Lung cancer screening

Integrated biosensor array for multiplex biomarkers cancer diagnosis via in-situ self-assembly carbon nanotubes with an ordered inverse-opal structure

To enhance the precision and reliability of early disease detection, especially in malignancies, an exhaustive investigation of multi-target biomarkers is essential. In this study, an advanced integrated electrochemical biosensor array that demonstrates exceptional performance was constructed. This biosensor was developed through a controllable porous-size mechanism and in-situ modification of carbon nanotubes (CNTs) to quantify multiplex biomarkers-specifically, C-reaction protein (CRP), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA)-in human serum plasma. The fabrication process involved creating a highly ordered three-dimensional inverse-opal structure with the CNTs (pCNTs) modifier through microdroplet-based microfluidics, confined spatial self-assembly of nanoparticles, and chemical wet-etching. This innovative approach allowed for direct in-situ modification of nanomaterial onto the surface of electrode array, eliminating secondary transfer and providing exceptional control over structure and stability. The outstanding electrochemical performance was achieved through the synergistic effect of the pCNTs nanomaterial, aptamer, and horseradish peroxidase-labeled (HRP-) antibody. Additionally, the integrated biosensor array platform comprised multiple individually addressable electrode units (n = 11), enabling simultaneous multi-parallel/target testing, thereby ensuring accuracy and high throughput. Crucially, this integrated biosensor array accurately quantified multiplex biomarkers in human serum, yielding results comparable to commercial methods. This integrated technology holds promise for point-of-care testing (POCT) in early disease diagnosis and biological analysis.

Lung cancer and lymphangioleiomyomatosis: A common complication in an uncommon condition

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Crosstalk among disulfidptosis-related lncRNAs in lung adenocarcinoma reveals a correlation with immune profile and clinical prognosis

Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.

Smoking as a mediator in the association between major depressive disorder and schizophrenia on lung cancer risk: a bidirectional/multivariable and mediation Mendelian randomization study

Background & Aims

Major depressive disorder and schizophrenia have been hypothesized to be closely associated with cancer. However, the associations between these psychiatric conditions and the development of lung cancer remain uncertain. This study aimed to explore the causal relationship among major depressive disorder, schizophrenia, and the risk of lung cancer.

Methods

Two-sample bidirectional/multivariable and mediation Mendelian randomization (MR) analyses were conducted. Genome-wide summary data on major depressive disorder (N=500,199) and schizophrenia (N=127,906) were utilized. Data on the risk of lung cancer (overall, adenocarcinoma, and squamous cell) were collected from a cohort of individuals of European ancestry (N=27,209). Three smoking-related behaviors (smoking initiation, pack years of smoking, and cigarettes smoked per day) were included in the multivariable and mediation MR analyses.

Results

Patients with schizophrenia had a significantly greater risk of developing lung cancer (odds ratio (OR) = 1.144, 95% confidence interval (95% CI): 1.048-1.248, P = 0.003). The number of cigarettes smoked per day partially mediated the relationship between schizophrenia and the overall risk of lung cancer (OR = 1.185, 95% CI: 1.112-1.264, P = 0.021, proportion of mediation effect: 61.033%). However, there is no reliable evidence indicating an association between major depressive disorder and the risk of lung cancer (overall, adenocarcinoma, and squamous cell cancer).

Conclusions

The findings indicated an association between schizophrenia and an increased risk of lung cancer, with smoking served as a partial mediator. When smoking was included in the regression analysis, the explanatory power of schizophrenia diagnosis was reduced, suggesting that smoking may be an important causal contributor to lung cancer in this population. Given the high prevalence of smoking among individuals with schizophrenia, these results underscore the need for further research to explore the underlying mechanisms of smoking's impact. Consequently, greater emphasis should be placed on monitoring the respiratory health of individuals with schizophrenia and implementing early interventions to address smoking-related behaviors.

Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework

BACKGROUND

Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning-based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation tasks, there are differences in efficiency between large and small objects and limited segmentation effects on objects of individual sizes. The previous segmentation frameworks still have room for improvement in multi-scale collaboration when segmenting objects.

PURPOSE

This paper proposes a method to train a deep learning segmentation framework using a feature pyramid processing dataset to improve the average precision (AP) index, and realizes multi-scale cooperation in target segmentation.

OBJECTIVE

Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (PanNuke) dataset was selected to include approximately 7500 pathology images with cells from 19 different types of tissues, including five classifications of cancer, non-cancer, inflammation, death, and connective tissue.

METHODS

First, the method uses whole-slide images in the pan-cancer histology dataset for nuclei instance segmentation and classification (PanNuke) dataset, combined with the mask region convolutional neural network (Mask R-CNN) segmentation framework and improved loss function to segment and detect each cellular tissue in cancerous sections. Second, to address the problem of non-synergistic object segmentation at different scales in cancerous tissue segmentation, a scheme using feature pyramids to process the dataset was adopted as part of the feature extraction module.

RESULTS

Extensive experimental results on this dataset show that the method in this paper yields 0.269 AP and a boost of about 4% compared to the original Mask R-CNN framework.

CONCLUSIONS

It is effective and feasible to use feature pyramid to process data set to improve the effect of medical image segmentation.

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

Background

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer.

Methods

Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed.

Results

The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115.

Conclusion

LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Demethylzeylasteral exerts potent efficacy against non-small-cell lung cancer via the P53 signaling pathway

Lung cancer has one of the highest mortality rates worldwide, with non-small-cell lung cancer (NSCLC) constituting approximately 85% of all cases. Demethylzeylasteral (DEM), extracted from Tripterygium wilfordii Hook F, exhibits notable anti-tumor properties. In this study, we revealed that DEM could effectively induce NSCLC cell apoptosis. Specifically, DEM can dose-dependently suppress the viability and migration of human NSCLC cells. RNA-seq analysis revealed that DEM regulates the P53-signaling pathway, which was further validated by assessing crucial proteins involved in this pathway. Biacore analysis indicated that DEM has high affinity with the P53 protein. The CDX model demonstrated DEM's anti-tumor actions. This work provided evidence that DEM-P53 interaction stabilizes P53 protein and triggers downstream anti-tumor activities. These findings indicate that DEM treatment holds promise as a potential therapeutic approach for NSCLC, which warrants further clinical assessment in patients with NSCLC.

