferroptosis via m6A-mediating PKM2 stabilization in non-small cell lung cancer

SETD5, an atypical member of the histone lysine methyltransferase family known for its association with cancer stemness, is a significant predictor of unfavorable survival outcomes in non-small cell lung cancer (NSCLC). However, the function of SETD5 in NSCLC stemness remains unclear, and whether it is an active H3K36me3 is controversial. Consequently, further investigation is required to clarify the pivotal role of SETD5 in NSCLC stemness and its related mechanism. Thus, this study employed the NSCLC tissue microarray and bioinformatics tools to analyze SETD5 expression and determine its effect on stemness and investigated the role of SETD5 in the metastasis of NSCLC using in vitro and in vivo analyses. The findings indicated high SETD5 expression in embryonic and NSCLC tissues, which was related to the pathological tumor stage, lymph node metastasis, and clinical stage, indicating that SETD5 could be used as a biomarker and prognostic factor in NSCLC. In addition, we found that SETD5 can promote glycolysis, thereby inhibiting ferroptosis and promoting the stemness of NSCLC, causing tumor metastasis and adverse prognosis in patients. In terms of mechanism, SETD5 as H3K36me3 facilitates the m6A modification of METTL14 and the recruitment of YTHDF1 and mediates PKM2 nuclear translocation and phosphorylation of p-PKM2 Tyr105, regulating GPX4 mediated ferroptosis resistance and SOX9 mediated stemness in NSCLC. The findings emphasize that SETD5 may serve as a promising indicator of stemness in NSCLC, which can help develop therapeutic targets for NSCLC and prognostic evaluation. This study provides evidence that SETD5 as H3K36me3 facilitates the m6A modification of METTL14 and the recruitment of YTHDF1 and mediates the nuclear translocation of PKM2, regulating GPX4 mediated ferroptosis resistance and SOX9 mediated stemness, causing tumor metastasis and adverse prognosis in patients.

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment

BACKGROUND

 Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.

METHODS

 The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability.

RESULTS

 A total of 176 patients were included, divided into benign/preinvasive (n = 76) and invasive cancer groups (n = 100). Multivariate analysis identified eight independent predictors of malignancy: lobulation sign, bronchial inflation sign, AI-predicted malignancy probability, nodule nature, diameter, solidity proportion, vascular endothelial growth factor, and lung cancer autoantibodies. The combined predictive model demonstrated high accuracy (area under the curve [AUC] = 0.946). DCA showed that the combined model significantly outperformed the traditional model, and also proved superior to models using AI-predicted malignancy probability or the seven lung cancer autoantibodies plus traditional model. External validation confirmed its robustness (AUC = 0.856), achieving a sensitivity of 0.80 and specificity of 0.86, effectively distinguishing between invasive and noninvasive nodules.

CONCLUSION

 This combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.

The Evidence Effect: How Fact Boxes Shift Perceptions of Lung Cancer Screening in Austrian Medical Practice

BACKGROUND

Recent results from the Dutch NELSON study have rekindled debates about the benefit-to-harm ratio of lung cancer screening and the comprehension of this by physicians.

METHODS

This research surveyed the perception and understanding of 136 Austrian physicians regarding the advantages and risks of lung cancer screening, examining the impact of educational data visualization tools, including fact box and icon array. Physicians participated in an online survey about their understanding before and after exposure to either a fact box alone or combined with an icon array.

RESULTS

The findings indicated that the fact box significantly enhanced physicians' grasp of the screening's benefits and harms, making them up to 13 times more likely to adjust their estimates within a predefined range. Notably, the intervention was more effective among physicians who initially did not recommend CT screening. However, the addition of the icon array did not offer significant improvement. Postintervention, physicians showcased better comprehension and an improved ability to offer patient-centered advice, which may bolster adherence to lung cancer screening protocols.

OUTLOOK

Despite its insights, the study's cross-sectional nature and the unique cultural context underline the need for more research. Further exploration should focus on different settings and assess the real-world implications on clinical practice and patient outcomes.

Development and validation of a tumor marker-based model for the prediction of lung cancer: an analysis of a multicenter retrospective study in Shanghai, China

Background

The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases.

Methods

This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis.

Results

Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889-0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making.

Conclusions

Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making.

Effect of Exercise and Pulmonary Rehabilitation in Pre- and Post-Surgical Patients with Lung Cancer: Systematic Review and Meta-Analysis

Background and objectives: Lung cancer is a common cancer, and its impact on public health is not only reflected in the 1 million deaths it causes annually but also in the significant implications it has on daily activities and quality of life, resulting in a considerable burden on healthcare systems. This review aims to determine the effects of pulmonary rehabilitation and pre- or post-surgical exercise in patients with lung cancer. Materials and methods: A systematic review with a meta-analysis of randomized controlled trials published between 2010 and 2024 was conducted; the search was carried out in PubMed, Cochrane Clinical Trial, SCOPUS, Science Direct, Web of Science, Scielo, and LILAC. Results: Pulmonary rehabilitation or exercise before surgery was associated with a greater 6 min walking distance (MD: 37.42, 95% CI: 9.68-65.1; p = 0.008); however, it had no implications on hospital stay (MD: -0.91, 95% CI: -1.88-0.055; p = 0.06). When the intervention was performed post-surgery, higher FEV1 (SMD: 0.62, 95% CI: 0.32-0.92; p = 0.0001) and improved 6 min walking distances (60.8, 95% CI: 20.96-100.6; p = 0.0033) were found compared to standard management. Conclusions: This review suggests that, depending on the timing of implementation, pulmonary rehabilitation or exercise could produce positive effects on certain clinical variables in lung cancer patients.

Multiplex plasma protein assays as a diagnostic tool for lung cancer

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.

Epidemiological study of overall survivability of individuals diagnosed with lung and bronchus cancer in Michigan between the years 1996 and 2017

INTRODUCTION

Lung and bronchus cancer is a leading cause of death in the United States. Compared with the national average, Michigan has an increased mortality rate and low early screening and treatment rates. This study aimed to explore the epidemiological trends and assess overall survival (OS) of patients diagnosed with lung cancer in Michigan from 1996 to 2017.

METHODS

Data was acquired from the Michigan Cancer Surveillance Program (MCSP). Log-rank test was used to test OS among the time periods, univariate and multivariate cox regression models were employed to determine factors that significantly affected OS. We hypothesized that the introduction of more inclusive lung cancer screening guidelines in 2013 would improve OS for patients diagnosed after its implementation and that individual characteristics and tumor characteristics would both affect OS.

RESULTS

Notably, 153 742 individuals met inclusion criteria: 54.22% male and 45.78% female. Mean age at diagnosis was 69 years. No significant difference in OS was found among the three time periods (p = 0.99). Univariate analyses identified four individual characteristics associated with reduced OS: age at diagnosis, male sex, American Indian race, and living in rural or urban area. Reduced OS was associated with primary sites tumors at main bronchus, lung base, or within overlapping lobes, and SEER stage 7.

CONCLUSIONS

This study highlights several factors that influence OS. Consideration of these factors may be helpful as a community outreach tool to help increase early detection and reduce overall mortality.

