Early Detection Of Lung Cancer Research Articles

Machine Learning-driven Lung Cancer Detection

Machine Learning-driven Lung Cancer Detection

Pulmonary Nodule Segmentation Network Based on Res Select Kernel Contextual U-Net

Abstract U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in computer tomography (CT) images, complex features, and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of Res select Kernel Contextual U-Net (RkcU-Net) network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multiscale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Second, the contextual supplementary (CS) block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve an intersection ratio (IoU) of 80.59% and a dice similarity coefficient (DSC) score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.

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

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

Early Detection of Lung Cancer with Low-Dose CT Scan Using Artificial Intelligence: A Comprehensive Survey

Early Detection of Lung Cancer with Low-Dose CT Scan Using Artificial Intelligence: A Comprehensive Survey

A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer

Introduction and objectivesLung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission tomography/computed tomography (PET/CT) has become a fundamental tool for the early and accurate detection of cancer. The objective of this study was to propose an image analysis architecture (PET/CT) ordered in phases through the application of ensemble or combined machine learning methods for the early detection of lung cancer by analyzing PET/CT images. Material and methodsA retrospective observational study was conducted utilizing a public dataset entitled “A large-scale CT and PET/CT dataset for lung cancer diagnosis.” Various imaging modalities, including CT, PET, and fused PET/CT images, were employed. The architecture or framework of this study comprised the following phases: 1. Image loading or collection, 2. Image selection, 3. Image transformation, and 4. Balancing the frequency distribution of image classes. Predictive models for lung cancer detection using PET/CT images included: a) the Stacking model, which used Random Forest and Support Vector Machine (SVM) as base models and complemented them with a logistic regression model, and b) the Boosting model, which employed the Adaptive Boosting (AdaBoost) model for comparison with the Stacking model. Quality metrics used for evaluation included accuracy, precision, recall, and F1-score. ResultsThis study showed a general performance of 94% with the Stacking method and a general performance of 77% with the Boosting method. ConclusionsThe Stacking method proved to be a model with high performance and quality for lung cancer detection when analyzing PET/CT images.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Machine Learning based Early-detection of Lung Cancer Using CT Scan for the early Identification and Classification

The current research is aimed at studying the application of machine learning algorithms for the early prediction and detection of lung cancer taking the images of CT scans and MR scans. In this respect, the dataset from kaggle includes 3200 images from diverse sources, which are pre-processed and applied for the process of feature extraction, using traditional methods and deep learning systems, which include Convolutional Neural Networks, VGG-16, VGG-19, and RNN, while after the training the systems are evaluated in accordance with precision, recall, f1 score, and accuracy. As a result, it is evidenced that the best model is VGG-19 with the highest accuracy of 97.86%, while follows VGG-19 the VGG-16 the CNN model, and the RNN, implying effective implications for clinical practice. Certainly, the results of the research help to create a non-invasive, effective, and fast tool, using by clinicians in their practice. The use of machine learning algorithms for the process of early prediction and detection would be helpful for the timely treatment of patients and personalized treatment plans. In such a way, based on the current research, it could be summarized that the best models are constantly developed and gradually implemented in practice. However, more significant collaboration between the researchers, clinicians, and the industry is needed to have the full implementation of applied methods in practice, having fast results and timely actions taken by clinicians.

Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model.

Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets. This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy. The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach. The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.

An optimal model for detection of lung cancer using convolutional neural network

In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data.

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lung cancer is a critical global health concern, necessitating precise early diagnosis and intervention for better patient outcomes. Computed Tomography (CT) scans are pivotal in lung cancer detection, and leveraging advanced technology is crucial. This study introduces "DLCTLungDetectNet," a Convolutional Neural Network (CNN) based deep learning framework, with a focus on early lung cancer detection using CT scan images. The core innovation lies in the integration of the robust "FusionNet," a hybrid model amalgamating feature from ResNet50 and InceptionV3. We conduct a comprehensive comparative analysis, showcasing the superior performance of DLCTLungDetectNet over established architectures such as VGG16, VGG19, and Inception v3. Rigorous evaluation based on standard metrics substantiates DLCTLungDetectNet's high accuracy, precision, Area Under Curve (AUC), and F1 score. This research not only highlights the potential of deep learning in enhancing lung cancer diagnosis but also establishes a benchmark, showcasing the efficacy of the FusionNet hybrid model for achieving superior accuracy in automated lung tumor detection.