Early Detection and Interception of Lung Cancer

Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.

Ultrasound for Intra-Operative Detection of Peri-Centimetric Pulmonary Nodules in Uniportal Video-Assisted Thoracic Surgery (VATS): A Comparison with Conventional Techniques in Multiportal VATS

Background: Video-assisted thoracic surgery (VATS) has become the gold-standard approach for lung resections. Given the impossibility of digital palpation, we witnessed the progressive development of peri-centimetric and deeply located pulmonary nodule alternative detection techniques. Intra-operative lung ultrasound is an increasingly effective diagnostic method, although only a few small studies have evaluated its accuracy. This study analyzed the effectiveness and sensitivity of uniportal VATS with intra-operative lung ultrasound (ILU), in comparison to multiportal VATS, for visualizing solitary and deep-sited pulmonary nodules. Methods: Patient data from October 2021 to October 2023, from a single center, were retrospectively gathered and analyzed. In total, 31 patients who received ILU-aided uniportal VATS (Group A) were matched for localization time, operative time, sensitivity, and post-operative complications, with 33 undergoing nodule detection with conventional techniques, such as manual or instrumental palpation, in multiportal VATS (Group B). Surgeries were carried out by the same team and ILU was performed by a certified operator. Results: Group A presented a significantly shorter time for nodule detection [median (IQR): 9 (8-10) vs. 14 (12.5-15) min; p < 0.001] and operative time [median (IQR): 33 (29-38) vs. 43 (39-47) min; p < 0.001]. All nodules were correctly localized and resected in Group A (sensitivity 100%), while three were missed in Group B (sensitivity 90.9%). Two patients in Group B presented with a prolonged air leak that was conservatively managed, compared to none in Group A, resulting in a post-operative morbidity rate of 6.1% vs. 0% (p = 0.16). Conclusions: ILU-aided uniportal VATS was faster and more effective than conventional techniques in multiportal VATS for nodule detection.

HTR3A Promotes Non-small Cell Lung Cancer Through the FOXH1/Wnt3A Signaling Pathway

5-Hydroxytryptamine receptors (5-HTRs) are strongly correlated with tumor progression in various types of cancer. Despite this, the underlying mechanisms responsible for the role of 5-HTRs in non-small cell lung cancer (NSCLC) remains unclear. This study aimed to investigate the relationship between 5-hydroxytryptamine receptor 3A (HTR3A) and NSCLC development. Our findings indicated a higher distribution of HTR3A expression in NSCLC tissues when compared with normal tissues, where patients with high HTR3A levels demonstrated shorter overall survival times. In vitro analyses revealed that overexpression of HTR3A facilitated the proliferation and migration of NSCLC cell lines (A549 and NCI-H3255). Similarly, a notable acceleration of tumor growth and enhanced pulmonary tumorigenic potential were observed in HTR3A-overexpressing tumor-bearing mice. Mechanistically, upregulation of Forkhead Box H1 (FOXH1) by HTR3A led to the activation of Wnt3A/β-catenin signaling pathways, thereby promoting the development of NSCLC. Our report thus highlights the significance of the HTR3A/FOXH1 axis during tumor progression in NSCLC, proposing HTR3A as a possible diagnostic indicator and candidate target for clinical therapy.

Targeting hyaluronan metabolism-related molecules associated with resistant tumor-initiating cells potentiates chemotherapy efficacy in lung cancer

The success of chemotherapy regimens in patients with non-small cell lung cancer (NSCLC) could be restricted at least in part by cancer stem cells (CSC) niches within the tumor microenvironment (TME). CSC express CD133, CD44, CD47, and SOX2, among other markers and factors. Analysis of public data revealed that high expression of hyaluronan (HA), the main glycosaminoglycan of TME, correlated positively with CSC phenotype and decreased disease-free interval in NSCLC patients. We aimed to cross-validate these findings on human and murine lung cancer cells and observed that CD133 + CSC differentially expressed higher levels of HA, HAS3, ABCC5, SOX2, and CD47 (p < 0.01). We modulated HA expression with 4-methylumbelliferone (4Mu) and detected an increase in sensitivity to paclitaxel (Pa). We evaluated the effect of 4Mu + chemotherapy on survival, HA metabolism, and CSC profile. The combination of 4Mu with Pa reduced the clonogenic and tumor-forming ability of CSC. Pa-induced HAS3, ABCC5, SOX2, and CD47 expression was mitigated by 4Mu. Pa + 4Mu combination significantly reduced in vivo tumor growth, enhancing animal survival and restoring the CSC profile in the TME to basal levels. Our results suggest that HA is involved in lung CSC phenotype and chemosensitivity, and its modulation by 4Mu improves treatment efficacy to inhibit tumor progression.

The pros and cons of lung cancer screening

Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.

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

BACKGROUND

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data.

METHODS

Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features.

RESULTS

For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer.

CONCLUSION

This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Development and validation of a TAAbs and TAAs based non-invasive model for diagnosing lung cancer

Background

Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection.

Methods

1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis.

Results

The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis.

Conclusions

This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.

Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.

Barriers to Completing Low Dose Computed Tomography Scan for Lung Cancer Screening

INTRODUCTION

Annual low-dose computed tomography (LDCT) screening has been shown to reduce lung cancer mortality in high-risk individuals by detecting the disease at an earlier stage. This study aims to assess the barriers to completing LDCT in a cohort of patients who were determined eligible for lung cancer screening (LCS).