Survival of lung cancer patients according to screening eligibility using Korean Lung Cancer Registry 2014-2016

This study assessed survival for lung cancer patients meeting criteria for the National Lung Cancer Screening Program in Korea launched in 2019 and updated guideline reported by the US Preventive Service Task Force (USPSTF). We assessed all-cause mortality based on the Korean Lung Cancer Registry (KLCR), including lung cancer patients diagnosed in 2014-2016. We compared survival among lung cancer patients eligible for extended USPSTF criteria (age 50-80 years and ≥ 20 pack-years) and those meeting current criteria (age 54-74 years and ≥ 30 pack-years, current or within the past 15 years). The nearest neighbour propensity-score matching was performed to generate a matched set. Kaplan-Meier curves were generated to compare survival among groups; differences in survival were analyzed using the stratified log-rank test. The mortality risk was estimated based on a Cox proportional hazards regression model and the robust standard error was calculated. Of 8110 patients, 37.4% and 24.3% met the extended USPSTF eligibility criteria and National Lung Cancer Screening Program (NLCSP) criteria, respectively. Overall mortality risk was not significantly different between the extended younger age group and the NLCSP group (hazard ratio [HR] [95% confidence interval (CI)]: 0.78 [0.59-1.02]). The extended older age group had a significantly higher mortality risk (HR [95% CI]: 1.41 [1.26-1.58]). Mortality risk was not significantly different between patients who smoked 20-29 pack-years and those who smoked ≥ 30 pack-years (HR [95% CI]: 0.90 [0.79-1.03]). Lung cancer patients aged 50-53 years and those with a 20-29 pack-years smoking history exhibited similar mortality risk to individuals meeting current criteria, while patients aged 75-80 years were at a higher risk of death. Although we verified similar or higher mortality risks in extended subgroups, a careful assessment of the benefits and harms of the screening tests is necessary when contemplating the extension of criteria.

Current Evidence for a Lung Cancer Screening Program

Background

Lung cancer screening is still in an early phase compared to other cancer screening programs, despite its high lethality particularly when diagnosed late. Achieving early diagnosis is crucial to obtain optimal outcomes.

Summary

In this review, we will address the current evidence on lung cancer screening through low-dose computed tomography (LDCT) and its impact on mortality reduction, existing screening recommendations, patient eligibility criteria, screening frequency and duration, benefits and harms, cost-effectiveness and some insights on lung cancer screening implementation and adoption. Additionally, new non-imaging, noninvasive biomarkers with high diagnostic potential are also briefly highlighted.

Key Messages

LDCT screening in a prespecified population based on age and smoking history proved to reduce lung cancer mortality. Optimization of the target population and management of LDCT pitfalls can further improve lung cancer screening efficiency and cost-effectiveness. Novel screening technologies and biomarkers being studied can potentially be game-changers in lung cancer screening and diagnosis.

Deep learning in distinguishing pulmonary nodules as benign and malignant

Background

Due to the high mortality of lung cancer, the aim was to find convolutional neural network models that can distinguish benign and malignant cases with high accuracy, which can help in early diagnosis with diagnostic imaging.

Methods

Patients who underwent tomography in our clinic and who were found to have lung nodules were retrospectively screened between January 2015 and December 2020. The patients were divided into two groups: benign (n=68; 38 males, 30 females; mean age: 59±12.2 years; range, 27 to 81 years) and malignant (n=29; 19 males, 10 females; mean age: 65±10.4 years; range, 43 to 88 years). In addition, a control group (n=67; 38 males, 29 females; mean age: 56.9±14.1 years; range, 26 to 81 years) consisting of healthy patients with no pathology in their sections was formed. Deep neural networks were trained with 80% of the three-class dataset we created and tested with 20% of the data. After the training of deep neural networks, feature extraction was done for these networks. The features extracted from the dataset were classified by machine learning algorithms. Performance results were obtained using confusion matrix analysis.

Results

After training deep neural networks, the highest accuracy rate of 80% was achieved with the AlexNET model among the models used. In the second stage results, obtained after feature extraction and using the classifier, the highest accuracy rate was achieved with the support vector machine classifier in the VGG19 model with 93.5%. In addition, increases in accuracy were noted in all models with the use of the support vector machine classifier.

Conclusion

Differentiation of benign and malignant lung nodules using deep learning models and feature extraction will provide important advantages for early diagnosis in radiology practice. The results obtained in our study support this view.

Risk-Adapted Starting Age of Personalized Lung Cancer Screening: A Population-Based, Prospective Cohort Study in China

BACKGROUND

The current one-size-fits-all screening strategy for lung cancer is not suitable for personalized screening.

RESEARCH QUESTION

What is the risk-adapted starting age of lung cancer screening with comprehensive consideration of risk factors?

STUDY DESIGN AND METHODS

The National Lung Cancer Screening program, a multicenter, population-based, prospective cohort study, was analyzed. Information on risk factor exposure was collected during the baseline risk assessment. A Cox proportional hazards model was used to estimate the association between risk factors and lung cancer incidence. Age-specific 10-year cumulative risk was calculated to determine the age at which individuals with various risk factors reached the equivalent risk level as individuals aged ≥ 50 years with active tobacco use and a ≥ 20 pack-year smoking history.

RESULTS

Of the 1,031,911 participants enrolled in this study, 3,908 demonstrated lung cancer after a median follow-up of 3.8 years. We identified seven risk factors for lung cancer, including pack-years of smoking, secondhand smoke exposure, family history of lung cancer in first-degree relatives, history of respiratory diseases, occupational hazardous exposure, BMI, and diabetes. The 10-year cumulative risk of lung cancer for people aged ≥ 50 years with active tobacco use and a ≥ 20 pack-year smoking history was 1.37%, which was treated as the risk threshold for screening. Individuals who never smoked and those with active tobacco use and a < 30-pack-year history of smoking reached the equivalent risk level 1 to 14 years later compared with the starting age of 50 years. Men with active tobacco use, a ≥ 30-pack-year history of smoking, and concurrent respiratory diseases or diabetes should be screened 1 year earlier at the age of 49 years.

INTERPRETATION

The personalized risk-adapted starting ages for lung cancer screening, based on the principle of equal management of equal risk, can served as an optimized screening strategy to identify high-risk individuals.

Five years of advances in electrochemical analysis of protein biomarkers in lung cancer: a systematic review

Lung cancer is the leading cause of cancer death in both men and women. It represents a public health problem that must be addressed through the early detection of specific biomarkers and effective treatment. To address this critical issue, it is imperative to implement effective methodologies for specific biomarker detection of lung cancer in real clinical samples. Electrochemical methods, including microfluidic devices and biosensors, can obtain robust results that reduce time, cost, and assay complexity. This comprehensive review will explore specific studies, methodologies, and detection limits and contribute to the depth of the discussion, making it a valuable resource for researchers and clinicians interested in lung cancer diagnosis.

High risk of lung cancer in surfactant-related gene variant carriers

BACKGROUND

Several rare surfactant-related gene (SRG) variants associated with interstitial lung disease are suspected to be associated with lung cancer, but data are missing. We aimed to study the epidemiology and phenotype of lung cancer in an international cohort of SRG variant carriers.

