Risk-Based lung cancer screening: A systematic review

Andrographis modulates cisplatin resistance in lung cancer via miR-155-5p/SIRT1 axis

Andrographis (Andro) has been identified as an anti-cancer herbal. This study was to explore its underlying regulatory routes regarding cisplatin (DDP) resistance in lung cancer. The impacts of Andro on cell viability in lung cancer cells and normal cells BEAS-2B were validated using CCK8 tests. Then, cell viability and apoptosis analysis was performed in the cells after DDP, Andro, or combined treatment. RT-qPCR was applied for evaluating miR-155-5p and SIRT1 mRNA expressions, while western blot was for evaluating SIRT1 protein expressions. Binding sites between SIRT1 and miR-155-5p were predicted on TargetScan and were confirmed using luciferase reporter assays. Xenograft animal models were established for in vivo validation of the regulatory function of Andro in lung cancer. Andro decreased the cell viability in lung cancer cells but not normal cells BEAS-2B. The combined treatment with DDP and Andro induced the lowest viability and highest apoptosis in both A549 and A549/DDP cells. MiR-155-5p expression was suppressed, and SIRT was promoted by the Andro treatment, while overexpression of miR-155-5p reversed effects of Andro in cells, which was further counteracted by SIRT1 activation. SIRT1 was verified to be a target of miR-155-5p in A549/DDP cells. Moreover, Andro synergized with DDP in mice with lung cancer via miR-155-5p/SIRT1. Andro modulates cisplatin resistance in lung cancer via miR-155-5p/SIRT1 axis.

A Systematic Review of Mesenchymal Epithelial Transition Factor (MET) and Its Impact in the Development and Treatment of Non-Small-Cell Lung Cancer

Lung cancer represents the leading cause of annual cancer-related deaths worldwide, accounting for 12.9%. The available treatment options for patients who experience disease progression remain limited. Targeted therapeutic approaches are promising but further understanding of the role of genetic alterations in tumorigenesis is imperative. The MET gene has garnered great interest in this regard. The aim of this systematic review was to analyze the findings from multiple studies to provide a comprehensive and unbiased summary of the evidence. A systematic search was conducted in the reputable scientific databases Embase and PubMed, leading to the inclusion of twenty-two articles, following the PRISMA guidelines, elucidating the biological role of MET in lung cancer and targeted therapies. The systematic review was registered in PROSPERO with registration ID: CRD42023437714. MET mutations were detected in 7.6-11.0% of cases while MET gene amplification was observed in 3.9-22.0%. Six studies showed favorable treatment outcomes utilizing MET inhibitors compared to standard treatment or placebo, with increases in PFS and OS ranging from 0.9 to 12.4 and 7.2 to 24.2 months, respectively, and one study reporting an increase in ORR by 17.3%. Furthermore, patients with a higher mutational burden may derive greater benefit from treatment with MET tyrosine kinase inhibitors (TKIs) than those with a lower mutational burden. Conversely, two studies reported no beneficial effect from adjunctive treatment with a MET targeted therapy. Given these findings, there is an urgent need to identify effective therapeutic strategies specifically targeting the MET gene in lung cancer patients.

A comprehensive analysis of lung cancer highlighting epidemiological factors and psychiatric comorbidities from the All of Us Research Program

Lung cancer is the leading cause of cancer-related mortality in the United States. Investigating epidemiological and clinical parameters can contribute to an improved understanding of disease development and management. In this cross-sectional, case-control study, we used the All of Us database to compare healthcare access, family history, smoking-related behaviors, and psychiatric comorbidities in light smoking controls, matched smoking controls, and primary and secondary lung cancer patients. We found a decreased odds of primary lung cancer patients versus matched smoking controls reporting inability to afford follow-up or specialist care. Additionally, we found a significantly increased odds of secondary lung cancer patients having comorbid anxiety and insomnia when compared to matched smoking controls. Our study provides a profile of the psychiatric disease burden in lung cancer patients and reports key epidemiological factors in patients with primary and secondary lung cancer. By using two controls, we were able to separate smoking behavior from lung cancer and identify factors that were mediated by heavy smoking alone or by both smoking and lung cancer.

Impact of low-dose computed tomography screening on lung cancer incidence and outcomes

PURPOSE OF REVIEW

To review findings from clinical trials of lung cancer screening (LCS), assess contemporary issues with implementation in clinical practice, and review emerging strategies to increase the uptake and efficiency of LCS.

RECENT FINDINGS

In 2013, the USPSTF recommended annual screening for individuals aged 55-80 years and currently smoke or quit within the past 15 years based on reduced mortality from lung cancer with annual low-dose computed tomography (LDCT) screening in the National Lung Screening Trial. Subsequent trials have demonstrated similar mortality outcomes in individuals with lower pack-year smoking histories. These findings, coupled with evidence for disparities in screening eligibility by race, resulted in updated guidelines by USPSTF to broaden eligibility criteria for screening. Despite this body of evidence, implementation in the United States has been suboptimal with fewer than 20% of eligible individuals receiving a screen. Barriers to efficient implementation are multifactorial and include patient, clinician, and system-level factors.

SUMMARY

Multiple randomized trials have established that annual LCS reduces mortality from lung cancer; however, several areas of uncertainty exist on the effectiveness of annual LDCT. Ongoing research is examining approaches to improve the uptake and efficiency of LCS, such as the use of risk-prediction models and biomarkers for identification of high-risk individuals.

Changing recommendations for lung cancer screening: National Lung Cancer Roundtable member perspectives

Although the revised (2021) US Preventive Services Task Force recommendations for lung cancer screening offer the opportunity to save more lives and reduce disparities, National Lung Cancer Roundtable members share a cautionary message about the challenges ahead. To facilitate high‐quality care for diverse populations, a patient‐centered approach is needed that incorporates high‐quality shared decision‐making, improved access to care and navigation, and more streamlined systems of care.

