Risk-Based lung cancer screening: A systematic review

Biomarker trajectory for earlier detection of lung cancer

BACKGROUND

To determine whether an algorithm based on repeated measurements of a panel of four circulating protein biomarkers (4 MP) for lung cancer risk assessment results in improved performance over a single time measurement.

METHODS

We conducted data analysis of the 4 MP consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in pre-diagnostic sera from 2483 ever-smoker participants (389 cases and 2094 randomly selected non-cases) in the Prostate, Lung, Colorectal, Ovarian (PLCO) Study who had at least two sequential blood collections over 6 years. A parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history at each time point, was compared to a single-threshold (ST) method.

FINDINGS

Among ever-smoker participants, the PEB approach yielded an additional 4% improvement in the AUC compared to the ST approach (P-value: 0.009). When considering an ≥10 PY smoking history and at a fixing the specificity corresponding to 1% 6-year lung cancer risk, PEB resulted in significant improvement in the sensitivity (SenPEB:96.3% vs SenST:91.0%; P-value: 6.7e-3). The PEB algorithm identified 17 of the 35 cases that remained ST negative, at an average of 1.26 years before diagnosis. Ten case individuals who were positive based on ST at an average of 1.03 years prior to diagnosis were identified earlier by PEB, at an average of 2.70 years.

INTERPRETATION

An algorithm based on repeated measurements of the 4 MP improves sensitivity and results in an earlier detection of lung cancer compared to a single-threshold method.

FUNDING

This study was supported by NIH Grant Nos. U01CA271888, U01CA194733, U01CA213285, NCI EDRN U01 CA200468, P30CA016672, and U24CA086368; the Cancer Prevention & Research Institute of Texas RP180505 and RP160693; the SPORE P50CA140388; the CCTS TR000371; and the generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program and the Lyda Hill Foundation.

Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study

OBJECTIVES

Exhaled breath analysis is an attractive lung cancer diagnostic tool. However, various factors that are not related to the disease status, comorbidities, and other diseases must be considered to obtain a reliable diagnostic model.

METHODS

Exhaled breath samples from 646 individuals including 273 patients with lung cancer (LC), 90 patients with cancer of other localizations (OC), 150 patients with noncancer lung diseases (NLD), and 133 healthy controls (HC) were analyzed using gas chromatography-mass spectrometry (GC-MS). The samples were collected in Tedlar bags. Volatile organic compounds (VOCs) were preconcentrated on Tenax TA sorbent tubes with subsequent two-stage thermal desorption followed by GC-MS analysis. The influence of age, gender, smoking status, time since last food consumption, and comorbidities on exhaled breath were evaluated. Also, the effect of histology, TNM, tumor localization, treatment status, and the presence of a tumor on VOC profile of patients with lung cancer were assessed. Intergroup statistics were estimated, diagnostic models were created using artificial neural networks (ANNs) and gradient boosted decision trees (GBDTs).

RESULTS

Smoking status and food consumption affect exhaled breath VOC profile: benzene, ethylbenzene, toluene, 1,3-pentadiene 1,4-pentadiene acetonitrile, and some ratios are significantly different in exhaled breath of smokers and nonsmokers; the ratios 2,3-butandione/2-pentanone, 2,3-butandione/dimethylsulfide, and 2-butanone/2-pentanone are affected by time since last food consumption. Exhaled breath of LC is affected by the form of the disease and comorbidities. One-pentanol and 2-butanone were different in exhaled breath of patients with various tumor localization; 2-butanone was different in exhaled breath of patients before and during treatment. Diabetes as a comorbidity affects the pentanal level in exhaled breath; obesity affects the ratios of 2,3-butanedione/dimethylsulfide and 2-butanone/isoprene. Sensitivity and specificity of diagnostic models aimed to discriminate LC and HC, OC, and NLD were 78.7% and 51.0%, 62.2% and 53.4%, and 60.4% and 58.0%, respectively. HC and patients, regardless of the disease, can be classified with sensitivity of 76.6% and specificity of 68.2%.

CONCLUSIONS

The models created to diagnose lung cancer can also classify OC and NLD as patients with lung cancer. Additionally, the influence of comorbidities and factors not related to the disease status must be considered before the creation of diagnostic models to avoid false results.

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study

BACKGROUND

A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening.

OBJECTIVE

This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations.

METHODS

We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables.

RESULTS

The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks.

CONCLUSIONS

This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

Identifying potential targets for preventing cancer progression through the PLA2G1B recombinant protein using bioinformatics and machine learning methods

Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key role in lipid metabolism and inflammation. PLA2G1B has selective substrate specificity. In this paper, the recombinant protein molecular structure of PLA2G1B was studied and novel therapeutic interventions were designed to disrupt PLA2G1B activity and impede tumor growth by targeting specific regions or residues in its structure. Construct protein-protein interaction networks and core genes using R's "STRING" program. LASSO, SVM-RFE and RF algorithms identified important genes associated with lung cancer. 282 deg were identified. Enrichment analysis showed that these genes were mainly related to adhesion and neuroactive ligand-receptor interaction pathways. PLA2G1B was subsequently identified as developing a preventative feature. GSEA showed that PLA2G1B is closely related to α-linolenic acid metabolism. Through the analysis of LASSO, SVM-RFE and RF algorithms, we found that PLA2G1B gene may be a preventive gene for lung cancer.