METHODS

We conducted a cross-sectional study of all adults with SRG variants in the OrphaLung network and compared lung cancer risk with telomere-related gene (TRG) variant carriers.

RESULTS

We identified 99 SRG adult variant carriers (SFTPA1 (n=18), SFTPA2 (n=31), SFTPC (n=24), ABCA3 (n=14) and NKX2-1 (n=12)), including 20 (20.2%) with lung cancer (SFTPA1 (n=7), SFTPA2 (n=8), SFTPC (n=3), NKX2-1 (n=2) and ABCA3 (n=0)). Among SRG variant carriers, the odds of lung cancer was associated with age (OR 1.04, 95% CI 1.01-1.08), smoking (OR 20.7, 95% CI 6.60-76.2) and SFTPA1/SFTPA2 variants (OR 3.97, 95% CI 1.39-13.2). Adenocarcinoma was the only histological type reported, with programmed death ligand-1 expression ≥1% in tumour cells in three samples. Cancer staging was localised (I/II) in eight (40%) individuals, locally advanced (III) in two (10%) and metastatic (IV) in 10 (50%). We found no somatic variant eligible for targeted therapy. Seven cancers were surgically removed, 10 received systemic therapy, and three received the best supportive care according to their stage and performance status. The median overall survival was 24 months, with stage I/II cancers showing better survival. We identified 233 TRG variant carriers. The comparative risk (subdistribution hazard ratio) for lung cancer in SRG patients versus TRG patients was 18.1 (95% CI 7.1-44.7).

CONCLUSIONS

The high risk of lung cancer among SRG variant carriers suggests specific screening and diagnostic and therapeutic challenges. The benefit of regular computed tomography scan follow-up should be evaluated.

Determination of Tumor Marker Screening for Lung Cancer Using ROC Curves

Introduction

Lung cancer ranks first among malignant tumors worldwide and is a leading cause of cancer-related mortality in both men and women. Combining tumor marker testing is a strategy to screen individuals at high risk of pulmonary cancer and minimize pulmonary cancer mortality. Therefore, tumor marker screening is crucial. In this study, we analyzed combinations of tumor markers for lung cancer screening using receiver operating characteristic (ROC) curve analysis.

Methods

A retrospective descriptive study was conducted on patients diagnosed with lung cancer, as well as healthy and benign lung diseases, using data from the China Huludao Central Hospital database between January 2016 and July 2022. The t-test and ROC curve were utilized to assess the effectiveness of individual tumor marker and the combination of multiple tumor markers. Tumor markers are molecular products metabolized and secreted by tumor tissues, characterized by cells or body fluids. They serve as indicators of tumor stage and grading, monitor treatment response, and predict recurrence.

Results

In this study, 267 healthy participants, 385 patients with benign lesions, and 296 patients with lung cancer underwent tumor marker screening. The sensitivity of five tumor markers-CEA, CYFRA21-1, NSE, pro-GRP, and CA125-was found to be <55%. This study revealed that a single tumor marker had limited value in lung cancer screening. However, combining two or more markers yielded varying area under the curves (AUC), with no significant impact on screening accuracy. The combination of CEA + CA125 demonstrated the highest accuracy for lung cancer screening in healthy participants. At a cutoff of 0.447 for CEA + CA125, the combination showed a sensitivity of 0.676 and specificity of 0.846 for lung cancer screening. Conversely, for patients with benign lung lesions, the optimal combination was CEA + NSE, with a cutoff of 0.393, yielding a sensitivity of 0.645 and specificity of 0.766 for lung cancer screening.

Conclusion

The five tumor markers-CEA, CA125, CY211, NSE, GRP-show promising results in screening healthy individuals and patients with lung cancer. However, only CEA, NSE, and GRP effectively differentiate patients with benign lung lesions from those with lung cancer. A single tumor marker has limited utility in detecting and screening for lung cancer and should be combined with other tumor markers. CEA + CA125 emerges as a superior tumor marker for distinguishing healthy individuals from those with lung cancer, whereas the CEA + NSE combination is more effective in identifying tumor markers in patients with benign lung lesions and lung cancer.

Increased monocytic myeloid-derived suppressor cells in type 2 diabetes correlate with hyperglycemic and was a risk factor of infection and tumor occurrence

To investigate the frequency of monocytic myeloid-derived suppressor cells (M-MDSCs) in type 2 diabetes mellitus (T2DM) patients and explore the potential associations between M-MDSCs, glycemic control, and the occurrence of infections and tumor. 102 healthy and 77 T2DM individuals were enrolled. We assessed the M-MDSCs frequency, levels of fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), and other relevant indicators. Each patient underwent a follow-up of at least 6 months after M-MDSCs detection. The M-MDSCs frequency was significantly higher in patients with poor glycemic control (PGC) compared to the healthy population (P < 0.001), whereas there was no significant difference between patients with good glycemic control and the healthy (P > 0.05). There was a positive correlation between the M-MDSCs frequency and FPG, HbA1c (R = 0.517 and 0.315, P < 0.001, respectively). T2DM patients with abnormally increased M-MDSCs have a higher incidence of infection and tumor (48.57% and 11.43% respectively). Our results shed new light on the pathogenesis of T2DM, help to understand why T2DM patients are susceptible to infection and tumor and providing novel insights for future prevention and treatment of T2DM.

Discovery of lung adenocarcinoma tumor antigens and ferroptosis subtypes for developing mRNA vaccines

mRNA vaccines are becoming a feasible alternative for treating cancer. To develop mRNA vaccines against LUAD, potential antigens were identified and LUAD ferroptosis subtypes distinguished for selecting appropriate patients. The genome expression omnibus, cancer genome atlas (TCGA) and FerrDB were used to collect gene expression profiles, clinical information, and the genes involved in ferroptosis, respectively. cBioPortal was used to visualize and compare genetic alterations, GEPIA2 to calculate prognostic factors of the selected antigens, and TIMER to visualize the relationship between potential antigens and tumor immune cell infiltration. Consensus clustering analysis was utilized to identify ferroptosis subtypes and their prognostic value assessed by Log-rank and cox regression tests. The modules of ferroptosis-related gene screening were conducted by weight gene co-expression network analysis. The LUAD ferroptosis landscape was visualized through dimensionality reduction and graph learning. Six tumor antigens had obvious LUAD-mutations, positively correlated with different antigen-presenting cells, and might induce tumor cell ferroptosis. LUAD patients were stratified into three ferroptosis subtypes (FS1, FS2, and FS3) according to diverse molecular, cellular, and clinical characteristics. FS3 showed the highest tumor mutation burden and the most somatic mutations, deemed potential indicators of mRNA vaccine effectiveness. Moreover, different ferroptosis subtypes expressed distinct immune checkpoints and immunogenic cell death modulators. AGPS, NRAS, MTDH, PANX1, NOX4, and PPARD are potentially suitable for mRNA vaccinations against LUAD, specifically in patients with FS3 tumors. This study defines vaccination candidates and establishes a theoretical basis for LUAD mRNA vaccinations.

Classification of benign and malignant pulmonary nodule based on local-global hybrid network

BACKGROUND

The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules.