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

BACKGROUND

Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches.

RESULTS

In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer.

CONCLUSIONS

This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

Lung Cancer Screening With Low-Dose Computed Tomography

BACKGROUND

Approximately 21 900 women and 35 300 men developed lung cancer in Germany in 2018, and 16 514 women and 28 365 men died of it. The outcome mainly depends on the tumor stage. In early stages (stage I or II), treatment can be curative; unfortunately, because early-stage lung cancers are generally asymptomatic, 74% of women and 77% of men already have advanced-stage disease (stage III or IV) at the time of diagnosis. Screening with low-dose computed tomography is an option enabling early diagnosis and curative treatment.

METHODS

This review is based on pertinent articles retrieved by a selective search of the literature on screening for lung cancer.

RESULTS

In the studies of lung cancer screening that have been published to date, sensitivity ranged from 68.5% to 93.8%, and specificity from 73.4% to 99.2%. A meta-analysis by the German Federal Office for Radiation Protection revealed a 15% reduction in lung cancer mortality when low-dose computed tomography was used in persons who were judged to be at high risk for lung cancer (risk ratio [RR] 0.85, 95% confidence interval [0.77; 0.95]). 1.9% of subjects died in the screening arm of the metaanalysis, and 2.2% in the control group. The observation periods ranged from 6.6 to 10 years; false-positive rates ranged from 84.9% to 96.4%. Malignant findings were confirmed in 45% to 70% of the biopsies or resective procedures that were performed.

CONCLUSION

Systematic lung cancer screening with low-dose CT lowers mortality from lung cancer in (current or former) heavy smokers. This benefit must be weighed against the high rate of false-positive findings and overdiagnoses.

Enhancement of cisplatin-induced apoptosis by saffron in human lung cancer cells

BACKGROUND

Cisplatin is a prevalent chemotherapeutic agent, and it has been used extensively to treat lung cancer. However, its clinical efficacy is hampered by its safety profile and dose-limiting toxicity. Saffron is a natural product that has shown significant anticancer effects. The combination treatment of saffron with chemotherapeutic agents has been considered a new strategy.

METHODS

Herein, saffron extract as a natural anticancer substance was combined with cisplatin to assess their combined efficacy against tumor development in vitro. In A549 and QU-DB cell lines, the combined effect of the saffron extract with cisplatin led to a significant reduction in cell viability as compared to cisplatin alone.

RESULTS

After 48 h incubation a considerable reduction in ROS levels in the QU-DB cell line upon treatment with cisplatin in the presence of saffron extract in comparison with cells treated with cisplatin alone. Furthermore, apoptosis increased significantly when in cells treated with cisplatin in combination with saffron extract compared to cisplatin alone.

CONCLUSION

Our data establish that the combination of saffron extract as a natural anticancer substance with cisplatin leads to improved cell toxicity of cisplatin as an anticancer agent. Therefore, the saffron extract could be potentially used as an additive to enable a reduction in cisplatin dosages and its side effects.

A Closer Look-Who Are We Screening for Lung Cancer?

Objective

To evaluate the characteristics of individuals receiving lung cancer screening (LCS) and identify those with potentially limited benefit owing to coexisting chronic illnesses and/or comorbidities.

Patients and Methods

In this retrospective study in the United States, patients were selected from a large clinical database who received LCS from January 1, 2019, through December 31, 2019, with at least 1 year of continuous enrollment. We assessed for potentially limited benefit in LCS defined strictly as not meeting the traditional risk factor inclusion criteria (age <55 years or >80 years, previous computed tomography scan within 11 months before an LCS examination, or a history of nonskin cancer) or liberally as having the potential exclusion criteria related to comorbid life-limiting conditions, such as cardiac and/or respiratory disease.

Results

A total of 51,551 patients were analyzed. Overall, 8391 (16.3%) individuals experienced a potentially limited benefit from LCS. Among those who did not meet the strict traditional inclusion criteria, 317 (3.8%) were because of age, 2350 (28%) reported a history of nonskin malignancy, and 2211 (26.3%) underwent a previous computed tomography thorax within 11 months before an LCS examination. Of those with potentially limited benefit owing to comorbidity, 3680 (43.9%) were because of severe respiratory comorbidity (937 [25.5%] with any hospitalization for coronary obstructive pulmonary disease, interstitial lung disease, or respiratory failure; 131 [3.6%] with hospitalization for respiratory failure requiring mechanical ventilation; or 3197 [86.9%] with chronic obstructive disease/interstitial lung disease requiring outpatient oxygen) and 721 (8.59%) with cardiac comorbidity.

Conclusion

Up to 1 of 6 low-dose computed tomography examinations may have limited benefit from LCS.

Genetic Polymorphisms of ACE1 Rs4646994 Associated with Lung Cancer in Patients with Pulmonary Nodules: A Case-Control Study

BACKGROUND

Currently, many detection methods have high sensitivity to the diagnosis of lung cancer. However, some postoperative patients with pulmonary nodules are eventually diagnosed as having benign nodules. The ideal evaluation of an individual with a pulmonary nodule would expedite therapy for a malignant nodule and minimize testing for those with a benign nodule.

METHODS

This case-control study is designed to explore the relationship between ACE1 rs4646994 polymorphism and the risk of lung cancer in patients with pulmonary nodules, for which 400 individuals with lung cancer and benign pulmonary nodules were included. A DNA extraction kit was used to extract DNA from peripheral blood. The relationship between ACE1 rs4646994 and the risk of lung cancer in patients with pulmonary nodules was determined by the chi-square test, logistic regression analysis and cross analysis.