METHODS

We performed a single institution, mixed methods, cross-sectional study of patients who had a LDCT ordered from July to December 2022. We then completed phone surveys with patients who did not complete LDCT to assess knowledge, attitude, and perceptions toward LCS.

RESULTS

We identified 380 patients who met inclusion criteria, including 331 (87%) who completed LDCT and 49 (13%) who did not. Patients who completed a LDCT and those who did not were similar regarding age, sex, race, primary language, household income, body mass index, median pack years, and quit time. Positive predictors of LDCT completion were: meeting USPSTF guidelines (97.9% vs 81.6%), being married (58.3% vs 44.9%), former versus current smokers (55% vs 41.7%), personal history of emphysema (60.4% vs 42.9%), and family history of lung cancer (13.9% vs 4.1%) (all P < .05). Of the patients who participated in the phone survey, only 7% of respondents thought they were high risk for developing lung cancer despite attending a shared decision-making visit and only 10% wanted to re-schedule their LDCT.

CONCLUSION

There exist barriers to completing LDCT even after patients are identified as eligible and complete a shared decision-making visit secondary to knowledge barriers, misperceptions, and patient disinterest.

Detection of PD‑L1 expression and epithelial‑mesenchymal transition of circulating tumor cells in non‑small cell lung cancer

The present study aimed to assess the roles of peripheral circulating tumor cell (CTC) count, CTC subtypes and programmed death ligand 1 (PD-L1) expression in the clinical staging and prognosis of patients with non-small cell lung cancer (NSCLC). A total of 100 patients with NSCLC with available tumor tissues were enrolled in the present study, and 7.5 ml peripheral blood was collected. Patients were divided into PD-L1-positive and PD-L1-negative groups according to PD-L1 immunohistochemical staining. Peripheral blood samples from both groups were analyzed to determine the CTC count, epithelial-type CTCs (E-CTCs), mesenchymal-type CTCs (M-CTCs) and PD-L1 expression. Clinical data were collected, and patients were followed up for a maximum of 36 months, with patient death as the endpoint event. Patients with PD-L1-positive tumors had a worse prognosis compared with those with PD-L1-negative tumors (P=0.045). The PD-L1-positive group exhibited significantly higher numbers of CTCs and M-CTCs compared with the PD-L1-negative group (P≤0.05). However, the number of E-CTCs did not differ significantly between the two groups (P>0.05). PD-L1-positive patients with higher CTC and M-CTC counts had relatively poorer prognoses (P≤0.05), while the number of E-CTCs had no significant effect on prognosis (P>0.05). Compared with the early-stage NSCLC group, the late-stage NSCLC group exhibited a significant increase in the CTC count (P≤0.05), while E-CTC and M-CTC counts did not significantly differ between the two groups (P>0.05). The PD-L1-positive group exhibited a significant increase in the number of PD-L1+ CTCs and PD-L1+ M-CTCs compared with the PD-L1-negative group (P≤0.05), while PD-L1+ E-CTC counts did not differ significantly between the two groups (P>0.05). The PD-L1-positive patients with a higher number of PD-L1+ CTCs and PD-L1+ M-CTCs had relatively poorer prognoses (P≤0.05), while the PD-L1+ E-CTC count had no significant effect on prognosis (P>0.05). Compared with the early-stage NSCLC group, the late-stage NSCLC group exhibited a significant increase in the number of PD-L1+ CTCs and PD-L1+ M-CTCs (P≤0.05), while PD-L1+ E-CTC counts did not significantly differ between the two groups (P>0.05). Based on univariate and multivariate analyses, the number of PD-L1+ M-CTCs was identified as an independent prognostic factor for NSCLC. In conclusion, the presence of CTCs in peripheral blood, particularly PD-L1+ M-CTC subtype, indicated poorer clinical staging and prognosis in patients with NSCLC. These findings suggested that CTCs, specifically the PD-L1+ M-CTC subtype, could serve as a monitoring indicator for the clinical staging and prognosis of patients with NSCLC.

Causal relationships between immune cell phenotypes and lung adenocarcinoma: A bidirectional two-sample Mendelian randomization study

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and closely associated with the immune system. Emerging evidence suggests that blood immune cell phenotypes in patients with LUAD may undergo alterations. Nevertheless, the limited amount of relevant research makes it difficult to understand the causal links between LUAD and changes in the immune cells. This study aimed to reveal the potential causal relationships between 731 immune cell phenotypes and LUAD.

METHODS

A bidirectional two-sample Mendelian randomization (MR) analysis was used to clarify causal relationships. Four types of immune phenotypes, absolute cell counts, relative cell counts, median fluorescence intensities (MFIs) of surface antigens, and morphological parameters, were investigated in this study. Heterogeneity tests, horizontal pleiotropy tests, and leave-one-out analyses were performed to validate the reliability of our study.

RESULTS

A total of 26 immune cell characteristics were identified as contributing to the occurrence of LUAD. Memory B cells, IgD-CD38br cells, CD4+ regulatory T cells (Tregs), and plasmacytoid dendritic cells (DCs) may play a role in the development of LUAD. Through reverse MR, our study discovered that the presence of LUAD also induced changes in the expression levels of 16 immune cell traits involving specific surface markers and various types of immune cells, some of which pertain to antigen presentation and immune activation processes.

CONCLUSION

Our study demonstrated causal links between several immune cell phenotypes and LUAD, thereby providing indications of the potentially oncogenic physiological state and early screening biomarkers for future research.

Assessing the efficiency of eligibility criteria for low-dose computed tomography lung screening in China according to current guidelines

BACKGROUND

Evidence from observational studies indicates that lung cancer screening (LCS) guidelines with high rates of lung cancer (LC) underdiagnosis, and although current screening guidelines have been updated and eligibility criteria for screening have been expanded, there are no studies comparing the efficiency of LCS guidelines in Chinese population.