Harnessing the potential of genomic characterization of mutational profiles to improve early diagnosis of lung cancer

INTRODUCTION

Lung Cancer (LC) continues to be a leading cause of cancer-related mortality globally, largely due to the asymptomatic nature of its early stages and the limitations of current diagnostic methods such as Low-Dose Computed Tomography (LDCT), whose often result in late diagnosis, highlighting an urgent need for innovative, minimally invasive diagnostic techniques that can improve early detection rates.

AREAS COVERED

This review delves into the potential of genomic characterization and mutational profiling to enhance early LC diagnosis, exploring the current state and limitations of traditional diagnostic approaches and the revolutionary role of Liquid Biopsies (LB), including cell-free DNA (cfDNA) analysis through fragmentomics and methylomics. New genomic technologies that allow for earlier detection of LC are scrutinized, alongside a detailed discussion on the literature that shaped our understanding in this field.

EXPERT OPINION

Despite the promising advancements in genomic characterization techniques, several challenges remain, such as the heterogeneity of LC mutations, the high cost, and limited accessibility of Next-Generation Sequencing (NGS) technologies. Additionally, there is a critical need of standardized protocols for interpreting mutational data. Future research should focus on overcoming these barriers to integrate these novel diagnostic methods into standard clinical practice, potentially revolutionizing the management of LC patients.

The role of tRF-Val-CAC-010 in lung adenocarcinoma: implications for tumorigenesis and metastasis

OBJECTIVE

Transfer RNA-derived fragments (tRFs) are short non-coding RNA (ncRNA) sequences, ranging from 14 to 30 nucleotides, produced through the precise cleavage of precursor and mature tRNAs. While tRFs have been implicated in various diseases, including cancer, their role in lung adenocarcinoma (LUAD) remains underexplored. This study aims to investigate the impact of tRF-Val-CAC-010, a specific tRF molecule, on the phenotype of LUAD cells and its role in tumorigenesis and progression in vivo.

METHODS

The expression level of tRF-Val-CAC-010 was quantified using quantitative real-time polymerase chain reaction (qRT-PCR). Specific inhibitors and mimics of tRF-Val-CAC-010 were synthesized for transient transfection. Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8), while cell invasion and migration were evaluated through Transwell invasion and scratch assays. Flow cytometry was utilized to analyze cell cycle and apoptosis. The in vivo effects of tRF-Val-CAC-010 on tumor growth and metastasis were determined through tumor formation and metastasis imaging experiments in nude mice.

RESULTS

The expression level of tRF-Val-CAC-010 was upregulated in A549 and PC9 LUAD cells (P < 0.01). Suppression of tRF-Val-CAC-010 expression resulted in decreased proliferation of A549 and PC9 cells (P < 0.001), reduced invasion and migration of A549 (P < 0.05, P < 0.001) and PC9 cells (P < 0.05, P < 0.01), enhanced apoptosis in both A549 (P < 0.05) and PC9 cells (P < 0.05), and increased G2 phase cell cycle arrest in A549 cells (P < 0.05). In vivo, the tumor formation volume in the tRF-inhibitor group was significantly smaller than that in the model and tRF-NC groups (P < 0.05). The metastatic tumor flux value in the tRF-inhibitor group was also significantly lower than that in the model and tRF-NC groups (P < 0.05).

CONCLUSION

This study demonstrates that tRF-Val-CAC-010 promotes proliferation, migration, and invasion of LUAD cells and induces apoptosis in vitro, however, its specific effects on the cell cycle require further elucidation. Additionally, tRF-Val-CAC-010 enhances tumor formation and metastasis in vivo. Therefore, tRF-Val-CAC-010 may serve as a novel diagnostic biomarker and potential therapeutic target for LUAD.

Detecting mir-155-3p through a Molecular Beacon Bead-Based Assay

Lung cancer (LC) is recognized as one of the most prevalent and lethal cancers worldwide, underscoring an urgent need for innovative diagnostic and therapeutic approaches. MicroRNAs (miRNAs) have emerged as promising biomarkers for several diseases and their progression, such as LC. However, traditional methods for detecting and quantifying miRNAs, such as PCR, are time-consuming and expensive. Herein, we used a molecular beacon (MB) bead-based assay immobilized in a microfluidic device to detect miR-155-3p, which is frequently overexpressed in LC. The assay relies on the fluorescence enhancement of the MB upon binding to the target miRNA via Watson and Crick complementarity, resulting in a conformational change from a stem-loop to a linear structure, thereby bringing apart the fluorophores at each end. This assay was performed on a microfluidic platform enabling rapid and straightforward target detection. We successfully detected miR-155-3p in a saline solution, obtaining a limit of detection (LOD) of 42 nM. Furthermore, we evaluated the method's performance in more complex biological samples, including A549 cells' total RNA and peripheral blood mononuclear cells (PBMCs) spiked with the target miRNA. We achieved satisfactory recovery rates, especially in A549 cells' total RNA.

Thoracic high resolution computed tomography evaluation of imaging abnormalities of 108 lung cancer patients with different pulmonary function

PURPOSE

Preserved ratio impaired spirometry (PRISm) and chronic obstructive pulmonary disease (COPD) belong to lung function injury. PRISm is a precursor to COPD. We compared and evaluated the different basic information, imaging findings and survival curves of 108 lung cancer patients with different pulmonary function based on high resolution computed tomography (HRCT).