RESULTS

The results showed that after adjusting for age and gender confounding factors, the risk of lung cancer in patients with pulmonary nodules carrying the DD genotype was more than three times that of the I carriers (II + ID) genotype (OR = 3.035, 95% CI, 1.252-7.356, p = 0.014). There was no significant difference between lung squamous cell carcinoma and lung adenocarcinoma in the polymorphism of ACE1 rs4646994 (p > 0.05). We also found that the ACE1 rs4646994 DD genotype frequency was inversely correlated with the risk of EGFR mutation in lung adenocarcinoma patients.

CONCLUSIONS

Our study indicated that ACE1 rs4646994 polymorphism increases the risk of lung cancer in patients with pulmonary nodules from China.

Initial low-dose computed tomography screening results and summary of participant characteristics: based on the latest Chinese guideline

Background

Low-dose computed tomography (LDCT) has been promoted as a promising screening strategy for early detection of lung cancer. China released the latest lung cancer screening guideline in 2021. The compliance of the individuals who received LDCT for lung cancer screening with the guideline is unknown yet. It is necessary to summarize the distribution of guideline-defined lung cancer-related risk factors in the Chinese population so as to inform the selection of target population for the future lung cancer screening.

Methods

A single-center, cross-sectional study design was adopted. All participants were individuals who underwent LDCT at a tertiary teaching hospital in Hunan, China, between 1 January and 31 December 2021. LDCT results were derived along with guideline-based characteristics for descriptive analysis.

Results

A total of 5,486 participants were included. Over one-quarter (1,426, 26.0%) of the participants who received screening did not meet the guideline-defined high-risk population, even among non-smokers (36.4%). Most of the participants (4,622, 84.3%) were found to have lung nodules, while no clinical intervention was required basically. The detection rate of positive nodules varied from 46.8% to 71.2% when using different cut-off values for positive nodules. Among non-smoking women, ground glass opacity appeared to be more significantly common compared with non-smoking men (26.7% vs. 21.8%).

Conclusion

Over one-quarter of individuals who received LDCT screening did not meet the guideline-defined high-risk populations. Appropriate cut-off values for positive nodules need to be continuously explored. More precise and localized criteria for high-risk individuals are needed, especially for non-smoking women.

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

BACKGROUND

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models.

METHODS

For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R²_D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]_v2, LLP_v3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]_M2012, PLCO_M2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria.

FINDINGS

There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R²_D in both sexes in the QResearch validation cohort and 59% of the R²_D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP_v2 and PLCO_M2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk.

INTERPRETATION

The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme.

FUNDING

Innovate UK (UK Research and Innovation).

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data

Background

Artificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.

Methods

Between 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.

Findings

A total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.

Conclusion

The nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning

Background

Algorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed.

Methods

A baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms.

Results

The proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668).

Conclusions

The external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.

One-off low-dose CT screening of positive nodules in lung cancer: A prospective community-based cohort study

BACKGROUND

To improve the early stage diagnosis and reduce the lung cancer (LC) mortality for positive nodule (PN) population, data on effectiveness of PN detection using one-off low-dose spiral computed tomography (LDCT) screening are needed to improve the PN management protocol. We evaluate the effectiveness of PN detection and developed a nomogram to predict LC risk for PNs.

METHODS

A prospective, community-based cohort study was conducted. We recruited 292,531 eligible candidates during 2012-2018. Individuals at high risk of LC based on risk assessment underwent LDCT screening and were divided into PN and non-PN groups. The effectiveness of PN detection was evaluated in LC incidence, mortality, and all-cause mortality. We performed subgroup analysis of characteristic variables for the association between PN and LC risk. A competing risk model was used to develop the nomogram.

RESULTS

Participants (n = 14901) underwent LDCT screening; PNs were detected in 1193 cases (8·0%). After a median follow-up of 6·1 years, 193 were diagnosed with LC (1·3%). Of these, 94 were in the PN group (8·0%). LC incidence, mortality, and all-cause mortality were significantly higher in the PN group (adjusted hazard ratios: 10.60 (7.91-14.20), 7.97 (5.20-12.20), and 1.94 (1.51-2.50), respectively). Additionally, various PN characteristics were associated with an increased probability of developing LC. The C-index value of the nomogram for predicting LC risk of PN individuals was 0·847.

CONCLUSIONS

The protocol of PNs management for improvement could focus on specific characteristic population and high-risk PN individuals by nomogram assessment.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.

Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis

BACKGROUND

In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.

OBJECTIVE

To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.

DESIGN

Comparative modeling analysis.

DATA SOURCES

National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator.

TARGET POPULATION

1960 U.S. birth cohort.

TIME HORIZON

45 years.

PERSPECTIVE

U.S. health care sector.

INTERVENTION

Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model.

OUTCOME MEASURES

Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost.

RESULTS OF BASE-CASE ANALYSIS

Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%).

RESULTS OF SENSITIVITY ANALYSES

Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions.

LIMITATION

Risk models were restricted to age, sex, and smoking-related risk predictors.

CONCLUSION

Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration.

PRIMARY FUNDING SOURCE

National Cancer Institute (NCI).

The Usefulness of Nanotechnology in Improving the Prognosis of Lung Cancer

Lung cancer remains a major public health problem both in terms of incidence and specific mortality despite recent developments in terms of prevention, such as smoking reduction policies and clinical management advances. Better lung cancer prognosis could be achieved by early and accurate diagnosis and improved therapeutic interventions. Nanotechnology is a dynamic and fast-developing field; various medical applications have been developed and deployed, and more exist as proofs of concepts or experimental models. We aim to summarize current knowledge relevant to the use of nanotechnology in lung cancer management. Starting from the chemical structure-based classification of nanoparticles, we identify and review various practical implementations roughly organized as diagnostic or therapeutic in scope, ranging from innovative contrast agents to targeted drug carriers. Available data are presented starting with standards of practice and moving to highly experimental methods and proofs of concept; particularities, advantages, limits and future directions are explored, focusing on the potential impact on lung cancer clinical prognosis.