METHODS

Between 2005 and 2022, 31,394 asymptomatic individuals were screened using low-dose computed tomography (LDCT) at our institution. Demographic data and relevant LC risk factors were collected. The efficiency of the LCS for each guideline criteria was expressed as the efficiency ratio (ER). The inclusion rates, eligibility rates, LC detection rates, and ER based on the different eligibility criteria of the four guidelines were comparatively analyzed. The four guidelines were as follows: China guideline for the screening and early detection of lung cancer (CGSL), the National Comprehensive Cancer Network (NCCN), the United States Preventive Services Task Force (USPSTF), and International Early Lung Cancer Action Program (I-ELCAP).

RESULTS

Of 31,394 participants, 298 (155 women, 143 men) were diagnosed with LC. For CGSL, NCCN, USPSTF, and I-ELCAP guidelines, the eligibility rates for guidelines were 13.92%, 6.97%, 6.81%, and 53.46%; ERe for eligibility criteria were 1.46%, 1.64%, 1.51%, and 1.13%, respectively; and for the inclusion rates, they were 19.0%, 9.5%, 9.3%, and 73.0%, respectively. LCs which met the screening criteria of CGSL, NCCN, USPSTF, and I-ELCAP guidelines were 29.2%, 16.4%, 14.8%, and 86.6%, respectively. The age and smoking criteria for CGSL were stricter, hence resulting in lower rates of LC meeting the screening criteria. The CGSL, NCCN, and USPSTF guidelines showed the highest underdiagnosis in the 45-49 age group (17.4%), while the I-ELCAP guideline displayed the highest missed diagnosis rate (3.0%) in the 35-39 age group. Males and females significantly differed in eligibility based on the criteria of the four guidelines (P < 0.001).

CONCLUSIONS

The I-ELCAP guideline has the highest eligibility rate for both males and females. But its actual efficiency ratio for those deemed eligible by the guideline was the lowest. Whereas the NCCN guideline has the highest ERe value for those deemed eligible by the guideline.

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.

Biological and therapeutic implications of the cancer-related germline mutation landscape in lung cancer

Although smoking is the primary cause of lung cancer, only about 15% of lifelong smokers develop the disease. Moreover, a substantial proportion of lung cancer cases occur in never-smokers, highlighting the potential role of inherited genetic factors in the cause of lung cancer. Lung cancer is significantly more common among those with a positive family history, especially for early-onset disease. Therefore, the presence of pathogenic germline variants might act synergistically with environmental factors. The incorporation of next-generation sequencing in routine clinical practice has led to the identification of cancer-predisposing mutations in an increasing proportion of patients with lung cancer. This Review summarises the landscape of germline susceptibility in lung cancer and highlights the importance of germline testing in patients diagnosed with the disease, which has the potential to identify individuals at risk, with implications for tailored therapeutic approaches and successful prevention through genetic counselling and screening.

Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis

OBJECTIVE

This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.

METHODS

A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.

RESULTS

The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).

CONCLUSIONS

Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques

Purpose

The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to identify and screen for osteoporosis in patients.

Method

A total of 488 patients who had undergone chest CT and bone turnover marker testing, and had known bone mineral density, were included in this study. ITK-SNAP software was used to delineate regions of interest, while radiomics features were extracted using Python. Multiple 2D and 3D deep learning models were trained to identify these regions of interest. The effectiveness of these techniques in screening for osteoporosis in patients was compared.

Result

Clinical models based on gender, age, and β-cross achieved an accuracy of 0.698 and an AUC of 0.665. Radiomics models, which utilized 14 selected radiomics features, achieved a maximum accuracy of 0.750 and an AUC of 0.739. The test group yielded promising results: the 2D Deep Learning model achieved an accuracy of 0.812 and an AUC of 0.855, while the 3D Deep Learning model performed even better with an accuracy of 0.854 and an AUC of 0.906. Similarly, the 2D Transfer Learning model achieved an accuracy of 0.854 and an AUC of 0.880, whereas the 3D Transfer Learning model exhibited an accuracy of 0.740 and an AUC of 0.737. Overall, the application of 3D deep learning and 2D transfer learning techniques on chest CT scans showed excellent screening performance in the context of osteoporosis.

Conclusion

Bone turnover markers may not be necessary for osteoporosis screening, as 3D deep learning and 2D transfer learning techniques utilizing chest CT scans proved to be equally effective alternatives.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Background

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients.

Methods

The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves.

Findings

Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients.

Interpretation

This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies.

Funding

This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

LncRNA RGMB-AS1 inhibits HMOX1 ubiquitination and NAA10 activation to induce ferroptosis in non-small cell lung cancer

Ferroptosis, an iron-dependent regulated cell death caused by excessive lipid peroxide accumulation, has emerged as a promising therapeutic target in various cancers, including non-small cell lung cancer (NSCLC). In this study, we identified the long non-coding RNA RGMB-AS1 as a key regulator of ferroptosis in NSCLC. Mechanistically, RGMB-AS1 interacted with heme oxygenase 1 (HMOX1) and prevented its ubiquitination by the E3 ligase TRC8, leading to increased HMOX1 stability and enhanced ferroptosis. Additionally, RGMB-AS1 bound to the 82-87 amino acid region of N-alpha-acetyltransferase 10 (NAA10), stimulating its acetyltransferase activity and promoting the conversion of acetyl-CoA to HMG-CoA, further contributing to ferroptosis. The RGMB-AS1-HMOX1 and RGMB-AS1-NAA10 axes synergistically inhibited NSCLC growth both in vitro and in vivo. Clinically, low RGMB-AS1 expression was associated with advanced tumor stage and poor overall survival in NSCLC patients. Furthermore, adeno-associated virus-mediated RGMB-AS1 overexpression significantly suppressed tumor growth in mouse xenograft models. Our findings uncover a novel lncRNA-mediated regulatory mechanism of ferroptosis and highlight the potential of RGMB-AS1 as a prognostic biomarker and therapeutic target in NSCLC.