The Contribution of Double-strand Break Repair Radiation Sensitive Protein 51 Genotypes to Lung Cancer in Taiwan.

Numerous studies have reported the over-expression of the radiation-sensitive protein 51 (RAD51) in various types of cancer. However, the role of RAD51 genotypes in lung cancer remains largely unknown. This study aimed to assess the impact of the common variant RAD51 rs1801320 (G-135C) genotypes on the risk of lung cancer in Taiwan. The contribution of RAD51 rs1801320 genotypes to lung cancer risk was investigated in a cohort comprising 358 lung cancer patients and 716 age- and sex-matched healthy controls, utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. The analysis revealed that among the control subjects, the percentages of GG, CG, and CC genotypes of RAD51 rs1801320 were 73.2%, 24.3%, and 2.5%, respectively. Among the lung cancer patients, these percentages were 71.0%, 25.1%, and 3.9%, respectively (p for trend=0.4075). Allelic frequency distributions showed no significant association between the C allele of RAD51 rs1801320 and lung cancer risk determination (p=0.2987). Specifically, the RAD51 rs1801320 CC genotypes were associated with an elevated risk of lung cancer among males [adjusted odds ratio (aOR)=2.28, 95% confidence interval (95%CI)=1.03-4.87] and smokers (aOR=2.93, 95%CI=1.23-5.87), but not among females and non-smokers. The RAD51 rs1801320 CC genotype was identified as a risk factor for elevated lung cancer risk in males and smokers. This genotype may serve as a molecular biomarker at the DNA level for early detection and prediction of lung cancer in Taiwan.

Smoking, Lung Cancer Stage, and Prognostic Factors-Findings from the National Lung Screening Trial.

Low-dose computed tomography (LDCT) increases the early detection of lung cancer. Identifying modifiable behaviors that may affect tumor progression in LDCT-detected patients increases the likelihood of long-term survival and a good quality of life. We examined cigarette smoking behaviors on lung cancer stage, progression, and survival in 299 ever-smoking patients with low-dose CT-detected tumors from the National Lung Screening Trial. Univariate and multivariate Cox models were used to estimate the hazard ratio (HR) for smoking variables on survival time. Current vs. former smokers and early morning smokers (≤5 min after waking, i.e., time to first cigarette (TTFC) ≤ 5 min) had more advanced-stage lung cancer. The adjusted HR for current vs. former smokers was 1.3 (95% confidence interval [CI] 0.911-1.98, p = 0.136) for overall survival (OS) and 1.3 (0.893-1.87, p = 0.1736) for progression-free survival (PFS). The univariate hazard ratios for TTFC ≤ 5 min vs. >5 min were 1.56 (1.1-2.2, p = 0.013) for OS and 1.53 (1.1-2.12, p = 0.01) for PFS. Among current smokers, the corresponding HRs for early TTFC were 1.78 (1.16-2.74, p = 0.0088) and 1.95 (1.29-2.95, p = 0.0016) for OS and PFS, respectively. In causal mediation analysis, the TTFC effect on survival time was mediated entirely through lung cancer stage. The current findings indicate smoking behaviors at diagnosis may affect lung cancer stage and prognosis.

Red blood cells function as reservoirs of tumor DNA.