Tumor Suppressor miR-613 Alleviates Non-Small Cell Lung Cancer Cell via Repressing M2 Macrophage Polarization

Background

Non-small cell lung cancer (NSCLC) is a crucial crux of cancer-related death, and M2 macrophage polarization facilitates NSCLC development. MicroRNA-613 (miR-613) is a tumor suppressor. This research aimed to clarify the miR-613 function in NSCLC and its impact on M2 macrophage polarization. Methods. miR-613 expressions in NSCLC tissues and cells were evaluated using quantitative real-time PCR. For miR-613 function in NSCLC, cell proliferation analysis, cell counting kit-8, flow cytometry, western blot, transwell, and wound-healing were conducted. Meanwhile, the miR-613 impact on M2 macrophage polarization was assessed by the NSCLC models. Results. miR-613 was lessened in NSCLC cells and tissues. It was corroborated that miR-613 overexpression retrained NSCLC cell proliferation, invasion, and migration but facilitated cell apoptosis. Moreover, miR-613 overexpression restrained NSCLC development by repressing M2 macrophage polarization.

Conclusion

Tumor suppressor miR-613 ameliorated NSCLC by restraining M2 macrophage polarization.

Roles of TGF‑β signalling pathway‑related lncRNAs in cancer (Review)

Long non-coding RNAs (lncRNAs) are a class of RNAs that are >200 nucleotides in length that do not have the ability to be translated into protein but are associated with numerous diseases, including cancer. The involvement of lncRNAs in the signalling of certain signalling pathways can promote tumour progression; these pathways include the transforming growth factor (TGF)-β signalling pathway, which is related to tumour development. The expression of lncRNAs in various tumour tissues is specific, and their interaction with the TGF-β signalling pathway indicates that they may serve as new tumour markers and therapeutic targets. The present review summarized the role of TGF-β pathway-associated lncRNAs in regulating tumorigenesis in different types of cancer and their effects on the TGF-β signalling pathway.

OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations

BACKGROUND

A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model.

METHODS

We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis.

FINDINGS

For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations.

INTERPRETATION

OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer.

FUNDING

National Natural Science Foundation of China, the US NIH.

The impact of low-dose CT on smoking behavior among non-smokers, former-smokers, and smokers: A population-based screening cohort in rural China

BACKGROUND

Lung cancer screening may provide a "teachable moment" for the smoking cessation and relapse prevention. However, the impact of lung cancer screening on smoking initiation in non-smokers has not been reported.

METHODS

A baseline smoking behavior survey was conducted in 2000 participants who were screened by low-dose computed tomography (LDCT) from 2014 to 2018. All participants were re-surveyed on their smoking behavior in 2019. Of these, 312 participants were excluded, leaving 1688 participants in the final analysis. The smoking initiation rate in baseline non-smokers, the relapse rate in baseline former smokers, and the abstinence rate in baseline current smokers were calculated, respectively. The associations between screening results, demographic characteristics, and smoking behavior change were analyzed using multivariable logistic regression.

RESULTS

From 2014 to 2019, smoking prevalence significantly decreased from 52.6% to 49.1%. The prevalence of smoking initiation, relapse, and abstinence in baseline non-smokers, former, and current smokers was 16.8%, 22.9%, and 23.7%, respectively. The risk of smoking initiation in baseline non-smokers was significantly higher in those with negative screening result (adjusted OR = 2.97, 95% CI: 1.27-6.94). Compared to smokers who only received baseline screening, the chance of smoking abstinence in baseline current smokers was reduced by over 80% in those who attended 5 rounds of screening (adjusted OR = 0.15, 95% CI:0.08-0.27). No significant associations were found between smoking relapse and prior screening frequency, with at least one positive screening result. Age, gender, occupational exposure, income, and smoking pack years were also associated with smoking behavior changes.

CONCLUSIONS

The overall decreased smoking prevalence indicated an overwhelming effect of "teachable moment" on "license to smoke." A tailored smoking cessation strategy should be integrated into lung cancer screening.

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.

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States

Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.

A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma

Background

The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear.

Methods

The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines.

Results

Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed.

Conclusion

The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.

Assessment of circulating biomarkers for detection of lung cancer in a high-risk cohort

BACKGROUND

There is an urgent need for early detection of lung cancer. Screening with low-dose computed tomography (LDCT) is now implemented in the US. Supplementary use of a lung cancer biomarker with high specificity is desirable.

OBJECTIVE

To assess the diagnostic properties of a biomarker panel consisting of cytokeratin 19 fragment (CYFRA 21-1), carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125).

METHODS

A cohort of 250 high-risk patients was investigated on suspicion of lung cancer. Ahead of diagnostic work-up, blood samples taken. Cross-validated prediction models were computed to assess lung cancer detection properties.

RESULTS

In total 32% (79/250) of patients were diagnosed with lung cancer. Area under the curve (AUC) for the three biomarkers was of 0.795, with sensitivity/specificity of 57%/93% and negative predictive value of 83%. When combining the biomarkers with US screening criteria, the AUC was 0.809, while applying only US screening criteria on the cohort, yielded an AUC of 0.62. The ability of the biomarkers to detect stage I-II lung cancer was substantially lower; AUC 0.54.

CONCLUSIONS

In a high-risk cohort, the detection properties of the three biomarkers were acceptable compared to current LDCT screening criteria. However, the ability to detect early stage lung cancer was low.

Exhaled phospholipid transfer protein and hepatocyte growth factor receptor in lung adenocarcinoma

BACKGROUND

Screening decreases mortality among lung cancer patients but is not widely implemented, thus there is an unmet need for an easily accessible non-invasive method to enable early diagnosis. Particles in exhaled air offer a promising such diagnostic tool. We investigated the validity of a particles in exhaled air device (PExA) to measure the particle flow rate (PFR) and collect exhaled breath particles (EBP) to diagnose primary lung adenocarcinoma (LUAD).