METHODS

This retrospective study was performed on 108 lung cancer patients who did pulmonary function test (PFT) and thoracic HRCT. The basic information was evaluated: gender, age, body mass index (BMI), smoke, smoking index (SI). The following pulmonary function findings were evaluated: forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), FEV1/FVC ratio. The following computed tomography (CT) findings were evaluated: appearance (bronchiectasis, pneumonectasis, atelectasis, ground-glass opacities [GGO], interstitial inflammation, thickened bronchial wall), diameter (aortic diameter, pulmonary artery diameter, MPAD/AD ratio, inferior vena cava diameter [IVCD]), tumor (volume, classification, distribution, staging [I, II, III, IV]). Mortality rates were calculated and survival curves were estimated using the Kaplan-Meier method.

RESULTS

Compared with normal pulmonary function group, PRISm group and COPD group were predominantly male, older, smoked more, poorer lung function and had shorter survival time after diagnosis. There were more abnormal images in PRISm group and COPD group than in normal lung function group (N-C group). In PRISm group and COPD group, lung cancer was found late, and the tumor volume was larger, mainly central squamous carcinoma. But the opposite was true for the N-C group. The PRISm group and COPD group had significant poor survival probability compared with the normal lung function group.

CONCLUSIONS

Considerable differences regarding basic information, pulmonary function, imaging findings and survival curves are found between normal lung function group and lung function injury group. Lung function injury (PRISm and COPD) should be taken into account in future lung cancer screening studies.

Applying machine learning to construct an association model for lung cancer and environmental hormone high-risk factors and nursing assessment reconstruction

INTRODUCTION

To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung cancer.

DESIGN

This study is exploratory in nature. In Stage 1, data were sourced from a biological database, and machine learning methods, including logistic regression and neural-like networks, were employed to construct an association model. Results indicate significant associations between lung cancer and blood cadmium, urine cadmium, urine cadmium/creatinine, and di(2-ethylhexyl) phthalate. In Stage 2, 128 lung adenocarcinoma patients were recruited through convenience sampling, and the model was validated using a questionnaire assessing daily living habits and exposure to environmental hormones.

RESULTS

Analysis reveals correlations between the living habits of patients with lung adenocarcinoma and exposure to blood cadmium, urine cadmium, urine cadmium/creatinine, polyaromatic hydrocarbons, diethyl phthalate, and di(2-ethylhexyl) phthalate.

CONCLUSIONS

According to the World Health Organization's global statistics, lung cancer claims approximately 1.8 million lives annually, with more than 50% of patients having no history of smoking or non-traditional risk factors. Environmental hormones have garnered significant attention in recent years in pathogen exploration. However, current nursing assessments for lung cancer risk have not incorporated environmental hormone-related factors. This study proposes reconstructing existing lung cancer nursing assessments with a comprehensive evaluation of lung cancer risks.

CLINICAL RELEVANCE

The findings underscore the importance of future studies advocating for public screening of environmental hormone toxins to increase the sample size and validate the model externally. The developed association model lays the groundwork for advancing cancer risk nursing assessments.

Lung cancer screening: where do we stand?

Lung cancer screening (LCS) programmes have emerged over recent years around the world. LCS programmes present differences in delivery, inclusion criteria and resource allocation. On a national scale, only a few LCS programmes have been fully established, but more are anticipated to follow. Evidence has shown that, in combination with a low-dose chest computed tomography scan, smoking cessation should be offered as part of a LCS programme for improved patient outcomes. Promising tools in LCS include further refined risk prediction models, the use of biomarkers, artificial intelligence and radiomics. However, these tools require further study and clinical validation is required prior to routine implementation.

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.

Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1)

BACKGROUND

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown.

METHODS

We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses.

RESULTS

The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators.

CONCLUSIONS

Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs.

TRIAL REGISTRATION

Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study

BACKGROUND

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

METHODS

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

RESULTS

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

CONCLUSIONS

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

Does the revised LDCT lung cancer screening guideline bridge the racial disparities gap: Results from the health and retirement study

PURPOSE

This study examined racial/ethnic disparities in lung cancer screening eligibility rates using 2013 US Preventive Services Task Force (USPSTF) guidelines for lung cancer with low-dose computed tomography (LDCT) and the revised 2021 guidelines.

METHODS

The study utilized a retrospective and cross-sectional research design by analyzing data from the Health and Retirement Study (HRS). N = 2,823 respondents aged 50-80 who self-reported current smoking were included in the analyses. Binary logistic regression analysis was conducted to examine the changed status of LDCT screening eligibility based on the revised 2021 guidelines by race/ethnicity after adjusting for respondent demographics.

RESULTS

Our study found substantial increases in screening eligibility rates across racial and ethnic groups when comparing the original and revised guidelines. The largest increase was observed among Black people (174%), Hispanics (152%), those in the other category (118%), and Whites who smoke (80.8%). When comparing original screening guidelines to revised guidelines, Whites who smoke had the highest percentage of changes from "not eligible" to "eligible" (28.3%), followed by individuals in the "other" category (28.1%), Black people (23.2%) and Hispanics who smoke (18.3%) (p < 0.001). Binary logistic regression results further showed that Black people who smoke (OR = 0.71, p = 0.001), as well as Hispanics who smoke (OR=0.54, p < 0.001), were less likely to change from not eligible to eligible for screening compared to Whites who smoke after adopting the revised screening guidelines. Based on the absolute differences in screening eligibility rates between Whites and other racial/ethnic groups, the disparities may have widened under the new guidelines, particularly with larger absolute differences observed between Whites, Black people, and Hispanics.

CONCLUSIONS

Our study highlights racial/ethnic disparities in LDCT screening eligibility among people who currently smoke. While the revised USPSTF guidelines increased screening eligibility for racial and ethnic minorities, they did not eliminate these disparities and may have widened under the new guidelines. Targeted interventions and policies are necessary to address barriers faced by underrepresented populations and promote equitable access to lung cancer screening.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging

Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images

Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches

BACKGROUND

Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen.