Efficacy of Opportunistic Screening with Chest CT in Identifying Osteoporosis and Osteopenia in Patients with T2DM

Purpose

To explore the validity of the thoracic spine Hounsfield Unit (HU) measured by chest computed tomography (CT) for opportunistic screening of diabetic osteoporosis. The current study attempted to establish a diagnostic threshold for thoracic spine HU in a type 2 diabetes mellitus (T2DM) population with osteoporosis.

Patients and Methods

The current study retrospectively included 334 patients with T2DM. They underwent chest CT and Dual-energy X-ray (DXA) between August 2021 and January 2022 in our hospital. HU values were measured on the resulting chest CT images at thoracic spine 11 and 12 to construct regions of interest. All patients were grouped according to the lowest T-value of DXA examination: osteoporosis, osteopenia and normal bone density. HU values were compared with T-values in each group of patients, and receiver operating characteristics curves were plotted to calculate diagnostic thresholds as well as sensitivity and specificity.

Results

There was a strong correlation between the HU values of chest CT and the T-values of DXA (p < 0.01). The sensitivity for osteoporosis was 88.7% for T11 attenuation≤ 98 HU and the specificity for osteoporosis was 87.5% for T12 attenuation ≤ 117HU; the specificity for normal BMD was 85.4% for T11 attenuation ≥ 147 HU and 82% for T12 attenuation ≥ 146 HU.

Conclusion

Chest CT can be used to screen patients with T2DM for opportunistic osteoporosis and help determine if they need DXA screening. The current study suggests that when the HU threshold of T11 ≤ 98/T12 ≤ 117, patients may need further osteoporosis screening.

Morphological Predictors of Primary Lung Cancer among Part-Solid Ground-Grass Nodules on High-Resolution CT

Recent advancements in computed tomography (CT) scanning have improved the detection rates of peripheral pulmonary nodules, including those with ground-glass opacities (GGOs). This study focuses on part-solid pure ground-glass nodules (GGNs) and aims to identify imaging predictors that can reliably differentiate primary lung cancer from nodules with other diagnoses among part-solid GGNs on high-resolution CT (HRCT). A retrospective study was conducted on 609 patients who underwent surgical treatment or observation for lung nodules. Radiological findings from pre-operative HRCT scans were reviewed and several CT imaging features of part-solid GGNs were examined for their positive predictive value to identify primary lung cancer. The proportions of the nodules with a final diagnosis of primary lung cancer were significantly higher in part-solid GGNs (91.9%) compared with solid nodules (70.3%) or pure GGNs (66.7%). Among CT imaging features of part-solid GGNs that were evaluated, consolidation-to-tumor ratio (CTR) < 0.5 (98.1%), pleural indentation (96.4%), and clear tumor border (96.7%) had high positive predictive value to identify primary lung cancer. When two imaging features were combined, the combination of CTR < 0.5 and a clear tumor border was identified to have 100% positive predictive values with a sensitivity of 40.8%. Thus we conclude that part-solid GGNs with a CTR < 0.5 accompanied by a clear tumor border evaluated by HRCT are very likely to be primary lung cancers with an acceptable sensitivity. Preoperative diagnostic procedures to obtain a pathological diagnosis may potentially be omitted in patients harboring such part-solid GGNs.

Diagnostic value of combined detection of plasma cfDNA concentration and integrity in NSCLC

Aim: To evaluate the value of combined detection of plasma cfDNA concentration and integrity in the early diagnosis of NSCLC. Methods: Real-time fluorescence quantitative PCR was used to determine the concentration and integrity of plasma cfDNA in 71 NSCLC patients and 53 healthy people. Results: Combined detection of plasma cfDNA concentration and integrity had higher diagnostic power in differentiating NSCLC patients with stage I/II from healthy people than detection of plasma cfDNA concentration alone or integrity alone. The AUC, sensitivity and specificity of the combined detection of plasma cfDNA concentration and integrity were 0.781, 0.62 and 0.85. Conclusion: Combined detection of plasma cfDNA concentration and integrity could improve the diagnostic value in NSCLC detection.

Global research trends in immunotherapy for non-small cell lung cancer patients with KRAS mutations: a bibliometric analysis

Background

Immunotherapy, frequently combined with conventional chemotherapy, is crucial for treating NSCLC. Kirsten rat sarcoma virus (KRAS) is a poor prognostic factor in patients with NSCLC, particularly lung adenocarcinoma, where binding of conventional inhibitors to mutated KRAS proteins is challenging. Field profiles, research hotspots, and prospects for immunotherapy for patients with NSCLC-carrying KRAS mutations were uncovered in this study.

Methods

Microsoft Excel 2019, Bibliometrix, VOSviewer software, and Citespace were utilized to conduct a comprehensive scientometric analysis and understand a specific research field's knowledge base and frontiers aided by bibliometrics.

Results

Between 2014 and 2023, 398 eligible documents in the English language were acquired using the WoSCC database, of which 113 and 285 were reviews and articles, respectively. The growth rate per year was 34.25 %. The most cited articles were from the United States, and China published the highest number of articles. Cancers was the journal, with increased publications in recent years. The keywords with the strongest citation bursts were analyzed using Citespace. "Immune checkpoint inhibitors," "co-occurring genomic alterations," and "KRAS" are among the research hotspots in this field.

Conclusion

Using bibliometric and visual analyses, we examined immunotherapy for patients with KRAS-mutant NSCLC over the previous decade. The whole analysis showed a steady, quick increase in yearly publications in this area. Our findings will provide a roadmap for future research on the mechanisms of immunotherapy and immune checkpoint inhibitor action in treating KRAS-mutant NSCLC.

An analysis of the influencing factors of false negative autoantibodies in patients with non-small cell lung cancer

Objectives

To analyze the clinical significance of seven autoantibodies (P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE, and CAGE) in patients with non-small cell lung cancer (NSCLC) and the factors that influence false-negative results.