METHODS

Seventeen patients listed for resection of LUAD stages IA-IIIA and 18 non-cancer surgical control patients were enrolled. EBP were collected before and after surgery for LUAD, and once for controls. Proteomic analysis was carried out using a proximity extension assay technology. Results were validated in both plasma from the same cohort and with microarray data from healthy lung tissue and LUAD tissue in the GSE10072 dataset.

RESULTS

Of the 92 proteins analyzed, levels of five proteins in EBP were significantly higher in the LUAD patients compared to controls. Levels of phospholipid transfer protein (PLTP) and hepatocyte growth factor receptor (MET) decreased in LUAD patients after surgery compared to control patients. PFR was significantly higher in the LUAD cohort at all timepoints compared to the control group. MET in plasma correlated significantly with MET in EBP.

CONCLUSION

Collection of EBP and measuring of PFR has never been performed in patients with LUAD. In the present study PFR alone could distinguish between LUAD and patients without LUAD. PLTP and MET were identified as potential biomarkers to evaluate successful tumor excision.

Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years

Randomized trials have demonstrated considerable reduction in lung cancer (LC) mortality by screening pre-selected heavy smokers with low-dose computed tomography (LDCT). Newer screening guidelines recommend refined LC risk models for selecting the target population for screening. We aimed to evaluate and compare the discrimination performance of LC risk models and previously used trial criteria in predicting LC incidence and mortality in a large German cohort of screening-age adults. Within ESTHER, a population-based prospective cohort study conducted in Saarland, Germany, 4812 ever smokers aged 50-75 years were followed up with respect to LC incidence and mortality for up to 17 years. We quantified the performance of 11 different LC risk models by the area under the curve (AUC) and compared the proportion of correctly predicted LC cases between the best performing models and the LDCT trial criteria. Risk prediction of LC incidence in the ESTHER ever smokers was best for the Bach model, LCRAT and LCDRAT with AUCs ranging from 0.782 to 0.787, from 0.770 to 0.774, and from 0.765 to 0.771 for the follow-up time periods of cases identified at 6, 11, and 17 years, respectively. At cutoffs yielding comparable positivity rates as the LDCT trial criteria, these models would have identified between 11.8 (95% CI 3.0-20.5) and 17.6 (95% CI 10.1-25.2) percent units higher proportions of LC cases occurring during the initial 6 years of follow-up. Use of LC risk models is expected to result in substantially greater potential to identify people at highest risk of LC, suggesting enhanced potential for reducing LC mortality by LC screening.

Eligibility to lung cancer screening among staffs of a university hospital: A large cross-sectional survey

INTRODUCTION

Implementation of Lung cancer screening (LCS) programs is challenging. The ILYAD study objectives is to evaluate communication methods to improve participation rate among the Lyon University Hospital employees. In this first part of the study, we aimed to determinate the number of eligible individuals among our population of employees.

METHOD

In November 2020, we conducted a questionnaire based cross sectional survey among the Lyon University Hospital employees (N = 26,954). We evaluated the PLCO m2012 risk prediction model and the eligibility criteria recommended by French guidelines. We assessed the proportion of eligible individuals among the responders and calculated the total eligible individuals in our hospital.

RESULTS

Overall, 4,526 questionnaires were available for analysis. 16.0% were current smokers, and 28.2% were former smokers. Among the 50-75yo ever-smoker employees, 27% were eligible according to the French guidelines, 2.7% of all eversmokers according to a PLCO m2012 score ≥ 1.51%, and thus, 3.8% of the surveyed population were eligible to the combined criteria. The factors associated with higher eligibility among 50-75yo ever-smokers were educational level, feeling symptoms related to tobacco smoking, personal history of COPD and family history of lung cancer. Using the French guidelines criteria only, we estimated the total number of eligible individuals in the hospital at 838.

CONCLUSION

In this study, we determined a theoretical number of eligible employees to LCS in our institution and the factors associated to eligibility. Secondly, we will propose LCS to all eligible employees of Lyon University Hospital with incremented information actions.

Exosome-delivered circSATB2 targets the miR-330-5p/PEAK1 axis to regulate proliferation, migration and invasion of lung cancer cells

Exosomes can carry various kinds of RNAs to mediate intercellular communication. Circular RNA (circRNA) special AT-rich sequence-binding protein 2 (circSATB2) was identified as an oncogene in lung cancer. This study was performed to explore the association of circSATB2 with exosomes and the regulatory mechanism of circSATB2. Exosomes could transmit circSATB2 into lung cancer cells. Exosomes enhanced cell proliferation, invasion, and migration by carrying circSATB2. Exosomal circSATB2 abrogated the inhibitory effect of short hairpin (sh)-circSATB2 on lung cancer progression. Moreover, circSATB2 promoted tumor growth in vivo via exosomes. CircSATB2 interacted with microRNA-330-5p (miR-330-5p) and miR-330-5p targeted pseudopodium enriched atypical kinase 1 (PEAK1). In addition, circSATB2 affected the PEAK1 level via sponging miR-330-5p in lung cancer cells. All results suggested that exosomal transfer of circSATB2 contributed to the malignant development of lung cancer by acting as a sponge of miR-330-5p to upregulate PEAK1.