METHODS

Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set.

RESULTS

The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95).

CONCLUSIONS

We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

PRMT5 activates KLF5 by methylation to facilitate lung cancer

The highly expressed oncogenic factor Krüppel-like factor 5 (KLF5) promotes various cancerous processes, such as cell growth, survival, anti-apoptosis, migration and metastasis, particularly in lung cancer. Nevertheless, the modifications to KLF5 after translation are poorly understood. Protein arginine methyltransferase 5 (PRMT5) is considered as an oncogene known to be involved in different types of carcinomas, including lung cancer. Here, we show that the expression levels of PRMT5 and KLF5 are highly expressed lung cancer. Moreover, PRMT5 interacts with KLF5 and facilitates the dimethylation of KLF5 at Arginine 41 in a manner that depends on methyltransferase activity. Downregulation or pharmaceutical suppression of PRMT5 reduces the expression of KLF5 and its downstream targets both in vitro and in vivo. Mechanistically, the dimethylation of KLF5 by PRMT5 promotes the maintenance and proliferation of lung cancer cells at least partially by stabilising KLF5 via regulation of the Akt/GSK3β signalling axis. In summary, PRMT5 methylates KLF5 to prevent its degradation, thereby promoting the maintenance and proliferation of lung cancer cells. These results suggest that targeting PRMT5/KLF5 axis may offer a potential therapeutic strategy for lung cancer.

Personal history of cancer as a risk factor for second primary lung cancer: Implications for lung cancer screening

BACKGROUND

Personal history of cancer is an independent risk factor for lung cancer but is omitted from existing lung cancer screening eligibility criteria. In this study, we assess the lung cancer risk among cancer survivors and discuss potential implications for screening.

METHODS

This was a retrospective, secondary analysis of data from the Surveillance, Epidemiology and End Results (SEER) registry and the MD Anderson Cancer Center (MDACC). We estimated the standardized incidence ratios (SIRs) for lung cancer by site of first primary cancer using data from SEER. We assessed the lung cancer risk among head and neck cancer survivors from MDACC using cumulative incidence and compared the risk ratios (RR) by individuals' screening eligibility status.

RESULTS

Other than first primary lung cancer (SIR: 5.10, 95% CI: 5.01-5.18), cancer survivors in SEER with personal history of head and neck cancer (SIR: 3.71, 95% CI: 3.63-3.80) had the highest risk of developing second primary lung cancer, followed by bladder (SIR: 1.86, 95% CI: 1.81-1.90) and esophageal cancers (SIR: 1.78, 95% CI: 1.61-1.96). Head and neck cancer survivors had higher risk to develop lung cancer compared to the National Lung Screening Trial's subjects, (781 vs. 572 per 100,000 person-years, respectively). Head and neck cancer survivors ineligible for lung cancer screening seen at MDACC had significantly higher lung cancer risk than head and neck cancer survivors from SEER (RR: 1.9, p < 0.001).

CONCLUSION

Personal history of cancer, primarily head and neck cancer, is an independent risk factor for lung cancer and may be considered as an eligibility criterion in future lung cancer screening recommendations.

Protein arginine methyltransferases (PRMTs): Orchestrators of cancer pathogenesis, immunotherapy dynamics, and drug resistance

Protein Arginine Methyltransferases (PRMTs) are a family of enzymes regulating protein arginine methylation, which is a post-translational modification crucial for various cellular processes. Recent studies have highlighted the mechanistic role of PRMTs in cancer pathogenesis, immunotherapy, and drug resistance. PRMTs are involved in diverse oncogenic processes, including cell proliferation, apoptosis, and metastasis. They exert their effects by methylation of histones, transcription factors, and other regulatory proteins, resulting in altered gene expression patterns. PRMT-mediated histone methylation can lead to aberrant chromatin remodeling and epigenetic changes that drive oncogenesis. Additionally, PRMTs can directly interact with key signaling pathways involved in cancer progression, such as the PI3K/Akt and MAPK pathways, thereby modulating cell survival and proliferation. In the context of cancer immunotherapy, PRMTs have emerged as critical regulators of immune responses. They modulate immune checkpoint molecules, including programmed cell death protein 1 (PD-1), through arginine methylation. Drug resistance is a significant challenge in cancer treatment, and PRMTs have been implicated in this phenomenon. PRMTs can contribute to drug resistance through multiple mechanisms, including the epigenetic regulation of drug efflux pumps, altered DNA damage repair, and modulation of cell survival pathways. In conclusion, PRMTs play critical roles in cancer pathogenesis, immunotherapy, and drug resistance. In this overview, we have endeavored to illuminate the mechanistic intricacies of PRMT-mediated processes. Shedding light on these aspects will offer valuable insights into the fundamental biology of cancer and establish PRMTs as promising therapeutic targets.