Methods

Seven autoantibodies were measured in the serum of 502 patients with non-small cell lung cancer (NSCLC) using ELISA, and their correlations with age, sex, smoking history, pathological type, clinical stage, and PD-L1 gene expression were analyzed. The clinicopathological data of the false-negative and positive groups for the seven autoantibodies were compared to determine the influencing factors.

Results

P53 antibody expression level was correlated with lobulation sign, PGP9.5 antibody expression level with sex and vascular convergence; SOX2 antibody expression level with pathological type, clinical stage, and enlarged lymph nodes; and MAGE antibody expression level with the pathological type (P<0.05). False-negative autoantibodies are prone to occur in lung cancer patients with ground-glass nodules, no enlarged lymph nodes, no vascular convergence, and PD-L1 gene expression <1% (P <0.05).

Conclusion

Detection of seven autoantibodies was clinically significant in patients with NSCLC. However, poor sensitivity should be considered in clinical diagnoses to prevent missed diagnoses.

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Cell-free DNA methylation in the clinical management of lung cancer

The clinical use of cell-free DNA (cfDNA) methylation in managing lung cancer depends on its ability to differentiate between malignant and healthy cells, assign methylation changes to specific tissue sources, and elucidate opportunities for targeted therapy. From a technical standpoint, cfDNA methylation analysis is primed as a potential clinical tool for lung cancer screening, early diagnosis, prognostication, and treatment, pending the outcome of elaborate validation studies. Here, we discuss the current state of the art in cfDNA methylation analysis, examine the unique features and limitations of these new methods in a clinical context, propose two models for applying cfDNA methylation data for lung cancer screening, and discuss future research directions.

Noteworthy impacts of COVID-19 pandemic on cancer screening: A systematic review

The sudden onset of the coronavirus disease 2019 (COVID-19) in January 2020 has affected essential global health services. Cancer-screening services that can reduce cancer mortality are strongly affected. However, the specific role of COVID-19 in cancer screening is not fully understood. This study aimed to assess the efficiency of global cancer screening programs before and during the COVID-19 pandemic and to promote potential cancer-screening strategies for the next pandemic. Electronic searches in PubMed, Embase, and Web of Science, and manual searches were performed between January 1, 2020 and March 1, 2023. Cohort studies that reported the number of participants who underwent cancer screening before and during the COVID-19 pandemic were included. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Differences in cancer-screening rates were estimated using the incidence rate ratio (IRR). Fifty-five cohort studies were included in this meta-analysis. The screening rates of colorectal cancer using invasive screening methods (Pooled IRR = 0.52, 95% CI: 0.42 to 0.65, p < 0.01), cervical cancer (Pooled IRR = 0.56, 95% CI: 0.47 to 0.67, p < 0.01), breast cancer (Pooled IRR = 0.57, 95% CI: 0.49 to 0.66, p < 0.01) and prostate cancer (Pooled IRR = 0.71, 95% CI: 0.56 to 0.90, p < 0.01) during the COVID-19 pandemic were significantly lower than those before the COVID-19 pandemic. The screening rates of lung cancer (Pooled IRR = 0.77, 95% CI: 0.58 to 1.03, p = 0.08) and colorectal cancer using noninvasive screening methods (Pooled IRR = 0.74, 95% CI: 0.50 to 1.09, p = 0.13) were reduced with no statistical differences. The subgroup analyses revealed that the reduction in cancer-screening rates varied across economies. Our results suggest that the COVID-19 pandemic has had a noteworthy impact on colorectal, cervical, breast, and prostate cancer screening. Developing innovative cancer-screening technologies is important to promote the efficiency of cancer-screening services in the post-COVID-19 era and prepare for the next pandemic.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Real-World Treatment Patterns and Survival Outcomes for Patients with Non-Metastatic Non-Small-Cell Lung Cancer in Sweden: A Nationwide Registry Analysis from the I-O Optimise Initiative

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, with ~40-50% of patients diagnosed with non-metastatic disease (stages IA-IIIC). The treatment landscape is evolving rapidly as immunotherapies and targeted therapy are introduced in the non-metastatic setting, creating a need to assess patient outcomes prior to their introduction. This real-world study using Swedish National Lung Cancer Registry data examined outcomes (overall survival (OS) and time to next treatment or death (TTNTD)) and treatment patterns for adults diagnosed with non-metastatic NSCLC. Baseline characteristics and OS from diagnosis were described for all patients; OS, treatment patterns, and TTNTD from treatment start were described for the treatment subgroup (patients diagnosed from 2014 onwards), stratified by disease stage and initial treatment. OS and TTNTD were described using the Kaplan-Meier estimator. The overall population (2008-2019) included 17,433 patients; the treatment subgroup included 5147 patients. Median OS (interquartile range) overall ranged from 83.3 (31.6-165.3) months (stage I patients) to 10.4 (4.3-24.2) months (stage IIIB patients). Among the treatment subgroup, median OS and TTNTD were longest among patients receiving surgery versus other anticancer treatments. These findings provide a baseline upon which to evaluate the epidemiology of non-metastatic NSCLC as newer treatments are introduced.

Enhanced recovery after surgery program focusing on chest tube management improves surgical recovery after video-assisted thoracoscopic surgery

OBJECTIVE

Chest drainage is a standard procedure in thoracoscopic surgery for lung cancer. However, chest tube placement may deteriorate the ventilation capacity and increase difficulty of postoperative management of patients. The study investigated on the effects of enhanced recovery after surgery (ERAS) program focusing on chest tube management on surgical recovery of lung cancer patients.

METHODS

The study population consisted of 60 patients undergoing video-assisted thoracoscopic surgery (VATS) after implementation of ERAS program and another group of 60 patients undergoing VATS before implementation of ERAS program.