OBJECTIVE

In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules.

METHODS

First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features.

RESULTS

Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%.

CONCLUSION

The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

A quantitative evaluation method utilizing the homology concept to assess the state of chromatin within the nucleus of lung cancer

Homology is a mathematical tool to quantify "the contact degree", which can be expressed in terms of Betti numbers. The Betti numbers used in this study consisted of two numbers, b0 (a zero-dimensional Betti number) and b1 (a one-dimensional Betti number). We developed a chromatin homology profile (CHP) method to quantify the chromatin contact degree based on this mathematical tool. Using the CHP method we analyzed the number of holes (surrounded areas = b1 value) formed by the chromatin contact and calculated the maximum value of b1 (b1MAX), the value of b1 exceeding 5 for the first time or Homology Value (HV), and the chromatin density (b1MAX/ns2). We attempted to detect differences in chromatin patterns and differentiate histological types of lung cancer from respiratory cytology using these three features. The HV of cancer cells was significantly lower than that of non-cancerous cells. Furthermore, b1MAX and b1MAX/ns2 showed significant differences between small cell and non-small cell carcinomas and between adenocarcinomas and squamous cell carcinomas, respectively. We quantitatively analyzed the chromatin patterns using homology and showed that the CHP method may be a useful tool for differentiating histological types of lung cancer in respiratory cytology.

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath

BACKGROUND

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD.

RESEARCH QUESTION

Can eNose technology be used for prospective detection of early lung cancer in patients with COPD?

STUDY DESIGN AND METHODS

BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis.

RESULTS

Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95).

INTERPRETATION

Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

Association between number of dissected lymph nodes and survival in patients undergoing resection for clinical stage IA pure solid lung adenocarcinoma: a retrospective analysis

BACKGROUND

Lymph node dissection is essential for staging of pure solid lung adenocarcinoma and selection of treatment after surgical resection, particularly for stage I disease since the rate of lymph node metastasis can vary from 0 to 23.7%.

METHODS

We retrospectively screened all adult patients (18 years of age or older) who underwent lobectomy for pure solid cT1N0M0 lung adenocarcinoma between January 2015 and December 2017 at our center. Cox proportional hazard regression was used to assess the association between the number of dissected lymph nodes and recurrence-free survival (RFS) and to determine the optimal number of dissected lymph nodes.

RESULTS

The final analysis included 458 patients (age: 60.26 ± 8.07 years; 241 women). RFS increased linearly with an increasing number of dissected lymph nodes at a range between 0 and 9. Kaplan-Meier analysis revealed significantly longer RFS in patients with ≥ 9 vs. <9 dissected lymph nodes. In subgroup analysis, ≥ 9 dissected lymph nodes was not only associated with longer RFS in patients without lymph node metastasis (n = 332) but also in patients with metastasis (n = 126). In multivariate Cox proportional hazard regression, ≥ 9 dissected lymph nodes was independently associated with longer RFS (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.26 to 0.73; P = 0.002).

CONCLUSIONS

≥9 Dissected lymph nodes was associated with longer RFS; accordingly, we recommend dissecting 9 lymph nodes in patients undergoing lobectomy for stage IA pure solid lung adenocarcinoma.

A comparative study of life-years lost attributable to air particulate matter in Asia-Pacific and European countries

Air particulate matter (PM) and its harmful effects on human health are of great concern globally due to all-cause and cause-specific mortality impacts across different population groups. While Europe has made significant progress in reducing particulate air pollution-related mortality through innovative technologies and policies, many countries in Asia-Pacific region still rely on high-polluting technologies and have yet to implement effective policies to address this issue, resulting in higher levels of mortality due to air pollution in the region. This study has three aims related to quantifying life-years lost (LYL) attributable to PM, and further separated into ambient PM and household air pollution (HAP): (1) to investigate LYL by causes of death; (2) to compare LYL between Asia-Pacific (APAC) and Europe; and (3) to assess LYL across different socio-demographic index (SDI) countries. The data used come from the Institute for Health Metrics and Evaluation (IHME) and Health Effects Institute (HEI). Our results show that average LYL due to PM in APAC was greater than in Europe, with some Pacific island countries particularly affected by the exposure to HAP. Three quarters of LYL came from premature deaths by ischemic heart disease and stroke, in both continents. There were significant differences between SDI groups for causes of death due to ambient PM and HAP. Our findings call for urgent improvement of clean air to reduce indoor and outdoor air pollution-related mortality in the APAC region.

Impact of previous extra-pulmonary malignancies on surgical outcomes of sequential primary non-small cell lung cancer

Reduced cancer deaths have led to an increase in the number of cancer survivors and the risk of the second primary tumor. This study explored the surgical outcomes of patients with non-small cell lung cancer as the second primary tumor and the impact of previous extra-pulmonary malignancies. Patients' data were obtained from Surveillance, Epidemiology and End Results database. The patients were divided into lung surgery and non-surgery groups. Propensity-score matching was used to balance potential confounders. Kaplan-Meier curves were generated to test the overall survival and lung-cancer-specific survival. Cox regression analysis was performed to calculate death risk. In total 3054 lung surgery and 1094 non-surgery patients with stage I-II non-small cell lung cancer as the second primary tumor were included. The surgery group showed longer overall survival (68 vs. 22 months) and lung cancer-specific survival (not reached vs. 37 months) than those of non-surgery groups (both P < 0.001). Patients with previous hormone-dependent malignancies had similar survival rates (overall survival: 22 vs. 20 months, P = 0.666; lung cancer-specific survival: 38 vs. 37 months, P = 0.292) as those with non-hormone dependent malignancies in the non-surgery group. Significantly longer overall survival (90 vs. 60 months, P = 0.001) was observed in patients with hormone-dependent malignancies in the surgery group; however, there was no difference in lung cancer-specific survival (P = 0.225). Competing risk analysis showed that for patients undergoing lung surgery, there was higher previous malignancy-induced mortality in patients with non-hormone dependent malignancies than in patients with hormone-dependent malignancies. However, there was no difference in lung cancer-induced mortality between the two groups. Patients who underwent lobectomy showed longer survival than those who underwent pneumonectomy and other resection types (89, 27.5 and 65 months, P < 0.001). In summary, lung surgery is beneficial for patients with stage I-II non-small cell lung cancer as the second primary tumor after hormone-dependent malignancy resection.

Initial development and testing of an exhaled microRNA detection strategy for lung cancer case-control discrimination

For detecting field carcinogenesis non-invasively, early technical development and case-control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically "fingerprinted", using paired EBC, upper and lower airway donor sample sets. A clinic-based case-control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case-control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1-2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.

The influence of the COVID-19 pandemic on lung cancer surgery in Queensland

BACKGROUND

The coronavirus disease-19 (COVID-19) pandemic poses unprecedented challenges to global healthcare. The contemporary influence of COVID-19 on the delivery of lung cancer surgery has not been examined in Queensland.

METHODS

We performed a retrospective registry analysis of the Queensland Cardiac Outcomes Registry (QCOR), thoracic database examining all adult lung cancer resections across Queensland from 1/1/2016 to 30/4/2022. We compared the data prior to, and after, the introduction of COVID-restrictions.