Genomic Profiling Reveals Immune-Related Gene Differences in Lung Cancer Patients Stratified by PD1/PDL1 Expression: Implications for Immunotherapy Efficacy

Lung cancer remains a leading cause of global cancer-related mortality, and the exploration of innovative therapeutic approaches, such as PD1/PDL1 immunotherapy, is critical. This study leverages comprehensive data from the Cancer Genome Atlas (TCGA) to investigate the differential expression of PD1/PDL1 in lung cancer patients and explores its implications. Clinical data, RNA expression, somatic mutations, and copy number variations of 1017 lung cancer patients were obtained from TCGA. Patients were categorized into high (HE) and low (LE) PD1/PDL1 expression groups based on mRNA levels. Analyses included differential gene expression, functional enrichment, protein-protein interaction networks, and mutational landscape exploration. The study identified 391 differentially expressed genes, with CD4 and PTPRC among the upregulated genes in the HE group. Although overall survival did not significantly differ between HE and LE groups, enrichment analysis revealed a strong association with immunoregulatory signaling pathways, emphasizing the relevance of PD1/PDL1 in immune response modulation. Notably, TP53 mutations were significantly correlated with high PD1/PDL1 expression. This study provides a comprehensive analysis of PD1/PDL1 expression in lung cancer, uncovering potential biomarkers and highlighting the intricate interplay between PD1/PDL1 and the immune response. The identified upregulated genes, including CD4 and PTPRC, warrant further investigation for their roles in the context of lung cancer and immunotherapy. The study underscores the importance of considering molecular heterogeneity in shaping personalized treatment strategies for lung cancer patients. Limitations, such as the retrospective nature of TCGA data, should be acknowledged.

Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland

Throughout Europe, computed tomography (CT) screening for lung cancer is in a phase of clinical implementation or reimbursement evaluation. To efficiently select individuals for screening, the use of lung cancer risk models has been suggested, but their incremental (cost-)effectiveness relative to eligibility based on pack-year criteria has not been thoroughly evaluated for a European setting. We evaluate the cost-effectiveness of pack-year and risk-based screening (PLCOm2012 model-based) strategies for Switzerland, which aided in informing the recommendations of the Swiss Cancer Screening Committee (CSC). We use the MISCAN (MIcrosimulation SCreening ANalysis)-Lung model to estimate benefits and harms of screening among individuals born 1940 to 1979 in Switzerland. We evaluate 1512 strategies, differing in the age ranges employed for screening, the screening interval and the strictness of the smoking requirements. We estimate risk-based strategies to be more cost-effective than pack-year-based screening strategies. The most efficient strategy compliant with CSC recommendations is biennial screening for ever-smokers aged 55 to 80 with a 1.6% PLCOm2012 risk. Relative to no screening this strategy is estimated to reduce lung cancer mortality by 11.0%, with estimated costs per Quality-Adjusted Life-Year (QALY) gained of €19 341, and a €1.990 billion 15-year budget impact. Biennial screening ages 55 to 80 for those with 20 pack-years shows a lower mortality reduction (10.5%) and higher cost per QALY gained (€20 869). Despite model uncertainties, our estimates suggest there may be cost-effective screening policies for Switzerland. Risk-based biennial screening ages 55 to 80 for those with ≥1.6% PLCOm2012 risk conforms to CSC recommendations and is estimated to be more efficient than pack-year-based alternatives.

Effectiveness of a single low-dose computed tomography screening for lung cancer: A population-based perspective cohort study in China

The purpose of this perspective cohort study was to evaluate the effectiveness of low-dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non-screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow-up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non-screened group had the highest cumulative lung cancer mortality and all-cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12-1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49-0.97]), and the all-cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68-0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all-cause mortality in China.

Analysis of screening outcomes and factors influencing compliance among community-based lung cancer high-risk population in Nanchang, China, 2018-2020

Objective

To investigate the screening results and compliance of low-dose computed tomography (LDCT) screening among the high-risk lung cancer populations in Jiangxi Province from 2018 to 2020, and to explore the related influencing factors of compliance.

Methods

From November 2018 to October 2020, permanent residents in Nanchang City were selected and their demographic data and lung cancer risk factor data were collected to screen high-risk groups, and LDCT screening was performed on high-risk groups with diagnostic reports by 2 chief physicians. Descriptive analysis method was used to analyze the basic information of screening, screening results and screening compliance. χ2 and logistic regression test were used to conduct single and multi-factor analysis of screening compliance.

Results

A total of 26,588 people participated in this screening, of which 34.4% (n=9,139) were at high risk of lung cancer, 3,773 participants were completed LDCT screening, and the screening compliance rate was 41.3%. Screening results showed that 389 participants were positive for suspected pulmonary tumor or lung nodules, the screening positive rate of 10.3%. The logistic multivariable results of screening compliance showed that the compliance was better in males, those who quit smoking, those with chronic respiratory diseases and family history of cancer, and those who have primary education, those with a history of occupational harmful exposure had a poor compliance.

Conclusion

Compliance with lung cancer screening in Jiangxi Province, China still needs to be improved, and gender, education level, harmful occupational exposure, smoking, chronic respiratory diseases, and family history of tumors cancer play an important role on screening compliance.

Acceptability of Personalized Lung Cancer Screening Program Among Primary Care Providers

Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its benefit, only 5%-15% of eligible patients receive LCS. Personalized screening strategies select individuals based on their lung cancer risk and may increase LCS's effectiveness. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. We surveyed 32,983 Texas-based PCPs on an existing network (Protocol 2019-1257; PI: Dr. Shete) and 300 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. We analyzed the responses by subgroups of interest. Using nonparametric bootstrap, we derived an enriched dataset to develop logistic regression models to understand current LCS practices and acceptability of personalized LCS. Response rates were 0.3% (n = 91) and 15% (n = 60) for the 2019-1257 and TAFP surveys, respectively. Most (84%) respondents regularly assess LCS in their practice. Half of the respondents were interested in adopting personalized LCS. The majority (66%) of respondents expressed concerns regarding time availability with the personalized LCS. Most respondents would use biomarkers as an adjunct to assess eligibility (58%), or to help guide indeterminate clinical findings (63%). There is a need to enhance the engagement of Texas-based PCPs in LCS. Most of the respondents expressed interest in personalized LCS. Time availability was the main concern related to personalized LCS. Findings from this project highlight the need for better education of Texas-based PCPs on the benefits of LCS, and the development of efficient decision tools to ensure successful implementation of personalized LCS.