RESULTS

The mean time of first food intake was 12.9 h required for the ERAS group, which was significantly shorter than 18.4 h required for the control group (p < 0.0001). The mean time of out-of-bed activity was 14.2 h taken for the ERAS group, which was notably shorter than 22.8 h taken for the control group (p < 0.0001). The duration of chest tube placement was 68.6 h in the ERAS group, which was remarkably shorter than 92.8 h in the control group (p < 0.0001). The rate overall postoperative complications were notably lower in the ERAS group than in the control group (p = 0.018). The visual analogue score (VAS) scores on the second postoperative day exhibited significant differences between the ERAS group and the control group (p = 0.017). The patients in the ERAS group had a shorter hospitalization stay than those in the control group (p < 0.0001).

CONCLUSION

The study suggests the ERAS program focusing on chest tube management could improve surgical recovery, remove patient chest tube earlier, and relieve patient pain after VATS.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary

Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.

Abnormal expression of Krüppel-like transcription factors and their potential values in lung cancer

Lung cancer still is one of the most common malignancy tumors in the world. However, the mechanisms of its occurrence and development have not been fully elucidated. Zinc finger protein family (ZNFs) is the largest transcription factor family in human genome. Recently, the more and more basic and clinical evidences have confirmed that ZNFs/Krüppel-like factors (KLFs) refer to a group of conserved zinc finger-containing transcription factors that are involved in lung cancer progression, with the functions of promotion, inhibition, dual roles and unknown classifications. Based on the recent literature, some of the oncogenic KLFs are promising molecular biomarkers for diagnosis, prognosis or therapeutic targets of lung cancer. Interestingly, a novel computational approach has been proposed by using machine learning on features calculated from primary sequences, the XGBoost-based model with accuracy of 96.4 % is efficient in identifying KLF proteins. This paper reviews the recent some progresses of the oncogenic KLFs with their potential values for diagnosis, prognosis and molecular target in lung cancer.

Heterogeneities and impact profiles of early chronic obstructive pulmonary disease status: findings from the China Pulmonary Health Study

Background

The prevalence, epidemiological and clinical heterogeneities, and impact profiles of individuals with preserved ratio impaired spirometry (PRISm), pre-COPD, young COPD, and mild COPD in general Chinese population were not known yet.

Methods

Data were obtained from the China Pulmonary Health study (2012-2015), a nationally representative cross-sectional survey that recruited 50,991 adults aged 20 years or older. Definitions of the four early disease status were consistent with the latest publications and the Global Initiative for Chronic Obstructive Lung Disease criteria.

Findings

The age-standardised prevalences of PRISm, pre-COPD, young COPD, and mild COPD were 5.5% (95% confidence interval, 4.3-6.9), 7.2% (5.9-8.8), 1.1% (0.7-1.8), and 3.1% (2.5-3.8), respectively. In summary, mild COPD was under more direct or established impact factor exposures, such as older age, male gender, lower education level, lower family income, biomass use, air pollution, and more accumulative cigarette exposures; young COPD and pre-COPD experienced more personal and parents' events in earlier lives, such as history of bronchitis or pneumonia in childhood, frequent chronic cough in childhood, parental history of respiratory diseases, passive smoke exposure in childhood, and mother exposed to passive smoke while pregnant; pre-COPD coexisted with heavier symptoms and comorbidities burdens; young COPD exhibited worse airway obstruction; and most of the four early disease status harbored small airway dysfunction. Overall, older age, male gender, lower education level, living in the urban area, occupational exposure, frequent chronic cough in childhood, more accumulated cigarette exposure, comorbid with cardiovascular disease and gastroesophageal reflux disease were all associated with increased presence of the four early COPD status; different impact profiles were additionally observed with distinct entities. Over the four categories, less than 10% had ever taken pulmonary function test; less than 1% reported a previously diagnosed COPD; and no more than 13% had received pharmaceutical treatment.

Interpretation

Significant heterogeneities in prevalence, epidemiological and clinical features, and impact profiles were noted under varied defining criteria of early COPD; a unified and validated definition for an early disease stage is warranted. Closer attention, better management, and further research need to be administrated to these population.

Funding

Chinese Academy of Medical Sciences Institute of Respiratory Medicine Grant for Young Scholars (No. 2023-ZF-9); China International Medical Foundation (No. Z-2017-24-2301); Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (No. 2021-I2M-1-049); National High Level Hospital Clinical Research Funding (No. 2022-NHLHCRF-LX-01); Major Program of National Natural Science Foundation of China (No. 82090011).

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

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

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

A review of obstructive sleep apnea and lung cancer: epidemiology, pathogenesis, and therapeutic options

Despite undeniable advances in modern medicine, lung cancer still has high morbidity and mortality rates. Lung cancer is preventable and treatable, and it is important to identify new risk factors for lung cancer, especially those that can be treated or reversed. Obstructive sleep apnea (OSA) is a very common sleep-breathing disorder that is grossly underestimated in clinical practice. It can cause, exacerbate, and worsen adverse outcomes, including death and various diseases, but its relationship with lung cancer is unclear. A possible causal relationship between OSA and the onset and progression of lung cancer has been established biologically. The pathophysiological processes associated with OSA, such as sleep fragmentation, intermittent hypoxia, and increased sympathetic nervous excitation, may affect normal neuroendocrine regulation, impair immune function (especially innate and cellular immunity), and ultimately contribute to the occurrence of lung cancer, accelerate progression, and induce treatment resistance. OSA may be a contributor to but a preventable cause of the progression of lung cancer. However, whether this effect exists independently of other risk factors is unclear. Therefore, by reviewing the literature on the epidemiology, pathogenesis, and treatment of lung cancer and OSA, we hope to understand the relationships between the two and promote the interdisciplinary exchange of ideas between basic medicine, clinical medicine, respiratory medicine, sleep medicine, and oncology.