RESULTS

There were 1207 patients. Mean age at surgery was 66 years and 1115 (92%) lobectomies were performed. We demonstrated a significant delay from time of diagnosis to surgery from 80 to 96 days (P < 0.0005), after introducing COVID-restrictions. The number of surgeries performed per month decreased after the pandemic and has not recovered (P = 0.012). 2022 saw a sharp reduction in cases with 49 surgeries, compared to 71 in 2019 for the same period.

CONCLUSION

Restrictions were associated with a significant increase in pathological upstaging, greatest immediately after the introduction of COVID-restrictions (IRR 1.71, CI 0.93-2.94, P = 0.05). COVID-19 delayed the access to surgery, reduced surgical capacity and consequently resulted in pathological upstaging throughout Queensland.

Education messages and strategies to inform the public, potential screening candidates and healthcare providers about lung cancer screening: A systematic review

International lung cancer screening (LCS) trials, using low-dose computed tomography, have demonstrated clinical effectiveness in reducing mortality from lung cancer. This systematic review aims to synthesise the key messages and strategies that could be successful in increasing awareness and knowledge of LCS, and ultimately increase uptake of screening. Studies were identified via relevant database searches up to January 2022. Two authors evaluated eligible studies, extracted and crosschecked data, and assessed quality. Results were synthesised narratively. Of 3205 titles identified, 116 full text articles were reviewed and 22 studies met the inclusion criteria. Twenty studies were conducted in the United States. While the study findings were heterogenous, key messages mentioned across multiple studies were about: provision of information on LCS and the recommendations for LCS (n = 8); benefits and harms of LCS (n = 6); cost of LCS and insurance coverage for participants (n = 6) and eligibility criteria (n = 5). To increase knowledge and awareness, evidence from awareness campaigns suggests that presenting information about eligibility and the benefits and harms of screening, may increase screening intention and uptake. Evidence from behavioural studies suggests that campaigns supporting engagement with platforms such as educational videos and digital awareness campaigns might be most effective. Group based learning appears to be most suited to increasing health professionals' knowledge. This systematic review found a lack of consistent evidence to demonstrate which strategies are most effective for increasing participant healthcare professional and community awareness and education about LCS.

Low-dose versus standard-dose computed tomography-guided biopsy for pulmonary nodules: a randomized controlled trial

BACKGROUND

To assess relative safety and diagnostic performance of low- and standard-dose computed tomography (CT)-guided biopsy for pulmonary nodules (PNs).

MATERIALS AND METHODS

This was a single-center prospective randomized controlled trial (RCT). From June 2020 to December 2020, consecutive patients with PNs were randomly assigned into low- or standard-dose groups. The primary outcome was diagnosis accuracy. The secondary outcomes included technical success, diagnostic yield, operation time, radiation dose, and biopsy-related complications. This RCT was registered on 3 January 2020 and listed within ClinicalTrials.gov (NCT04217655).

RESULTS

Two hundred patients were randomly assigned to low-dose (n = 100) and standard-dose (n = 100) groups. All patients achieved the technical success of CT-guided biopsy and definite final diagnoses. No significant difference was found in operation time (n = 0.231) between the two groups. The mean dose-length product was markedly reduced within the low-dose group compared to the standard-dose group (31.5 vs. 333.5 mGy-cm, P < 0.001). The diagnostic yield, sensitivity, specificity, and accuracy of the low-dose group were 68%, 91.5%, 100%, and 94%, respectively. The diagnostic yield, sensitivity, specificity, and accuracy were 65%, 88.6%, 100%, and 92% in the standard-dose group. There was no significant difference observed in diagnostic yield (P = 0.653), diagnostic accuracy (P = 0.579), rates of pneumothorax (P = 0.836), and lung hemorrhage (P = 0.744) between the two groups.

CONCLUSIONS

Compared with standard-dose CT-guided biopsy for PNs, low-dose CT can significantly reduce the radiation dose, while yielding comparable safety and diagnostic accuracy.

Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement

PURPOSE

Lung cancer screening programs generate a high volume of low-dose computed tomography (LDCT) reports that contain valuable information, typically in a free-text format. High-performance named-entity recognition (NER) models can extract relevant information from these reports automatically for inter-radiologist quality control.

METHODS

Using LDCT report data from a longitudinal lung cancer screening program (8,305 reports; 3,124 participants; 2006-2019), we trained a rule-based model and two bidirectional long short-term memory (Bi-LSTM) NER neural network models to detect clinically relevant information from LDCT reports. Model performance was tested using F1 scores and compared with a published open-source radiology NER model (Stanza) in an independent evaluation set of 150 reports. The top performing model was applied to a data set of 6,948 reports for an inter-radiologist quality control assessment.

RESULTS

The best performing model, a Bi-LSTM NER recurrent neural network model, had an overall F1 score of 0.950, which outperformed Stanza (F1 score = 0.872) and a rule-based NER model (F1 score = 0.809). Recall (sensitivity) for the best Bi-LSTM model ranged from 0.916 to 0.991 for different entity types; precision (positive predictive value) ranged from 0.892 to 0.997. Test performance remained stable across time periods. There was an average of a 2.86-fold difference in the number of identified entities between the most and the least detailed radiologists.

CONCLUSION

We built an open-source Bi-LSTM NER model that outperformed other open-source or rule-based radiology NER models. This model can efficiently extract clinically relevant information from lung cancer screening computerized tomography reports with high accuracy, enabling efficient audit and feedback to improve quality of patient care.

Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies

BACKGROUND

Early detection of cancer offers the opportunity to identify candidates when curative treatments are achievable. The THUNDER study (THe UNintrusive Detection of EaRly-stage cancers, NCT04820868) aimed to evaluate the performance of ELSA-seq, a previously described cfDNA methylation-based technology, in the early detection and localization of six types of cancers in the colorectum, esophagus, liver, lung, ovary and pancreas.

PATIENTS AND METHODS

A customized panel of 161,984 CpG sites was constructed and validated by public and in-house (cancer: n=249; non-cancer: n=288) methylome data, respectively. The cfDNA samples from 1,693 participants (cancer: n=735; non-cancer: n=958) were retrospectively collected to train and validate two multi-cancer detection blood test models (MCDBT-1/2) for different clinical scenarios. The models were validated on a prospective and independent cohort of age-matched 1,010 participants (cancer: n=505; non-cancer: n=505). Simulation using the cancer incidence in China was applied to infer stage-shift and survival benefits to demonstrate the potential utility of the models in the real world.

RESULTS

MCDBT-1 yielded a sensitivity of 69.1% (64.8%‒73.3%), a specificity of 98.9% (97.6%‒99.7%) and tissue origin accuracy of 83.2% (78.7%‒87.1%) in the independent validation set. For early stage (I‒III) patients, the sensitivity of MCDBT-1 was 59.8% (54.4%‒65.0%). In the real-world simulation, MCDBT-1 achieved the sensitivity of 70.6% in detecting the six cancers, thus decreasing late-stage incidence by 38.7%‒46.4%, and increasing 5-year survival rate by 33.1%‒40.4%, respectively. In parallel, MCDBT-2 was generated at a slightly low specificity of 95.1% (92.8%-96.9%) but a higher sensitivity of 75.1% (71.9%-79.8%) than MCDBT-1 for populations at relatively high risk of cancers, and also had ideal performance.

