Early Lung Cancer Research Articles

1221P Stereotactic ablative radiotherapy in combination with nivolumab for early stage operable non-small cell lung cancer: A phase II study

1221P Stereotactic ablative radiotherapy in combination with nivolumab for early stage operable non-small cell lung cancer: A phase II study

1238TiP Combining immunotherapy with Trop2 ADc in early stage non-small cell lung cancer (CITADEL)

1238TiP Combining immunotherapy with Trop2 ADc in early stage non-small cell lung cancer (CITADEL)

1226P Predictive value of circulating tumor DNA (ctDNA) before and shortly after curative treatment in early stage non-small cell lung cancer (NSCLC), and exploration of (pre-)analytical factors

1226P Predictive value of circulating tumor DNA (ctDNA) before and shortly after curative treatment in early stage non-small cell lung cancer (NSCLC), and exploration of (pre-)analytical factors

1214P Imaging AI prognosis of early stage lung cancer using CT radiomics

1214P Imaging AI prognosis of early stage lung cancer using CT radiomics

1236P ctDNA-Lung-Detect: Profiling of non-shedding ctDNA early stage resected non-small cell lung cancers

1236P ctDNA-Lung-Detect: Profiling of non-shedding ctDNA early stage resected non-small cell lung cancers

Preoperative localization of pulmonary nodules by electromagnetic navigation bronchoscopy combined with methylene blue injection.

Electromagnetic navigation bronchoscopy (ENB) can help to accurately locate pulmonary nodules using a minimally invasive approach. This study sought to evaluate the clinical efficacy and safety of dye marking localization under the guidance of ENB followed by surgery. A retrospective analysis was performed of 61 patients who underwent ENB localization using methylene blue dye marking before surgery at Shanghai General Hospital from October 2021 to February 2022. The clinical efficacy and safety of ENB localization and the related factors affecting the navigation time of ENB location were analyzed. ENB was performed on 170 pulmonary nodules in 61 patients with a median age of 60 [interquartile range (IQR), 18] years. The majority of patients (70.69%) had more than two pulmonary nodules. The median maximum nodule diameter was 10 (IQR, 8) mm, and 48.21% of the nodules were mixed ground-glass nodules. Median time for ENB navigation was 10.5 (IQR, 6) min. The navigation success rate was 92.96%, and the ENB location success rate was 95.89%. The rate of complications related to ENB localization was 1.64% (there was only one case of pulmonary hemorrhage). The multivariate analysis showed that the factors related to the navigation time included the node location (P=0.001) and location mode (P=0.04). ENB-guided methylene blue injection is an effective and safe tool for localizing and marking pulmonary nodules, and can be used to assist the diagnosis and treatment of early lung cancer. The node location and location mode had significant effects on navigation time.

Impact of Ventilator Settings on Pulmonary Nodule Localization Accuracy in a Hybrid Operating Room: A Single-Center Study.

Background: Pulmonary nodule localization in a hybrid operating room (OR) followed by thoracoscopic operation presents a viable alternative for early lung cancer treatment, potentially supplanting conventional two-stage preoperative computed tomography-guided localization. This hybrid OR technique enables lesion localization under positive ventilation, contrasting with the traditional method requiring concurrent respiratory motion. This study aimed to evaluate our experience with different ventilator settings and the accuracy of pulmonary nodule localization. Methods: We retrospectively analyzed 176 patients with multiple pulmonary nodules who had localization procedures in our hybrid operating room. Ninety-five patients were assigned to the traditional ventilator setting group (tidal volume 8-10 mL/kg) and 81 to the lung-protective strategy group (tidal volume < 8 mL/kg). Localization accuracy was assessed via hybrid computed tomography imaging, ensuring that the needle-to-lesion distance was ≤5 mm. Between-group differences were assessed using the chi-squared test, Fisher's exact test, and the Mann-Whitney U test, as appropriate. Results: Pathological findings revealed primary lung malignancy in 150 patients, inclusive of invasive adenocarcinoma, adenocarcinoma in situ, and minimally invasive adenocarcinoma. Multivariate regression analysis identified tidal volume, nodule count, and localization depth as significant predictors of localization accuracy. Conclusions: This study demonstrated that ventilator settings with a tidal volume of 8-10 mL/kg significantly enhanced localization accuracy and slightly improved patient oxygenation. However, additional randomized controlled trials are warranted to validate these findings and establish definitive guidelines for future interventions.