MiR-1246 is responsible for lung cancer cells-derived exosomes-mediated promoting effects on lung cancer stemness via targeting TRIM17

The stemness of lung cancer cells contributes to drug resistance, tumor occurrence, progression, and recurrence; however, the underlying mechanisms are still fragmentary. In the present study, it was found that exosomes from cisplatin-resistant cells and spheres derived from lung cancer cells enhanced the stemness of the parental lung cancer cells. Then we screened the upregulated miRNAs in spheres derived from lung cancer cells and cisplatin-resistant lung cancer cells/exosomes compared to that in the parental lung cancer cells. It was found that miR-1246 was remarkably enriched in cisplatin-resistant lung cancer cells/exosomes and spheres. Additionally, inhibition of miR-1246 attenuated the stemness of lung cancer cells induced by exosomes from cisplatin-resistant cells and spheres. Furthermore, TRIM17 was identified to the direct target of miR-1246 in lung cancer cells. Our findings suggest that exosomal miR-1246 could be as a potential target for lung cancer treatment.

Assessment of Uptake Appropriateness of Computed Tomography for Lung Cancer Screening According to Patients Meeting Eligibility Criteria of the US Preventive Services Task Force

Importance

Currently, computed tomography (CT) is used for lung cancer screening (LCS) among populations with various levels of compliance to the eligibility criteria from the US Preventive Services Task Force (USPSTF) recommendations and may represent suboptimal allocation of health care resources.

Objective

To evaluate the appropriateness of CT LCS according to the USPSTF eligibility criteria.

Design, Setting, and Participants

This cross-sectional study used the 2019 Behavioral Risk Factor Surveillance System (BRFSS) survey. Participants included individuals who responded to the LCS module administered in 20 states and had valid answers to questions regarding screening and smoking history. Data were analyzed between October 2021 and August 2022.

Exposures

Screening eligibility groups were categorized according to the USPSTF 2013 recommendations, and subgroups of individuals who underwent LCS were analyzed.

Main Outcomes and Measures

Main outcomes included LCS among the screening-eligible population and the proportions of the screened populations according to compliance categories established from the USPSTF 2013 and 2021 recommendations. In addition, the association between respondents' characteristics and LCS was evaluated for the subgroup who were screened despite not meeting any of the 3 USPSTF screening criteria: age, pack-year, and years since quitting smoking.

Results

A total of 96 097 respondents were identified for the full study cohort, and 2 subgroups were constructed: (1) 3374 respondents who reported having a CT or computerized axial tomography to check for lung cancer and (2) 33 809 respondents who did not meet any screening eligibility criteria. The proportion of participants who were under 50 years old was 53.1%; between 50 and 54, 9.1%; between 55 and 79, 33.8%; and over 80, 4.0%. A total of 51 536 (50.9%) of the participants were female. According to the USPSTF 2013 recommendation, 807 (12.8%) of the screening-eligible population underwent LCS. Among those who were screened, only 807 (20.9%) met all 3 screening eligibility criteria, whereas 538 (20.1%) failed to meet any criteria. Among respondents in subgroup 2, being of older age and having a history of stroke, chronic obstructive pulmonary disease, kidney disease, or diabetes were associated with higher likelihood of LCS.

Conclusions and Relevance

In this cross-sectional study of the BRFSS 2019 survey, the low uptake rate among screening-eligible patients undermined the goal of LCS of early detection. Suboptimal screening patterns could increase health system costs and add financial stress, psychological burden, and physical harms to low-risk patients, while failing to provide high-quality preventive services to individuals at high risk of lung cancer.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data

When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.

The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography

Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.

LINC00467: an oncogenic long noncoding RNA

Long non-coding RNAs (lncRNAs) have been found to play essential roles in the cell proliferation, fission and differentiation, involving various processes in humans. Recently, there is more and more interest in exploring the relationship between lncRNAs and tumors. Many latest evidences revealed that LINC00467, an oncogenic lncRNA, is highly expressed in lung cancer, gastric cancer, colorectal cancer, hepatocellular carcinoma, breast cancer, glioblastoma, head and neck squamous cell carcinoma, osteosarcoma, and other malignant tumors. Besides, LINC00467 expression was linked with proliferation, migration, invasion and apoptosis via the regulation of target genes and multiple potential pathways. We reviewed the existing data on the expression, downstream targets, molecular mechanisms, functions, relevant signaling pathways, and clinical implications of LINC00467 in various cancers. LINC00467 may serve as a novel biomarker or therapeutic target for the diagnosis and prognosis of various human tumors.

Serum Metabolomics Profiling Reveals Metabolic Alterations Prior to a Diagnosis with Non-Small Cell Lung Cancer among Chinese Community Residents: A Prospective Nested Case-Control Study

The present high mortality of lung cancer in China stems mainly from the lack of feasible, non-invasive and early disease detection biomarkers. Serum metabolomics profiling to reveal metabolic alterations could expedite the disease detection process and suggest those patients who are harboring disease. Using a nested case-control design, we applied ultra-high-performance liquid chromatography/mass spectrometry (LC-MS)-based serum metabolomics to reveal the metabolomic alterations and to indicate the presence of non-small cell lung cancer (NSCLC) using serum samples collected prior to disease diagnoses. The studied serum samples were collected from 41 patients before a NSCLC diagnosis (within 3.0 y) and 38 matched the cancer-free controls from the prospective Shanghai Suburban Adult Cohort. The NSCLC patients markedly presented cellular metabolism alterations in serum samples collected prior to their disease diagnoses compared with the cancer-free controls. In total, we identified 18 significantly expressed metabolites whose relative abundance showed either an upward or a downward trend, with most of them being lipid and lipid-like molecules, organic acids, and nitrogen compounds. Choline metabolism in cancer, sphingolipid, and glycerophospholipid metabolism emerged as the significant metabolic disturbance of NSCLC. The metabolites involved in these biological processes may be the distinctive features associated with NSCLC prior to a diagnosis.