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.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Healthy Lifestyle and Cancer Risk: Modifiable Risk Factors to Prevent Cancer

Cancer has become a serious problem worldwide, as it represents the main cause of death, and its incidence has increased over the years. A potential strategy to counter the growing spread of various forms of cancer is the adoption of prevention strategies, in particular, the use of healthy lifestyles, such as maintaining a healthy weight, following a healthy diet; being physically active; avoiding smoking, alcohol consumption, and sun exposure; and vitamin D supplementation. These modifiable risk factors are associated with this disease, contributing to its development, progression, and severity. This review evaluates the relationship between potentially modifiable risk factors and overall cancer development, specifically breast, colorectal, and prostate cancer, and highlights updated recommendations on cancer prevention. The results of numerous clinical and epidemiological studies clearly show the influence of lifestyles on the development and prevention of cancer. An incorrect diet, composed mainly of saturated fats and processed products, resulting in increased body weight, combined with physical inactivity, alcohol consumption, and smoking, has induced an increase in the incidence of all three types of cancer under study. Given the importance of adopting correct and healthy lifestyles to prevent cancer, global institutions should develop strategies and environments that encourage individuals to adopt healthy and regular behaviors.

The insufficiency of CT examination in early detection of central lung squamous cell carcinoma and squamous epithelial precancerous lesions

BACKGROUND

CT examination for lung cancer has been carried out for more than 20 years and great achievements have been made in the early detection of lung cancer. However, in the clinical work, a large number of advanced central lung squamous cell carcinoma are still detected through bronchoscopy. Meanwhile, a part of CT-occult central lung squamous cell carcinoma and squamous epithelial precancerous lesions are also accidentally detected through bronchoscopy.

METHODS

This study retrospectively collects the medical records of patients in the bronchoscopy room of the Endoscopy Department of Zhejiang Cancer Hospital from January 2014 to December 2018. The inclusion criteria for patients includes: 1.Patient medical records completed, 2.Without history of lung cancer before the diagnosis and first pathological diagnosis of primary lung cancer, 3.Have the lung CT data of the same period, 4.Have the bronchoscopy records and related pathological diagnosis, 5.The patients undergoing radical surgical treatment must have a complete postoperative pathological diagnosis. Finally, a total of 10,851 patients with primary lung cancer are included in the study, including 7175 males and 3676 females, aged 22-98 years. Firstly, 130 patients with CT-occult lesions are extracted and their clinical features are analyzed. Then, 604 cases of single central squamous cell carcinoma and 3569 cases of peripheral adenocarcinoma are extracted and compares in postoperative tumor diameter and lymph node metastasis.

RESULTS

115 cases of CT-occult central lung squamous cell carcinoma and 15 cases of squamous epithelial precancerous lesions are found. In the total lung cancer, the proportion of CT-occult lesions is 130/10,851 (1.20%). Meanwhile, all these patients are middle-aged and elderly men with a history of heavy smoking. There are statistically significant differences in postoperative median tumor diameter (3.65 cm vs.1.70 cm, P < 0.0001) and lymph node metastasis rate (50.99% vs.13.06%, P < 0.0001) between 604 patients with operable single central lung squamous cell carcinoma and 3569 patients with operable peripheral lung adenocarcinoma. Of the 604 patients with squamous cell carcinoma, 96.52% (583/604) are male with a history of heavy smoking and aged 40-82 years with a median age of 64 years.

CONCLUSIONS

This study indicates that the current lung CT examination of lung cancer is indeed insufficiency for the early diagnosis of central squamous cell carcinoma and squamous epithelial precancerous lesions. Further bronchoscopy in middle-aged and elderly men with a history of heavy smoking can make up for the lack of routine lung CT examination.

Robotic Versus Thoracoscopic Sub-lobar Resection for Octogenarians with Clinical Stage IA Non-small Cell Lung Cancer: A Propensity Score-Matched Real-World Study

BACKGROUND

Minimally invasive sub-lobectomy is sufficient in treating small early-stage non-small cell lung cancer (NSCLC). However, comparison of the feasibility and oncologic efficacy between robot-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in performing sub-lobectomy for early-stage NSCLC patients age 80 years or older is scarce.

METHODS

Octogenarians with clinical stage IA NSCLC (tumor size, ≤ 2 cm) undergoing minimally invasive wedge resection or segmentectomy at Shanghai Chest Hospital from 2011 to 2020 were retrospectively reviewed from a prospectively maintained database. Propensity score-matching (PSM) with a RATS versus VATS ratio of 1:4 was performed. Perioperative and long-term outcomes were analyzed.

RESULTS

The study identified 594 patients (48 RATS and 546 VATS patients), and PSM resulted in 45 cases in the RATS group and 180 cases in the VATS group. The RATS patients experienced less intraoperative bleeding (60 mL [interquartile range (IQR), 50-100 mL] vs. 80 mL [IQR, 50-100 mL]; P = 0.027) and a shorter postoperative hospital stay (4 days [IQR, 3-5 days] vs. 5 days [IQR, 4-6 days]; P = 0.041) than the VATS patients. The two surgical approaches were comparable concerning other perioperative outcomes and postoperative complications (20.00% vs. 26.11%; P = 0.396). Additionally, during a median follow-up period of 66 months, RATS and VATS achieved comparable 5-year overall survival (90.48% vs. 87.93%; P = 0.891), recurrence-free survival (83.37% vs. 83.18%; P = 0.782), and cumulative incidence of death. Further subgroup comparison also demonstrated comparable long-term outcomes between the two approaches. Finally, multivariate Cox analysis indicated that the surgical approach was not independently correlated with long-term outcomes.

CONCLUSIONS

The RATS approach shortened the postoperative hospital stay, reduced intraoperative bleeding by a statistically notable but clinically insignificant amount, and achieved long-term outcomes comparable with VATS in performing sub-lobectomy for octogenarians with early-stage small NSCLC.

RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.