CONCLUSION

In this large-scale clinical validation study, MCDBT-1/2 models showed a high sensitivity, specificity, and accuracy of predicted origin in detecting six types of cancers.

LC-MS/MS Based Volatile Organic Compound Biomarkers Analysis for Early Detection of Lung Cancer

(1) Background: lung cancer is the world's deadliest cancer, but early diagnosis helps to improve the cure rate and thus reduce the mortality rate. Annual low-dose computed tomography (LD-CT) screening is an efficient lung cancer-screening program for a high-risk population. However, LD-CT has often been characterized by a higher degree of false-positive results. To meet these challenges, a volatolomic approach, in particular, the breath volatile organic compounds (VOCs) fingerprint analysis, has recently received increased attention for its application in early lung cancer screening thanks to its convenience, non-invasiveness, and being well tolerated by patients. (2) Methods: a LC-MS/MS-based volatolomics analysis was carried out according to P/N 5046800 standard based breath analysis of VOC as novel cancer biomarkers for distinguishing early-stage lung cancer from the healthy control group. The discriminatory accuracy of identified VOCs was assessed using subject work characterization and a random forest risk prediction model. (3) Results: the proposed technique has good performance compared with existing approaches, the differences between the exhaled VOCs of the early lung cancer patients before operation, three to seven days after the operation, as well as four to six weeks after operation under fasting and 1 h after the meal were compared with the healthy controls. The results showed that only 1 h after a meal, the concentration of seven VOCs, including 3-hydroxy-2-butanone (TG-4), glycolaldehyde (TG-7), 2-pentanone (TG-8), acrolein (TG-11), nonaldehyde (TG-19), decanal (TG-20), and crotonaldehyde (TG-22), differ significantly between lung cancer patients and control, with the invasive adenocarcinoma of the lung (IAC) having the most significant difference. (4) Conclusions: this novel, non-invasive approach can improve the detection rate of early lung cancer, and LC-MS/MS-based breath analysis could be a promising method for clinical application.

Potential Impact of Criteria Modifications on Race and Sex Disparities in Eligibility for Lung Cancer Screening

INTRODUCTION

Low-dose computed tomography (LDCT) screening reduces lung cancer mortality, but current eligibility criteria underestimate risk in women and racial minorities. We evaluated the impact of screening criteria modifications on LDCT eligibility and lung cancer detection.

METHODS

Using data from a Lung Nodule Program, we compared persons eligible for LDCT by the following: U.S. Preventive Services Task Force (USPSTF) 2013 criteria (55-80 y, ≥30 pack-years of smoking, and ≤15 y since cessation); USPSTF2021 criteria (50-80 y, ≥20 pack-years of smoking, and ≤15 y since cessation); quit duration expanded to less than or equal to 25 years (USPSTF2021-QD25); reducing the pack-years of smoking to more than or equal to 10 years (USPSTF2021-PY10); and both (USPSTF2021-QD25-PY10). We compare across groups using the chi-square test or analysis of variance.

RESULTS

The 17,421 individuals analyzed were of 56% female sex, 69% white, 28% black; 13% met USPSTF2013 criteria; 17% USPSTF2021; 18% USPSTF2021-QD25; 19% USPSTF2021-PY10; and 21% USPSTF2021-QD25-PY10. Additional eligible individuals by USPSTF2021 (n = 682) and USPSTF2021-QD25-PY10 (n = 1402) were 27% and 29% black, both significantly higher than USPSTF2013 (17%, p < 0.0001). These additional eligible individuals were 55% (USPSTF2021) and 55% (USPSTF2021-QD25-PY10) of female sex, compared with 48% by USPSTF2013 (p < 0.05). Of 1243 persons (7.1%) with lung cancer, 22% were screening eligible by USPSTF13. USPSTF2021-QD25-PY10 increased the total number of persons with lung cancer by 37%. These additional individuals with lung cancer were of 57% female sex (versus 48% with USPSTF2013, p = 0.0476) and 24% black (versus 20% with USPSTF2013, p = 0.3367).

CONCLUSIONS

Expansion of LDCT screening eligibility criteria to allow longer quit duration and fewer pack-years of exposure enriches the screening-eligible population for women and black persons.

Analyzing bronchoalveolar fluid derived small extracellular vesicles using single-vesicle SERS for non-small cell lung cancer detection

An emerging body of research by biologists and clinicians has demonstrated the clinical application of small extracellular vesicles (sEVs, also commonly referred to as exosomes) as biomarkers for cancer detections. sEVs isolated from various body fluids such as blood, saliva, urine, and cerebrospinal fluid have been used for biomarker discoveries with highly encouraging outcomes. Among the biomarkers discovered are those responsible for multiple cancer types and immune responses. These biomarkers are recapitulated from the tumor microenvironments. Yet, despite numerous discussions of sEVs in scientific literature, sEV-based biomarkers have so far played only a minor role for cancer diagnostics in the clinical setting, notably less so than other techniques such as imaging and biopsy. In this paper, we report the results of a pilot study (n = 10 from each of the patient and the control group) using bronchoalveolar lavage fluid to determine the presence of sEVs related to non-small cell lung cancer in twenty clinical samples examined using surface enhanced Raman spectroscopy (SERS).

The Effect of Body Mass Index on Survival in Lung Cancer

The prognostic significance of body mass index in lung cancer and the direction of this relationship are not yet clear. This study aimed to evaluate the relationship between BMI and overall survival time of advanced-stage lung cancer patients treated in a center in Turkey, a developing country. In this study, the data of 225 patients diagnosed with stage III or stage IV lung cancer between 2016 and 2020 were analyzed. The effects of BMI and other variables on survival were examined by Cox regression analysis for NSCLC and SCLC. For NSCLC and SCLC, being underweight compared to the normal group, being diagnosed at a more advanced stage, and having a worse performance score were associated with a significantly higher risk of death. Other variables significantly associated with survival were gender, type of radiotherapy for NSCLC, age group, and family history for SCLC. This study showed that being underweight relative to the normal group was associated with worse survival for NSCLC and SCLC but did not support the obesity paradox. Studies that are representative of all BMI categories and free of bias are needed to understand the BMI-lung cancer survival relationship clearly.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BACKGROUND

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.

METHODS

Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.

RESULTS

Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.

CONCLUSION

CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Lobectomy Demonstrates Superior Survival Than Segmentectomy for High-Grade Non-Small Cell Lung Cancer: The National Cancer Database Analysis

BACKGROUND

Current recommendations for segmentectomy for non-small cell lung cancer (NSCLC) include size ≤2 cm, margins ≥ 2 cm, and no nodal involvement. This study further stratifies the selection criteria for segmentectomy using the National Cancer Database (NCDB).

METHODS

The NCDB was queried for patients with high-grade (poorly/undifferentiated) T1a/b peripheral NSCLC (tumor size ≤2 cm), who underwent either lobectomy or segmentectomy. Patients with pathologic node-positive disease or who received neoadjuvant/adjuvant treatments were excluded. Propensity score analysis was used to adjust for differences in pretreatment characteristics.