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Alveolar type II (AT II) is a key structure of the distal lung epithelium and essential to maintain normal lung homeostasis. Dedifferentiation of AT II cells is significantly correlated with lung tumor progression. However, the potential molecular mechanism and clinical significance of AT II-associated genes for lung cancer has not yet been fully elucidated. In this study, we comprehensively analyzed the gene expression, prognosis value, genetic alteration, and immune cell infiltration of eight AT II-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1, and PGC) in Nonsmall Cell Lung Cancer (NSCLC). The results have shown that the expression of eight genes were remarkably reduced in cancer tissues and observably relating to clinical cancer stages. Survival analysis of the eight genes revealed that low-expression of CLDN18, FOXA2, NKX2-1, PGC, SFTPB, SFTPC, and SFTPD were significantly related to a reduced progression-free survival (FP), and low CLDN18, FOXA2, and SFTPD mRNA expression led to a short postprogression survival (PPS). Meanwhile, the alteration of 8 AT II-associated genes covered 273 out of 1053 NSCLC samples (26%). Additionally, the expression level of eight genes were significantly correlated with the infiltration of diverse immune cells, including six types of CD4+T cells, macrophages, neutrophils, B cells, CD8+ T cells, and dendritic cells. In summary, this study provided clues of the values of eight AT II-associated genes as clinical biomarkers and therapeutic targets in NSCLC and might provide some new inspirations to assist the design of new immunotherapies.

Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.

A phase II study of antiangiogenic therapy (Apatinib) plus chemotherapy as second-line treatment in advanced small cell lung cancer

INTRODUCTION

Currently, only a few options are available for the treatment of patients with small-cell lung cancer (SCLC) after the failure of first-line platinum-based chemotherapy. The present study aimed to evaluate the efficacy and safety of apatinib plus chemotherapy for second-line treatment of advanced SCLC.

PATIENTS AND METHODS

This prospective clinical trial recruited patients treated with apatinib plus second-line chemotherapy until disease progression or intolerable toxicity. Logrank test power analysis was used for calculating samples. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety.

RESULTS

A total of 29/31 enrolled patients were available for response evaluation until October 2019. The ORR and DCR were 27.59% (8/29) and 96.55% (28/29), respectively. The median PFS and OS were 7.36 months and 14.16 months, respectively, indicating better efficacy compared with the standard second-line chemotherapies. The most common adverse events (AEs) were neutropenia (41.94%, 13/31), followed by leucopenia (35.48%, 11/31) and thrombocytopenia (25.81%, 8/31). The grade 3-4 AEs occurred in 12 (38.71%) patients, of which neutropenia (19.35%, 6/31), leucopenia (9.68%, 3/31), and proteinuria (6.45%, 2/31) were most common. Patients receiving an initial dose of apatinib 250 mg had a better tolerance.

CONCLUSION

Antiangiogenic therapy plus chemotherapy had encouraging efficacy in advanced SCLC patients, which provides an insight into the current status of second-line therapy in SCLC.

Identification of molecular subtypes, risk signature, and immune landscape mediated by necroptosis-related genes in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.

Methods

In The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.

Results

With the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.

Conclusion

This study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy.

Potential Implications of the Lung Microbiota in Patients with Chronic Obstruction Pulmonary Disease and Non-Small Cell Lung Cancer

Recently, chronic obstructive pulmonary disease (COPD) has been considered as a common risk factor of non-small cell lung cancer (NSCLC). However, very few studies have been conducted on the effects of COPD on the lung microbiota in patients with NSCLC. To identify the lung microbiota in patients with COPD and NSCLC (CN), the microbiome of the induced sputa of 90 patients was analyzed using 16S rDNA sequencing. The results showed no significant differences in the bacterial diversities of induced sputa among patients with COPD, NSCLC, and CN and no intrinsic differences among patients with different pathological types of lung cancer. After surgical operation, the diversities of the induced sputa in patients with CN significantly decreased. More remarkably, both the microbial community phenotypes and the components of the induced sputa in patients with CN obviously differed from those in patients with COPD or NSCLC. The relative abundances of Streptococcus, Veillonella, Moraxella, and Actinomyces significantly decreased, but those of Neisseria and Acinetobacter significantly increased in patients with CN compared with those in patients with COPD or NSCLC alone, resulting in increased Gram-negative microbiota and, therefore, in potential pathogenicity and stress tolerance, as well as in enhancement of microbial glycolipid metabolism, amino acid metabolism, and oxidative stress. Although COPD did not affect the number of pulmonary flora species in patients with NSCLC, these significant alterations in the microbial populations, phenotypes, and functions of induced sputa due to COPD would contribute to inflammation-derived cancer progression in patients with CN.

Photodynamic therapy to a primary cancer of the peripheral lung: Case report

Photodynamic therapy (PDT) is an FDA approved treatment for lung cancer. In the United States the photosensitizer porfimer sodium (Photofrin®, Pinnacle Biologics) is intravenously introduced at 2mg/kg. After approximately 48 h, illumination to activate the photosensitizer is initiated, with 630nm red light at 200J/cm, delivered by fiber-optic catheter, brought to the tumor endo- bronchially, and delivered for 500 s. This will create, in the presence of oxygen, a Type II Photodynamic Reaction (PDR) which generates singlet oxygen species that are tumor ablative. Classically, PDT for lung cancer has been employed for symptomatic central and obstructing tumors with great success. This case report describes an innovative approach to treat a peripheral, early stage lung cancer employing magnetic navigation and endobronchial treatment. We report on a 79 year old male with numerous comorbidities including pulmonary fibrosis, who was found to have a biopsy proven peripheral and solitary non-small cell cancer. Due to prior SBRT (stereotactic body radiation therapy) with dose levels causing radiation fibrosis, he was not a candidate for repeat SBRT, and he was not a surgical candidate due to comorbidities. Tumor control with PDT was achieved without treatment related morbidity. This report details our findings.