PREVENTION RELEVANCE

Personalized LCS facilitated by a risk model and/or a biomarker test is proposed as an alternative to existing programs. Acceptability of personalized approach among PCPs is unknown. The goal of this study is to assess the acceptability of personalized LCS among PCPs.

Lung Cancer Screening in Head and Neck Cancer Patients: An Untapped Opportunity

BACKGROUND

Despite evidence demonstrating that lung cancer screening (LCS) decreases mortality, widespread implementation is lagging. Efforts to identify and recruit patients for LCS are in need. Candidacy for LCS is based on identifiable risk factors, many of which overlap with those of head and neck malignancies. Thus, we aimed to evaluate the prevalence of candidacy for LCS in the head and neck cancer patient population.

METHODS

We performed a review of anonymous surveys collected from patients who presented to a head and neck cancer clinic. Variables collected from these surveys included age, biologic sex, smoking history, and head and neck cancer history. Patients' candidacy for screening was determined, and descriptive analyses were performed.

RESULTS

A total of 321 patient surveys were reviewed. Mean age was 63.7 years, and 195 (60.7%) were men. In this sample, 19 (5.91%) were current smokers, and 112 (34.9%) were former smokers, having quit an average of 19.4 years prior to completing the survey. Average pack-years was 29.3. Of the 321 patients surveyed, 60 (18.7%) would qualify for LCS using current guidelines. However, among those 60 patients who qualified for LCS, only 15 (25%) patients had been offered screening and only 14 (23.3%) had been screened.

CONCLUSIONS

We have importantly demonstrated both a substantial prevalence of candidacy for LCS in the head and neck cancer population as well as disappointingly low levels of screening utilization in this group of patients. We have identified this setting as a key patient population that ought to be targeted for information about and access to LCS.

Social and Biological Determinants in Lung Cancer Disparity

Lung cancer remains a leading cause of death in the United States and globally, despite progress in treatment and screening efforts. While mortality rates have decreased in recent years, long-term survival of patients with lung cancer continues to be a challenge. Notably, African American (AA) men experience significant disparities in lung cancer compared to European Americans (EA) in terms of incidence, treatment, and survival. Previous studies have explored factors such as smoking patterns and complex social determinants, including socioeconomic status, personal beliefs, and systemic racism, indicating their role in these disparities. In addition to social factors, emerging evidence points to variations in tumor biology, immunity, and comorbid conditions contributing to racial disparities in this disease. This review emphasizes differences in smoking patterns, screening, and early detection and the intricate interplay of social, biological, and environmental conditions that make African Americans more susceptible to developing lung cancer and experiencing poorer outcomes.

Pneumothorax and pulmonary hemorrhage after C-arm cone-beam computed tomography-guided percutaneous transthoracic lung biopsy: incidence, clinical significance, and correlation

OBJECTIVE

This study aimed to assess the incidence and clinical significance of pneumothorax (PTX) and pulmonary hemorrhage (PH) after percutaneous transthoracic lung biopsy (PTLB) guided by C-arm cone-beam computed tomography (CBCT). Furthermore, this study aimed to examine the relationships between PTX and PH with demographics, clinical characteristics, imaging, and PTLB parameters.

METHODS

A retrospective analysis was conducted on 192 patients who underwent PTLB at our hospital between January 2019 and October 2022. Incidences of PTX and PH were recorded. PTX was considered clinically significant if treated with chest tube insertion (CTI), and PH if treated with bronchoscopes or endovascular treatments. The various factors on PTX and PH were analyzed using the Chi-squared test and Student t-test. Logistic regression analyses were then used to determine these factors on the correlation to develop PTX and PH.

RESULTS

PTX occurred in 67/192 cases (34.9%); CTI was required in 5/67 (7.5%). PH occurred in 63/192 cases (32.8%) and none of these cases required bronchoscopes or endovascular treatments. Lesion diameter (ORPTX = 0.822; ORPH = 0.785), presence of pulmonary emphysema (ORPH = 2.148), the number of samples (ORPH = 1.834), the use of gelfoam (ORPTX = 0.474; ORPH = 0.341) and ablation (ORPTX = 2.351; ORPH = 3.443) showed statistically significant correlation to PTX and PH.

CONCLUSIONS

CBCT-guided PTLB is a safe and effective method for performing lung biopsies. The use of gelfoam has been shown to reduce the occurrence of PTX and PH. However, caution should be exercised when combining radiofrequency ablation with PTLB, as it may increase the risk of PTX and PH.

Effects of RNA methylation on Tumor angiogenesis and cancer progression

Tumor angiogenesis plays vital roles in the growth and metastasis of cancer. RNA methylation is one of the most common modifications and is widely observed in eukaryotes and prokaryotes. Accumulating studies have revealed that RNA methylation affects the occurrence and development of various tumors. In recent years, RNA methylation has been shown to play an important role in regulating tumor angiogenesis. In this review, we mainly elucidate the mechanisms and functions of RNA methylation on angiogenesis and progression in several cancers. We then shed light on the role of RNA methylation-associated factors and pathways in tumor angiogenesis. Finally, we describe the role of RNA methylation as potential biomarker and novel therapeutic target.

Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role?