Novel screening techniques for early detection of lung cancer are urgently needed. Profiling circulating tumor cell-free DNA (ctDNA) has emerged as a promising tool for biopsy-free tumor genotyping. However, both the scarcity and short half-life of ctDNA substantially limit the sensitivity and clinical utility of ctDNA detection methodologies. Our discovery that red blood cells (RBCs) sequester mitochondrial DNA opens a new avenue for detecting circulating nucleic acids, as RBCs represent an unrecognized reservoir of circulating nucleic acid. Here, we show that RBCs acquire tumor DNA following coculture with lung cancer cell lines harboring Kirsten rat sarcoma viral oncogene homolog (KRAS) and epidermal growth factor receptor (EGFR) mutations. RBC-bound tumor DNA is detectable in patients with early-stage non-small cell lung cancer (NSCLC) but not in healthy controls by qPCR. Our results collectively uncover a previously unrecognized yet easily accessible reservoir of tumor DNA, offering a promising foundation for future RBC-based tumor diagnostics.NEW & NOTEWORTHY We present a novel method for lung cancer detection by revealing RBCs as a reservoir for tumor DNA, overcoming the limitations of current circulating tumor ctDNA methodologies. By demonstrating that RBCs can capture tumor DNA, including critical mutations found in lung cancer, we provide a promising, biopsy-free avenue for early cancer diagnostics. This discovery opens up exciting possibilities for developing RBC-based diagnostic tools, significantly enhancing the sensitivity and clinical utility of noninvasive cancer detection.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

A Review on Lung Cancer with Emphasis on Current Treatment

Cancer is a broad term that refers to more than 277 types of cancer. It is a disease that results from a variety of causes, including mutations resulting from oncogene activation, tumor suppressor gene failure, or other factors. Mutations in these genes cause abnormal cell growth. Lung cancer is one of the leading causes of cancer in the world. Lung cancer, also known as lung cancer, is a lung disease caused by the uncontrolled growth of cells in tissues. Risk factors include smoking , exposure to radon gas, asbestos, secondhand smoke, and pollution. The two types of lung cancer are small cell lung cancer (SCLS) and non-small cell lung cancer (NSCLS). Common symptoms include cough, hemoptysis, weight loss, fatigue, fever or clubbing, hypercalcemia, myasthenic syndrome (muscle fatigue), and changes. Lung cancer is classified by histological type and stage using TNM (tumor, tumour, and metastasis). Treatment depends on the type of cancer and includes surgery, radiation and chemotherapy. Long-term intake of vitamin A, vitamin D, or vitamin E does not reduce the risk of lung cancer. Consuming more vegetables and fruits will reduce the risk. There is no clear link between diet and lung cancer. Most patients with advanced NSCLC who respond to surgery are treated with adjuvant chemotherapy. Histological diagnosis can be made by sputum cytology, thoracentesis, access to lymph nodes, bronchoscopy, transthoracic needle aspiration, video-assisted thoracoscopy or thoracotomy. Initial evaluation of metastatic disease is based on history and physical examination, chest examination, computed tomography, PET scan, and clear tissue of mediastinal involvement. Despite the interest in screening among scientists and doctors, there is no major organization recommending screening for early detection of lung cancer. Smoking is still an important part of primary prevention.

Abstract 3661: Non-coding RNAs as biomarkers for early detection of lung cancer

Abstract Introduction: Screening programs proven to reduce lung cancer deaths in high-income countries. Biomarkers detection in body fluids may guide more precise selection of high-risk subjects but this approach has not been currently employed for lung cancer screening. Aims: To identify ncRNA-based signatures for early detection of lung cancer in liquid biopsies. Methods: This is a case-control prospective study, conducted at the Barretos Cancer Hospital Screening Program. We analyzed miRNAs in plasma and sputum samples from high-risk subjects (n=51), and patients (n=44) diagnosed with non-small cell lung cancer (NSCLC), no metastatic, at Barretos Cancer Hospital (Brazil). We analyzed circRNAs in plasma samples from high-risk subjects (n=100), and patients (n=86) with NSCLC at Hospital Universitari Quiron Dexeus (Spain). Samples were subjected to RNA isolation followed expression analysis, for miRNA expression was used nCounter Human V3 microRNA painel, and for circRNA expression was use a nCounter customized painel (NanoString). Counts were normalized by housekeeping, and differentially expressed miRNAs were filtered out according to fold-change (≥|1.5|), p-value (≤0.05). Results: All cancer patients were diagnosed with adenocarcinoma (n=68), squamous cell carcinoma (n=41) and others/no information (n=36). No differences were observed related to age, sex, and tobacco exposure between high-risk and cancer groups (p&amp;gt;0.05). For Brazilian group, we observed six differentially expressed miRNAs in plasma able to distinguish cancer from non-cancer samples - three miRNAs upregulated, and three miRNAs downregulated -, and three of them presented with high accuracy (AUC = 0.70). For sputum, we observed four differentially expressed miRNAs able to distinguish cancer from non-cancer samples, and the 4-miRNA signature present with high accuracy (AUC = 0.73). None of miRNAs were shared between plasma and sputum. In both sample types we have miRNAs associated with zinc finger (ZNF99 and ZNF268), that predicted to be involved in regulation of transcription by RNA polymerase II. In Spanish cohort, we observed 4-circRNA signature with high accuracy (AUC = 0.745). These four circRNAs are associated with insulin resistance and proto-oncogenes. Conclusion: We identified ncRNA-based biomarkers in biofluids potentially to be employed in lung cancer screening programs to better guide selection of high-risk subjects for early detection of lung cancer. Citation Format: Giovanna Maria Stanfoca Casagrande, Alessandro Pascon Filho, Alexia Polo Siqueira, Rodrigo Sampaio Chiarantano, Fabiana de Lima Vazquez, Miguel Angel Molina, Carlos Pedraz-Valdunciel, Ana Gimenez-Capitán, Rui Manuel Reis, Leticia Ferro Leal. Non-coding RNAs as biomarkers for early detection of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3661.