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

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

The links between symptom burden, illness perception, psychological resilience, social support, coping modes, and cancer-related worry in Chinese early-stage lung cancer patients after surgery: a cross-sectional study

ObjectivesThis study aims to investigate the links between the clinical, demographic, and psychosocial factors and cancer-related worry in patients with early-stage lung cancer after surgery.MethodsThe study utilized a descriptive cross-sectional design. Questionnaires, including assessments of cancer-related worry, symptom burden, illness perception, psychological resilience, coping modes, social support and participant characteristics, were distributed to 302 individuals in early-stage lung cancer patients after surgery. The data collection period spanned from January and October 2023. Analytical procedures encompassed descriptive statistics, independent Wilcoxon Rank Sum test, Kruskal-Wallis- H- test, Spearman correlation analysis, and hierarchical multiple regression.ResultsAfter surgery, 89.07% had cancer-related worries, with a median (interquartile range, IQR) CRW score of 380.00 (130.00, 720.00). The most frequently cited concern was the cancer itself (80.46%), while sexual issues were the least worrisome (44.37%). Regression analyses controlling for demographic variables showed that higher levels of cancer-related worry (CRW) were associated with increased symptom burden, illness perceptions, and acceptance-rejection coping modes, whereas they had lower levels of psychological resilience, social support and confrontation coping modes, and were more willing to obtain information about the disease from the Internet or applications. Among these factors, the greatest explanatory power in the regression was observed for symptom burden, illness perceptions, social support, and sources of illness information (from the Internet or applications), which collectively explained 52.00% of the variance.ConclusionsHealthcare providers should be aware that worry is a common issue for early stage lung cancer survivors with a favorable prognosis. During post-operative recovery, physicians should identify patient concerns and address unmet needs to improve patients’ emotional state and quality of life through psychological support and disease education.

Comprehensive Analysis of Immune Responses to Neoadjuvant Immunotherapy in Resectable Non-small Cell Lung Cancer.

Neoadjuvant immunotherapy using immune checkpoint inhibitors (ICIs) has revolutionized the treatment of early stage non-small cell lung cancer (NSCLC). However, little is known about which patients are likely to benefit most from neoadjuvant immunotherapy. In this study, we performed a multiplatform analysis on samples from resectable NSCLC treated with neoadjuvant immunotherapy to explore molecular characteristics related to immune responses. A total of 17 patients with resectable stage IB-IIIA NSCLC treated with neoadjuvant immunotherapy were included. A multiplex cytokine assay, bulk TCR sequencing in peripheral blood, and multiplexed immunohistochemistry were performed. Low levels of stromal cell-derived factor (SDF)-1alpha at baseline were associated with unfavorable disease-free survival (DFS). Patients with major pathologic response (MPR) showed a decrease in HGF after one cycle of neoadjuvant immunotherapy. An increase in IDO and IP-10 was observed in patients who developed immune-related adverse events (irAEs) after neoadjuvant immunotherapy. There were no correlations between irAEs and MPR or DFS. The MPR group presented a significant decrease in white blood cells and neutrophil count after neoadjuvant immunotherapy. The high peripheral baseline TCR convergence was correlated with MPR and favorable DFS in lung squamous cell carcinoma (LUSC) receiving neoadjuvant immunotherapy. Neoadjuvant immunotherapy led to a significant increase in CD4+, CD8+, and CD8+CD39+ T-cell infiltration in tumor areas. This study suggests the potential roles of cytokines and TCR convergence for predicting ICIs response in resectable NSCLC and LUSC. CD8+CD39+T cells and CD4+ T cells could be involved in the action of neoadjuvant immunotherapy.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph.

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine.

Innovations in Early Lung Cancer Detection: Tracing the Evolution and Advancements in Screening.