A Novel M6A-Related Genes Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a primary cause of cancer-related death around the world and has a poor outcome and high incidence. Treatment options are, however, restricted. One of the most critical factors in cancer and metastasis is the N6-methyladenine (m6A) alteration on RNA. This modification could alter gene expression and even function at numerous levels, such as the stability, translocation and translation of RNA splicing. This study aimed to construct an m6A-related genes signature to accurately predict the prognosis of LUAD patients. From TCGA datasets, the LUAD patient data and m6A-related genes were retrieved. LUAD patients’ mutational features and differentially expressed genes (DEGs) were investigated. An univariate and LASSO model with m6A-related genes were constructed for the prediction of outcomes in LUAD. It was possible to develop a prognostic nomogram that could quantitatively predict LUAD patients’ overall survival chances at 1, 3, and 5 years. Research into biological processes and cell pathways was carried out using GSEA. This study found six m6A-related DEGs in LUAD patients, and three of these DEGs(HNRNPC, IGFBP3 and IGF2BP1) were linked to the clinical outcomes of LUAD patients. We found that the overall survival rate for all LUAD patients with high-risk subgroup was considerably lower. According to ROC curves, the prognostic signature demonstrated a high degree of accuracy in predicting future outcomes. In addition, we created a novel nomogram achieved great accuracy with this one as well. The researchers also found that the novel signature might favorably modulate the immune response, and high-risk scores samples were more susceptible to numerous chemotherapeutic medicines. Overall, we developed a m6A-related gene prognostic signature that effectively predicted outcomes of LUAD patients and gave an immunological perspective for creating customized therapeutics.

Extracellular Vesicle Derived From Mesenchymal Stem Cells Have Bidirectional Effects on the Development of Lung Cancer

Mesenchymal stem cell is a kind of pluripotent cells with the ability of self-renewal and multi-directional differentiation, which exist in bone marrow, umbilical cord blood, umbilical cord tissue, placenta tissue, adipose tissue and so on. Extracellular vesicles are membranous lipid vesicles secreted by a variety of cells and widely present in body fluids, which contain proteins, mRNA, microRNA and other substances, and are an important medium of intercellular communication. At present, more and more evidence shows that mesenchymal stem cell-derived extracellular vesicles play an important role in the development of lung cancer. Regulating the levels of proteins, RNAs and other substances in MSC-EVs and then transplanting them into patients may be a new way to alleviate the development of lung cancer. We mainly introduce the role of extracellular vesicles derived from human umbilical cord mesenchymal stem cells, bone marrow mesenchymal stem cells and adipose mesenchymal stem cells in lung cancer, to provide new alternatives for the treatment of lung cancer.

Correlation between the Expression of VEGF and Ki67 and Lymph Node Metastasis in Non-small-Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background

Lymph node metastasis is the most common and important way of metastasis in NSCLC and is also the most important factor affecting lung cancer stage and prognosis. It is very important to analyze the relationship between the expression of vascular endothelial growth factor (VEGF) and Ki67 and lymph node metastasis (LNM) in non-small-cell lung cancer (NSCLC).

Methods

We searched the PubMed, EMBASE, and Cochrane Library and conducted meta-analyses using the R meta-package. Relative risk (RR) with a 95% confidence interval (95% CI) was the main indicator.

Results

Totally, 18 studies were considered eligible, with 4521 patients, including 1518 LNM-positive patients and 3033 LNM-negative patients. The incidence of LNM in Ki67-negative patients was lower than that in Ki67-positive patients (RR = 0.66, 95% CI: 0.44, 0.98). The incidence of LNM in VEGF-A-negative patients was lower than that in VEGF-A-positive patients (RR = 0.64, 95% CI: 0.49, 0.83). The incidence of LNM in VEGF-C negative patients was lower than that in VEGF-C positive patients (RR = 0.68, 95% CI: 0.53, 0.88). The incidence of LNM in VEGF-D negative and positive patients were of no significant differences (RR = 0.84, 95% CI: 0.61, 1.14).

Conclusion

The high expression of Ki67, VEGF-A, and VEGF-C significantly increases the risk of lymph node metastasis in NSCLC, while the VEGF-D expression has no correlation with lymph node metastasis. The expression levels of Ki67, VEGF-A, and VEGF-C show a good potential for lymph node metastasis prediction.

Effectiveness of Perioperative Cardiopulmonary Rehabilitation in Patients With Lung Cancer Undergoing Video-Assisted Thoracic Surgery

Objectives

Patients with lung cancer pose a high risk of morbidity and mortality after lung resection. Those who receive perioperative cardiopulmonary rehabilitation (PRCR) have better prognosis. Peak oxygen consumption (peak VO₂), VO₂ at the ventilatory threshold (VO₂ at VT), and slope of minute ventilation to carbon dioxide production (V_E/V_CO2 slope) measured during pre-surgical cardiopulmonary exercise testing (CPET) have prognostic values after lung resection. We aimed to investigate the influence of individualized PRCR on postoperative complications in patients undergoing video-assisted thoracic surgery (VATS) for lung cancer with different pre-surgical risks.

Methods

This was a retrospective study. We recruited 125 patients who underwent VATS for lung cancer between 2017 and 2021. CPET was administered before surgery to evaluate the risk level and PRCR was performed based on the individual risk level defined by peak VO2, VO2 at VT, and VE/VCO2 slope, respectively. The primary outcomes were intensive care unit (ICU) and hospital lengths of stay, endotracheal intubation time (ETT), and chest tube insertion time (CTT). The secondary outcomes were postoperative complications (PPCs), including subcutaneous emphysema, pneumothorax, pleural effusion, atelectasis, infection, and empyema.

Results

Three intergroup comparisons based on the risk level by peak VO2 (3 groups), VO2 at VT (2 groups), and VE/VCO2 slope (3 groups) were done. All of the comparisons showed no significant differences in both the primary and secondary outcomes (p = 0.061–0.910).

Conclusion

Patients with different risk levels showed comparable prognosis and PPCs after undergoing CPET-guided PRCR. PRCR should be encouraged in patients undergoing VATS for lung cancer.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.