BACKGROUND

In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility.

METHODS

The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination.

RESULTS

Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots).

DISCUSSION

A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.

[Analysis of Salivary Microbiota Characteristics in Patients With Pulmonary Nodules: A Prospective Nonrandomized Concurrent Controlled Trial]

Objective

To uncover and identify the differences in salivary microbiota profiles and their potential roles between patients with pulmonary nodules (PN) and healthy controls, and to propose new candidate biomarkers for the early warning of PN.

Methods

16S rRNA amplicon sequencing was performed with the saliva samples of 173 PN patients, or the PN group, and 40 health controls, or the HC group, to compare the characteristics, including diversity, community composition, differential species, and functional changes of salivary microbiota in the two groups. Random forest algorithm was used to identify salivary microbial markers of PN and their predictive value for PN was assessed by area under the curve (AUC). Finally, the biological functions and potential mechanisms of differentially-expressed genes in saliva samples were preliminarily investigated on the basis of predictive functional profiling of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2).

Results

The α diversity and β diversity of salivary microbiota in the PN group were higher than those in the HC group (P<0.05). Furthermore, there were significant differences in the community composition and the abundance of oral microorganisms between the PN and the HC groups (P<0.05). Random forest algorithm was applied to identify differential microbial species. Porphyromonas, Haemophilus, and Fusobacterium constituted the optimal marker sets (AUC=0.79, 95% confidence interval: 0.71-0.86), which can be used to effectively identify patients with PN. Bioinformatics analysis of the differentially-expressed genes revealed that patients with PN showed significant enrichment in protein/molecular functions involved in immune deficiency and redox homeostasis.

Conclusion

Changes in salivary microbiota are closely associated with PN and may induce the development of PN or malignant transformation of PN, which indicates the potential of salivary microbiota to be used as a new non-invasive humoral marker for the early diagnosis of PN.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma

BACKGROUND

Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers.

METHODS

The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level.

RESULTS

The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs.

CONCLUSION

We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

A novel preoperative image-guided localization for small pulmonary nodule resection using a claw-suture device

Video-assisted thoracoscopic surgery (VATS) provides better option concerning pathological diagnosis and curative intention of small pulmonary nodules (SPNs) that are sometimes challenging to localize. We assess the safety and feasibility of a new localization technique for SPNs, and report experience accumulated over time. A retrospective review of the new claw-suture localization cases between February 2018 and May 2023 was performed. Nodules were localized by a novel system that has an anchor claw and a tri-colored suture, guided by computed tomography (CT). Localization and operative procedure outcomes were then assessed. A total of 590 SPNs were localized from 568 patients before operation. The median nodule size was 0.70 cm (range, 0.3-2.0 cm). The claw-suture localization was successful without dislodgment or device fracture in 574 of 590 lesions (97.3%). Failures included not meeting target distance between claw and lesion (n = 13 [2.2%]), and device displacement (n = 3 [0.5%]). Complications requiring no further medical intervention included asymptomatic pneumothorax (n = 68 [11.5%]), parenchymal hemorrhage (n = 51 [8.6%]), and hemothorax (n = 1 [0.2%]) with the exception of pleural reaction observed in 2 cases (0.3%). Additionally, the depth of pulmonary nodules was significantly associated with the occurrence of pneumothorax (P = 0.036) and parenchymal hemorrhage (P = 0.000). The median duration of the localization was 12 min (range, 7-25 min). No patient complained of remarkable pain during the entire procedure. Retrieve of device after operation was 100%. The new localization technique is a safe, feasible, and well-tolerated method to localize SPNs for VATS resection.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening

RATIONALE AND OBJECTIVES

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer.

MATERIALS AND METHODS

This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images.

RESULTS

ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans.

CONCLUSION

A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Clinical Adjuncts to Lung Cancer Screening: A Narrative Review

The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.

Association between a family history of cancer and multiple primary lung cancer risks: a population-based analysis from China

OBJECTIVES

The incidence of multiple primary lung cancer (MPLC) has increased in recent years. The risk factors of MPLC are not well studied, especially in the Asian population. This case-control study investigated the association between a family history of cancer and MPLC risk.

METHODS

We used data from people who surgically confirmed MPLC with at least 2 nodes of Fujian Medical University Union Hospital and matched 1:2 normal individuals as controls between 2016 and 2017. Information on age, sex, lifestyle, personal history, and family history of cancer was collected using a self-administered questionnaire, and odds ratios (OR) were estimated using unconditional logistic regression.

RESULTS

We included 2 104 patients. In total, 321 patients with histologically confirmed MPLC and 642 healthy controls were studied. The significantly higher ratio of current smokers was observed for the cases than the controls (54.1% vs. 30.0%). A family history of LC in first-degree relatives of the cases reported a significantly higher proportion than in the controls (15.3% vs. 8.6%). Family history of all cancers and LC significantly increased the risk of MPLC (OR = 1.64, P = 0.009 and OR = 2.59, P = 0.000, respectively). The multivariate analysis identified a significantly increased risk of MPLC (OR = 2.45, P = 0.000) associated with parents and siblings influenced by LC history. The younger age (aged < 55 years) of LC cases at diagnosis exhibited a significantly increased risk of MPLC (OR = 2.39, P = 0.000). A significant association with a family history of LC was found for male squamous carcinoma and male adenocarcinoma (OR = 1.59, p = 0.037 and OR = 1.64, p = 0.032, respectively). A positive association with LC history was only observed for female adenocarcinoma (OR = 2.23, p = 0.028). The risk of MPLC was not significantly associated with A family history of cancers in non-smokers (OR = 0.91, P = 0.236). Ever-smokers with a positive family history of cancer or LC had a significantly elevated risk of MPLC (OR = 4.01, P = 0.000 and OR = 6.49, P = 0.000, respectively). We also observed a very elevated risk for smokers with no family history (OR = 3.49, P = 0.000). Such a positive association was also observed in ever-smokers with no family history of LC (OR = 3.55, P = 0.000). Adenocarcinoma in females was prevalent and significantly associated with a family history of LC in risk of MPLC compared with other histologic subtypes.