RESULTS

11 091 patients were included with 10 413 patients (93.9%) treated with lobectomy and 678 patients (6.1%) underwent segmentectomy. In a propensity matched pair analysis of 1282 patients, lobectomy showed significantly improved median survival of 88.48 months vs 68.30 months for segmentectomy, P = .004. On multivariate Cox regression, lobectomy was associated with significantly improved survival (hazard ratio (HR): .81, 95% CI .72-.92, P = .001). Subgroup analysis of propensity score matched patients with a Charlson-Deyo comorbidity score (CDCC) of 0 also demonstrated a trend of improved survival with lobectomy.

DISCUSSION

Lobectomy may confer significant survival advantage over segmentectomy for high-grade NSCLC (≤2 cm). More work is needed to further stratify various NSCLC histologies with their respective grades allowing more comprehensive selection criteria for segmentectomy.

Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3-523) days, longer than that for radiologists (8 (0-263) days). The AI model can assist radiologists in the early detection of lung nodules.

A global burden assessment of lung cancer attributed to residential radon exposure during 1990-2019

This study aimed to explore the spatial and temporal trends of lung cancer burden attributable to residential radon exposure at the global, regional, and national levels. Based on the Global Burden of Disease Study (GBD) 2019, we collected the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life rate (ASDR) of lung cancer attributable to residential radon exposure from 1990 to 2019. The Joinpoint model was used to calculate the annual average percentage change (AAPC) to evaluate the trend of ASMR and ASDR from 1990 to 2019. The locally weighted regression (LOESS) was used to estimate the relationship of the socio-demographic index (SDI) with ASMR and ASDR. In 2019, the global ASMR and ASDR for lung cancer attributable to residential radon exposure were 1.03 (95% CI: 0.20, 2.00) and 22.66 (95% CI: 4.49, 43.94) per 100 000 population, which were 15.6% and 23.0% lower than in 1990, respectively. According to the estimation, we found the lung cancer burden attributable to residential radon exposure declined significantly in high and high-middle SDI regions, but substantially increased in middle and low-middle SDI regions from 1990 to 2019. Across age and sex, the highest burden of lung cancer attributable to residential radon exposure was found in males and elderly groups. In conclusion, the global burden of lung cancer attributable to residential radon exposure showed a declining trend from 1990 to 2019, but a relatively large increase was found in the middle SDI regions. In 2019, the burden of lung cancer attributable to residential radon exposure remained high, particularly in males, the elderly, and high-middle SDI regions compared with other groups.

The Emerging Roles and Clinical Potential of circSMARCA5 in Cancer

Circular RNAs (circRNAs) are a type of endogenous non-coding RNA and a critical epigenetic regulation way that have a closed-loop structure and are highly stable, conserved, and tissue-specific, and they play an important role in the development of many diseases, including tumors, neurological diseases, and cardiovascular diseases. CircSMARCA5 is a circRNA formed by its parental gene SMARCA5 via back splicing which is dysregulated in expression in a variety of tumors and is involved in tumor development with dual functions as an oncogene or tumor suppressor. It not only serves as a competing endogenous RNA (ceRNA) by binding to various miRNAs, but it also interacts with RNA binding protein (RBP), regulating downstream gene expression; it also aids in DNA damage repair by regulating the transcription and expression of its parental gene. This review systematically summarized the expression and characteristics, dual biological functions, and molecular regulatory mechanisms of circSMARCA5 involved in carcinogenesis and tumor progression as well as the potential applications in early diagnosis and gene targeting therapy in tumors.

The Journey for Lung Cancer Screening where we Stand Today

BACKGROUND

Lung cancer remains a leading cause of cancer mortality worldwide with many patients presenting with advanced disease.

OBJECTIVE

We reviewed the available literature for lung cancer screening using low dose computed tomography (LDCT). We reviewed the National Lung Screening Trial (NLST), Early Lung Cancer Action Program (ELCAP) and the (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trials. We also look at different lung cancer risk prediction models that may aid in identifying target populations and also discuss the cost-effectiveness of LDCT screening in different groups of smokers and ex-smokers. Lastly, we discuss recent guideline changes that have occurred in line with new and emerging evidence on lung cancer screening.

CONCLUSION

LDCT has been shown reduce lung cancer mortality in certain groups of current and former smokers and should be considered to help in the early diagnosis of lung cancer.

Estimation of the Number of Individuals Living With Metastatic Cancer in the United States

BACKGROUND

The purpose of this study was to estimate the number of individuals living with metastatic breast, prostate, lung, colorectal, or bladder cancer or metastatic melanoma in the United States using population-based data.

METHODS

A back-calculation method was used to estimate the number of individuals living with metastatic cancer for each cancer type from US cancer mortality and survival statistics from the Surveillance, Epidemiology, and End Results registries. The percentages of those living with metastatic cancer who advanced to metastatic disease from early stage cancer vs who were diagnosed with metastatic cancer de novo were calculated. One- and 5-year relative survival rates for de novo metastatic cancer were compared by year of diagnosis to assess time trends in survival.

RESULTS

It is estimated that, in 2018, 623 405 individuals were living with metastatic breast, prostate, lung, colorectal, or bladder cancer, or metastatic melanoma in the United States. This number is expected to increase to 693 452 in 2025. In 2018, the percentage of metastatic cancer survivors who were initially diagnosed with early stage cancer and advanced to metastatic cancer ranged from 30% for lung cancer to 72% for bladder cancer.

CONCLUSIONS

This study demonstrates increasing numbers of individuals living with metastatic cancer of the 6 most common cancer types in the United States. This information is critical for informing the allocation of research efforts and healthcare infrastructure needed to address the needs of these individuals.

Identification of HMGB2 associated with proliferation, invasion and prognosis in lung adenocarcinoma via weighted gene co-expression network analysis

Background

High mobility group protein B2 (HMGB2) is a multifunctional protein that plays various roles in different cellular compartments. Moreover, HMGB2 serves as a potential prognostic biomarker and therapeutic target for lung adenocarcinoma (LUAD).

Methods

In this study, the expression pattern, prognostic implication, and potential role of HMGB2 in LUAD were evaluated using the integrated bioinformatics analyses based on public available mRNA expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus databases, both at the single-cell level and the tissue level. Further study in the patient-derived samples was conducted to explore the correlation between HMGB2 protein expression levels with tissue specificity, (tumor size-lymph node-metastasis) TNM stage, pathological grade, Ki-67 status, and overall survival. In vitro experiments, such as CCK-8, colony-formation and Transwell assay, were performed with human LUAD cell line A549 to investigate the role of HMGB2 in LUAD progression. Furthermore, xenograft tumor model was generated with A549 in nude mice.

Results

The results showed that the HMGB2 expression was higher in the LUAD samples than in the adjacent normal tissues and was correlated with high degree of malignancy in different public data in this study. Besides, over-expression of HMGB2 promoted A549 cells proliferation and migration while knocking down of HMGB2 suppressed the tumor promoting effect.

Conclusions

Our study indicated that HMGB2 was remarkably highly expressed in LUAD tissues, suggesting that it is a promising diagnostic and therapeutic marker for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02110-y.