Lung cancer mortality rates, particularly non-small cell lung cancer (NSCLC), continue to present a significant global health challenge, and the adoption of lung cancer screening remains limited, often influenced by inequities in access to healthcare. Despite clinical evidence demonstrating the efficacy of annual screening with low-dose computed tomography (LDCT) and recommendations from medical organizations including the U.S. Preventive Services Task Force (USPSTF), the national lung cancer screening uptake remains around 5% among eligible individuals. Advancements in the clinical management of NSCLC have recently become more personalized with the implementation of blood-based biomarker testing. Extensive research into tumor-derived cell-free DNA (cfDNA) through fragmentation offers a novel method for improving early lung cancer detection. This review assesses the screening landscape, explores obstacles to lung cancer screening, and discusses how a plasma whole genome fragmentome test (pWGFrag-Lung) can improve lung cancer screening participation and adherence.

Distinctive field effects of smoking and lung cancer case-control status on bronchial basal cell growth and signaling

RationalBasal cells (BCs) are bronchial progenitor/stem cells that can regenerate injured airway that, in smokers, may undergo malignant transformation. As a model for early stages of lung carcinogenesis, we set out to characterize cytologically normal BC outgrowths from never-smokers and ever-smokers without cancers (controls), as well as from the normal epithelial “field” of ever-smokers with anatomically remote cancers, including lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) (cases).MethodsPrimary BCs were cultured and expanded from endobronchial brushings taken remote from the site of clinical or visible lesions/tumors. Donor subgroups were tested for growth, morphology, and underlying molecular features by qRT-PCR, RNAseq, flow cytometry, immunofluorescence, and immunoblot.Results(a) the BC population includes epithelial cell adhesion molecule (EpCAM) positive and negative cell subsets; (b) smoking reduced overall BC proliferation corresponding with a 2.6-fold reduction in the EpCAMpos/ITGA6 pos/CD24pos stem cell fraction; (c) LUSC donor cells demonstrated up to 2.8-fold increase in dysmorphic BCs; and (d) cells procured from LUAD patients displayed increased proliferation and S-phase cell cycle fractions. These differences corresponded with: (i) disparate NOTCH1/NOTCH2 transcript expression and altered expression of potential downstream (ii) E-cadherin (CDH1), tumor protein-63 (TP63), secretoglobin family 1a member 1 (SCGB1A1), and Hairy/enhancer-of-split related with YRPW motif 1 (HEY1); and (iii) reduced EPCAM and increased NK2 homeobox-1 (NKX2-1) mRNA expression in LUAD donor BCs.ConclusionsThese and other findings demonstrate impacts of donor age, smoking, and lung cancer case-control status on BC phenotypic and molecular traits and may suggest Notch signaling pathway deregulation during early human lung cancer pathogenesis.

Retrospective evaluation of plasma protein tumour markers for early lung cancer detection

BackgroundBlood-based biomarkers might help lung cancer diagnosis. A panel of serum tumour markers (TM) has been validated for hospital referrals due to clinical suspicion of lung cancer. We have compared plasma from a cohort enriched for early-stage lung cancer, including controls from a healthy population cohort.MethodsTM assays for CEA, CYFRA 21-1, CA15.3, ProGRP and SCC were run on a Roche Elecsys 2010 Immunoassay Analyser for a retrospective, nested case-control cohort from the Liverpool Lung Project. The primary endpoints were the sensitivity and specificity of a pre-defined TM panel using published thresholds.ResultsExcept for ProGRP, TM levels were significantly higher in cases and ROC AUC values demonstrated significant discriminant power. Accuracy and levels were higher for late-stage cancers, except for ProGRP which was highest in stage 1 cancers. Although providing similar sensitivity (82.4% vs 88.5%), TMs performed worse for specificity (39.3% vs 82%) and overall (Youden’s Index 0.22 vs 0.77) and this was not improved by threshold optimisation or binary logistic regression.ConclusionsAlthough the TMs were associated with lung cancer status and discriminatory with a high sensitivity when combined, performance was compromised in early-stage disease, which casts some doubt on utility in the screening setting.