Abstract 1060: Circulating early myeloid derived suppressor cells as biomarkers for early detection of lung cancer

Abstract Background. Lung cancer (LC) patients, despite innovative cancer treatments such as target and immune-therapies, still have the poorest five-year survival rate. In this context the possibility of an early detection can anticipate therapeutic intervention, avoiding aggressive therapies thus prolonging disease-free and overall survival. Therefore, the identification of new circulating early biomarkers still represents a relevant clinical need. Methods. Lung cancer (LC) patients (n=63) and heavy smokers (HS) cancer free-subjects (n=52) were evaluated for the enrichment of specific subtypes of myeloid cells. Circulating early myeloid derived suppressor cells (eMDSCs) and low-density neutrophils (LDNs) were assessed by flow cytometry on peripheral blood mononuclear cells (PMBCs). Early MDSCs were defined as CD11b+CD33+CD15-CD14- cells among Lineage negative (Lin-) HLA-DR- PBMCs, whereas LDNs were defined as CD66b+CD15+ cells within the gate of CD11b+ PBMCs. Mature and immature subtypes were discriminated according to the expression of CD10. Results. Performing a sex- and smoking -matched case-control analysis in 39 LC and 39 HS subjects we were able to compare the capacity of specific myeloid population to discriminate lung cancer patients from heavy smokers. The analysis showed significantly higher values of eMDSC (OR=1.12; 95% Confidence Interval (CI) 1.03-1.21), total LDN (CD66b+CD15+; OR=1.23; 95%CI 1.04-1.45) and of mature CD10+ LDN (OR=1.30; 95%CI 1.05-1.61). Moreover, within the overall cohort of 63 LC patients, we analyzed the distributions of the different myeloid populations according to stage (I-II vs III-IV). In this comparison, total LDN (CD66b+CD15+; Kruskal-Wallis (KW) Test p-value=0.014), and mature CD10+ LDN (KW test p-value=0.006) showed significant different distributions according to tumor stage, observing higher values in patients with advanced stage. On the contrary, no difference among stages was observed for the eMDSC population. Conclusions. These findings point to eMDSCs as an early marker for lung cancer detection whereas LDN could be as well associated with disease progression. Citation Format: Orazio Fortunato, Barbara Bassani, Mara Lecchi, Paolo Verderio, Ugo Pastorino, Gabriella Sozzi, Mario Paolo Colombo, Sabina Sangaletti. Circulating early myeloid derived suppressor cells as biomarkers for early detection of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1060.