CONCLUSIONS

Our findings suggest an association between a family history of LC and MPLC risk among an Asian population. Smoking status and family history of LC have a synergistic effect on MPLC. These findings indicate that MPLC exhibits familiar aggregation and that inherited genetic susceptibility may contribute to the development of MPLC.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study

BACKGROUND

Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.

METHODS AND FINDINGS

For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts.

CONCLUSIONS

We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

Prediabetes and the risk of lung cancer incidence and mortality: A meta-analysis

AIMS/INTRODUCTION

There has been conflicting evidence regarding the role of prediabetes as a risk factor of lung cancer. A systemic review and meta-analysis was conducted to determine the relationship between prediabetes and lung cancer incidence and mortality in general adult populations.

MATERIALS AND METHODS

Observational studies relevant to the objective were found in Medline, Embase, Cochrane Library, and Web of Science. By incorporating potential heterogeneity into the model, a randomized-effects model was selected.

RESULTS

Ten cohort studies were included. People with prediabetes were associated with a mildly increased risk of lung cancer incidence compared with controls with normoglycemia (risk ratio [RR]: 1.09, 95% confidence interval [CI]: 1.01-1.18, P = 0.03; I2  = 79%), which was mainly observed in men rather than in women (RR: 1.07 vs 0.99, P for subgroup difference < 0.001). Prediabetes was related to a higher risk of lung cancer mortality (RR: 1.19, 95% CI: 1.02-1.39, P = 0.03; I2  = 52%), and the results were consistent in both men and women (P for subgroup difference = 0.67). The association between prediabetes and lung cancer incidence or mortality did not appear to be significantly affected by different definitions of prediabetes (P for subgroup difference = 0.27 and 0.37).

CONCLUSIONS

Prediabetes might be associated with a mildly increased risk of lung cancer incidence in men, but not in women. In addition, prediabetes may be related to a higher risk of lung cancer mortality in the adult population.

Optimizing Lung Cancer Screening With Risk Prediction: Current Challenges and the Emerging Role of Biomarkers

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.Lung cancer screening has been demonstrated to reduce lung cancer mortality, but its benefits must be weighed against the potential harms of unnecessary procedures, false-positive radiological findings, and overdiagnosis. Individuals at highest risk of lung cancer are more likely to maximize benefits while minimizing harm from screening. Although current lung cancer screening guidelines recommended by the US Preventive Services Task Force (USPSTF) only consider age and smoking history for screening eligibility, National Comprehensive Cancer Network and other society guidelines recommend screening on the basis of individualized risk assessment including family history, environmental exposures, and presence of chronic lung disease. Risk prediction models have been developed to integrate various risk factors into an individualized risk prediction score. Previous evidence showed that risk prediction model-based screening eligibility could improve sensitivity for detecting lung cancer cases without reducing specificity. Furthermore, recent advances in lung cancer biomarkers have enhanced the performance of risk prediction in identifying lung cancer cases relative to the USPSTF criteria. These risk prediction models can be used to guide shared decision-making discussions before proceeding with lung cancer screening. This study aims to provide a concise overview of these prediction models and the emerging role of biomarker testing in risk prediction to facilitate conversations with patients. The goal was to assist clinicians in assessing individual patient risk, leading to more informed decision making.

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm

Background

At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.

Method

In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).

Result

A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.

Conclusion

We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography

PURPOSE

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images.

MATERIALS AND METHODS

Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently.

RESULTS

The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set.

CONCLUSION

The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Prevalence of hospitalized women at high-risk for developing lung cancer

BACKGROUND

Lung cancer screening with low-dose computer tomography (CT) has been shown to reduce the lung cancer mortality in high-risk individuals by 20%. Despite the proven mortality benefit, the utilization of lung cancer screening among high-risk populations remains low.

OBJECTIVE

This study explores the prevalence of high-risk population for developing lung cancer among hospitalized women and evaluates the screening behavior toward other common cancers during a hospital stay.

METHODS

This is a cross-sectional study in which 248 cancer-free hospitalized women aged 50-75 years who reported current or prior smoking were enrolled during hospital admission at an academic center. A bedside survey was conducted to collect socio-demographic, cancer screening behavior, and medical comorbidities for the study patients. Unpaired t-test and Chi-square tests were used to compare characteristics and common cancer screening behavior by lung cancer risk stratification.

RESULTS

Forty-three percent of the hospitalized women were at intermediate to high-risk for developing lung cancer risk. Intermediate to high-risk women were more likely to be older, Caucasian, retired, or with a disability, and had higher comorbidity burden as compared to the low-risk group. Women at low and intermediate to high risk were equally non-adherent with breast (35% vs 31%, p = 0.59) and colorectal (32% vs 24%, p = 0.20) cancers screening guidelines. Only 38% of women from the intermediate to the high-risk group had a CT chest within the last year.

CONCLUSION

The study's findings suggest that almost half of the hospitalized women who report current or past smoking are at high-risk for developing lung cancer.

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.