Chinese expert consensus on diagnosis and treatment of pulmonary nodules(2024)

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in China. The 5-year survival rate released for China in 2018 was 19.7%. The 5-year survival rate for stage Ⅰ patients is 77%-92%. Early diagnosis and treatment of lung cancer are key to improving the 5-year overall survival rate and prognosis of lung cancer patients. Therefore, experts from the Academic Group of Lung Cancer in Chinese Thoracic Society and Chinese Alliance Against Lung Cancer Expert Group formulated the Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules in 2015 to standardize the diagnosis and treatment of lung nodules. In 2018, this consensus was updated to formulate the Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2018 edition), and widely applied in multiple branch centers of Chinese Alliance Against Lung Cancer, proposing the Intelligent Treatment of Million Early Lung Cancer Project. Based on applied experience of the expert consensus in recent years, with reference to the latest evidence has been updated, Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2024 edition) was formulated. The updated content of this consensus mainly includes the following aspects: (1) Define the screening age of high-risk lung cancer populations in China based on the national conditions; (2) Propose definition of "difficult-to-determine pulmonary nodules" to avoid delaying the diagnosis and treatment; (3) Evaluate pulmonary nodules assisted by artificial intelligence (AI) imaging-assisted diagnostic system and propose human-machine MDT to avoid the limitations of AI; (4) Evaluate pulmonary nodules by routine and individualized evaluations for different populations, and make recommendations based on evidence-based management guidelines for different types and sizes of pulmonary nodules. In the updated consensus, 18 consensus points were recommended as a reference for clinical management of pulmonary nodules to improve the 5-year overall survival rate and the prognosis of lung cancer in China.

Integrating cardiovascular risk assessment into mobile low-dose CT lung screenings in rural Appalachia: A comprehensive analysis of the relationship between lung cancer risk, coronary artery calcium burden, and cardiovascular risk reduction strategies

ObjectiveMobile low-dose computed tomography (LDCT) lung screenings are part of an outreach program in rural Appalachia to detect early lung cancer. Coronary artery calcium (CAC) scoring on LDCT can identify calcium deposits in coronary arteries and can prompt consideration of risk modification for prevention of cardiovascular disease (CVD) events. It is not known if Lung CT Screening Reporting & Data System (Lung-RADS) scoring correlates with CAC scores. There is no clear guidance for patients undergoing LDCT screenings to receive follow-up regarding CAC or prevention of associated CVD risk. MethodsThis was a retrospective review of mobile LDCT LCS in adults with no known history of CVD. CT images were obtained at 100 kVp with a slice thickness of 3 mm. Agatston CAC scoring was performed retroactively. Lung-RADS scores were categorized as: Negative (1), Benign (2), Probably Benign (3), and Suspicious (4). CAC scoring was grouped as 0, 1–100, 101–399, and ≥400. Descriptive statistics and chi-square analyses were utilized. ResultsA total of 526 LDCT screenings were included. Over 54 % of patients had coronary calcification on LDCT LCS. 161 patients (30.6 %) had a CAC score of ≥100 and 75 patients (14.3 %) had a CAC score ≥400. Of patients with a CAC score ≥100, 7.5 % received referrals for follow-up after the LDCT screen and 9.3 % had additional cardiac testing. Of those with a CAC score ≥100 not already on a statin (45.3 %) and not already on aspirin (63.3 %), few were started within 3 months of LDCT for prevention (8.2 % and 5.9 % respectively). Among patients with a Lung-RADS score of 4, 17 % had a CAC score >400, whereas only 12 % with a Lung-RADS score of 1 fell into the same CAC category. Higher Lung-RADS scores correlated with fewer patients with CAC of 0. A significant correlation was observed between higher Lung-RADS scores and elevated CAC scores (p = 0.02). ConclusionIn patients with no CVD history, coronary artery calcification was frequently identified on mobile LDCT lung screenings in rural communities. Patients with higher probabilities of malignant lung nodules may also be at increased risk for significant coronary artery disease. Calcium scoring from LDCT screenings allowed for simultaneous assessment of lung cancer and CVD risk. Unfortunately, few referrals or CVD prevention medications were initiated. Awareness of CAC score utility, follow-up for identified coronary calcifications, and consideration of primary prevention medications when indicated, would be beneficial in patients undergoing LDCT lung screenings, especially in rural areas with limited healthcare access.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

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

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