Pan-cancer analysis of the prevalence and associated factors of lung metastasis and the construction of the lung metastatic classification system

This study first presents an analysis of the prevalence and associated factors of the lung metastasis (LM) database and then uses this analysis to construct an LM classification system. Using cancer patient data gathered from the surveillance, epidemiology, and end results (SEER) database, this study shows that the prevalence of LM is not consistent among different cancers; that is, the prevalence of LM ranges from 0.0013 [brain; 95% confidence interval (95% CI); 0.0010–0.0018] to 0.234 (“other digestive organs”; 95% CI; 0.221–0.249). This study finds that advanced age, poor grade, higher tumor or node stage, and metastases including bone, brain, and liver are positively related to LM occurrence, while female gender, income, marital status, and insured status are negatively related. Then, this study generates four categories from 58 cancer types based on prevalence and influence factors and satisfactorily validates these. This classification system reflects the LM risk of different cancers. It can guide individualized treatment and the management of these synchronous metastatic cancer patients and help clinicians better distribute medical resources.

Electrical Impedance Analysis for Lung Cancer: A Prospective, Multicenter, Blind Validation Study

Hypothesis

Patients with cancer have different impedances or conductances than patients with benign normal tissue; thus, we can apply electrical impedance analysis (EIA) to identify patients with cancer.

Method

To evaluate EIA’s efficacy and safety profile in diagnosing pulmonary lesions, we conducted a prospective, multicenter study among patients with pulmonary lesions recruited from 4 clinical centers (Zhongshan Hospital Ethics Committee, Approval No. 2015-16R and 2017-035(3). They underwent EIA to obtain an Algorithm Composite Score or ‘Prolung Index,’ PI. The classification threshold of 29 was first tested in an analytical validation set of 144 patients and independently validated in a clinical validation set of 418 patients. The subject’s final diagnosis depended on histology and a 2-year follow-up.

Results

In total, 418 patients completed the entire protocol for clinical validation, with 186 true positives, 145 true negatives, 52 false positives, and 35 false negatives. The sensitivity, specificity, and diagnostic yield were 84% (95% CI 79.3%-89.0%), 74% (95% CI 67.4%-79.8%), and 79% (95%CI 75.3%-83.1%), respectively, and did not differ according to age, sex, smoking history, body mass index, or lesion types. The sensitivity of small lesions was comparable to that of large lesions (p = 0.13). Four hundred eighty-four patients who underwent the analysis received a safety evaluation. No adverse events were considered to be related to the test.

Conclusion

Electrical impedance analysis is a safe and efficient tool for risk stratification of pulmonary lesions, especially for patients with a suspicious lung lesion.

Cell-by-Cell: Unlocking Lung Cancer Pathogenesis

For lung cancers, cellular trajectories and fates are strongly pruned by cell intrinsic and extrinsic factors. Over the past couple of decades, the combination of comprehensive molecular and genomic approaches, as well as the use of relevant pre-clinical models, enhanced micro-dissection techniques, profiling of rare preneoplastic lesions and surrounding tissues, as well as multi-region tumor sequencing, have all provided in-depth insights into the early biology and evolution of lung cancers. The advent of single-cell sequencing technologies has revolutionized our ability to interrogate these same models, tissues, and cohorts at an unprecedented resolution. Single-cell tracking of lung cancer pathogenesis is now transforming our understanding of the roles and consequences of epithelial-microenvironmental cues and crosstalk during disease evolution. By focusing on non-small lung cancers, specifically lung adenocarcinoma subtype, this review aims to summarize our knowledge base of tumor cells-of-origin and tumor-immune dynamics that have been primarily fueled by single-cell analysis of lung adenocarcinoma specimens at various stages of disease pathogenesis and of relevant animal models. The review will provide an overview of how recent reports are rewriting the mechanistic details of lineage plasticity and intra-tumor heterogeneity at a magnified scale thanks to single-cell studies of early- to late-stage lung adenocarcinomas. Future advances in single-cell technologies, coupled with analysis of minute amounts of rare clinical tissues and novel animal models, are anticipated to help transform our understanding of how diverse micro-events elicit macro-scale consequences, and thus to significantly advance how basic genomic and molecular knowledge of lung cancer evolution can be translated into successful targets for early detection and prevention of this lethal disease.

Pulmonary Lesion Classification Framework Using the Weighted Ensemble Classification with Random Forest and CNN Models for EBUS Images

Lung cancer is a deadly disease with a high mortality rate. Endobronchial ultrasonography (EBUS) is one of the methods for detecting pulmonary lesions. Computer-aided diagnosis of pulmonary lesions from images can help radiologists to classify lesions; however, most of the existing methods need a large volume of data to give good results. Thus, this paper proposes a novel pulmonary lesion classification framework for EBUS images that works well with small datasets. The proposed framework integrates the statistical results from three classification models using the weighted ensemble classification. The three classification models include the radiomics feature and patient data-based model, the single-image-based model, and the multi-patch-based model. The radiomics features are combined with the patient data to be used as input data for the random forest, whereas the EBUS images are used as input data to the other two CNN models. The performance of the proposed framework was evaluated on a set of 200 EBUS images consisting of 124 malignant lesions and 76 benign lesions. The experimental results show that the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are 95.00%, 100%, 86.67%, 92.59%, 100%, and 93.33%, respectively. This framework can significantly improve the pulmonary lesion classification.

Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational study

INTRODUCTION

Lung cancer is the leading cause of cancer death in the USA and worldwide, and lung cancer screening (LCS) with low-dose CT (LDCT) has the potential to improve lung cancer outcomes. A critical question is whether the ratio of potential benefits to harms found in prior LCS trials applies to an older and potentially sicker population. The Personalised Lung Cancer Screening (PLuS) study will help close this knowledge gap by leveraging real-world data to fully characterise LCS recipients. The principal goal of the PLuS study is to characterise the comorbidity burden of individuals undergoing LCS and quantify the benefits and harms of LCS to enable informed decision-making.

METHODS AND ANALYSIS

PLuS is a multicentre observational study designed to assemble an LCS cohort from the electronic health records of ~40 000 individuals undergoing annual LCS with LDCT from 2016 to 2022. Data will be integrated into a unified repository to (1) examine the burden of multimorbidity by race/ethnicity, socioeconomic status and age; (2) quantify potential benefits and harms; and (3) use the observational data with validated simulation models in the Cancer Intervention and Surveillance Modeling Network (CISNET) to provide LCS outcomes in the real-world US population. We will fit a multivariable logistic regression model to estimate the adjusted ORs of comorbidity, functional limitations and impaired pulmonary function adjusted for relevant covariates. We will also estimate the cumulative risk of LCS outcomes using discrete-time survival models. To our knowledge, this is the first study to combine observational data and simulation models to estimate the long-term impact of LCS with LDCT.

ETHICS AND DISSEMINATION

The study was approved by the Kaiser Permanente Southern California Institutional Review Board and VA Portland Health Care System. The results will be disseminated through publications and presentations at national and international conferences. Safety considerations include protection of patient confidentiality.

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (¹⁸F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.