Abstract 6104: EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules

Abstract Background: The goal of the Early Detection Research Network (EDRN) Lung Team Project #2 (LTP-2) is to establish a cohort (n= 300) of high-risk people with indeterminate pulmonary nodules (6mm-30mm) to clinically validate the diagnostic accuracy of existing molecular and imaging biomarkers discovered and analytically validated through the EDRN program. These biomarkers comprise signals measured in serum (glycan or cytokine), plasma (protein, miRNA, RNA, or methylated circulating tumor DNA), airway epithelial cells (RNA or DNA), nasal brush cells (RNA or DNA), and/or chest CT images. Methods: Eligible participants had an indeterminate nodule discovered incidentally or through low-dose CT screening within 14 months prior to enrollment and were aged 45-90 years, with 20 pack-years or more cigarette smoking history, free of lung cancer, and willing to provide blood, nasal brushing, and optionally bronchoscopic brush biopsy specimens. Baseline medical and demographic data were collected from each subject. Baseline and years 1 and 2 follow-up CT images were obtained. Each specimen at each respective site was labeled with a 2-D barcode provided by the DMCC and specimen information was entered into the Validation Study Information Management System (VSIMS) developed by the EDRN Data Management and Coordinating Center (DMCC). Aliquots of blinded specimens from selected cases and controls will be dispensed to each EDRN site for biomarker analysis. Analysis plan: Primary endpoint: The positive diagnostic likelihood ratio (DLR+) will be used to measure biomarker performance within values of DLR+ &amp;gt;= 2 or DLR- &amp;lt;= 0.5, a range considered clinically useful for any patients with indeterminate lung nodules. The sample size calculation targeted &amp;gt;80% power to test the null hypotheses that a) a positive biomarker does not alter risk across a range of specificities, and b) a negative biomarker does not alter risk across a range of sensitivities. The EDRN Data Management and Coordinating Center (DMCC) will unblind and complete analysis and report results. Exploratory endpoint: The diagnostic accuracy of combined biomarkers will be calculated using a logistic regression model with flexible functional forms. Results and Discussion: More than 300 subjects were enrolled into the LTP2 cohort including a sufficient number of cases and controls (cohort size n&amp;gt;300) to reach power according to calculated estimates. Chest CT images were collected and are in the process of transfer to a central database. Plasma specimens have been distributed to test sites. RNA and DNA extraction from NBC and AEC is in process and based on results thus far the specimen quality and quantity is more than sufficient from the number of subjects needed to reach power for the primary endpoint and to conduct the planned exploratory analyses. Citation Format: James C. Willey, Eric L. Grogan, Stephen A. Deppen, James G. Herman, Michael N. Kammer, Matthew B. Schabath, Lynn Sorbara, Harvey I. Pass, Jun-Chieh J. Tsay, Ziding Feng, Avrum Spira, Erin L. Crawford, Jackie Dahlgren, Steven M. Dubinett, Kimberly R. Rieger-Christ, Marc E. Lenburg. EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6104.

Abstract 7029: Trajectory of DNA methylation in blood up to 18 years prior to lung cancer diagnosis

Abstract Purpose: Lung cancer (LC) has the highest mortality rate in South Korea. As prediction and early detection of lung cancer is pivotal to enhance survival rates, we attempted to investigate the trajectory of DNA methylation (DNAm) several years prior to lung cancer diagnosis by using prediagnostic blood samples. Methods: From National Cancer Center Health Examinee Cohort, established in 2003, we selected study participants who became diagnosed with LC several years after health examination, thereby collecting prediagnostic samples from 18 years to 1 year prior to LC diagnosis. Normal controls (n=150) were matched with patients (n=150) by sex, age, and the time of sample collection. Epigenome-wide DNAm data were obtained by using Infinium Human MethylationEPIC v2.0 Beadchip. We assumed non-linear relationship between DNAm changes and the time-to-diagnosis and estimated an optimal threshold point of non-linear relationship with a piecewise linear regression analysis at each CpG sites (CpGs). We used the ratio of M-values of patients and matched controls to establish the non-linear model composed of two linear models with a threshold point where the slope changes. Then, we performed functional enrichment analysis by each year prior to LC diagnosis using ReactomePA. Results: We identified threshold models with a threshold point where the slope changes from zero (from 18 years prior to LC up to the threshold point) to positive or negative (from threshold point to the point of LC diagnosis), which we termed “flat-to-change model”. Out of 790,048 CpGs, we identified a total of 7214 CpGs showing the “flat-to-change model”, of which 944, 789, 480, 413, 687, 542, 516, 1140, 315, 284, 327, 432, 83, 95, 74, 32, and 23 CpGs were found prior to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17 years before LC diagnosis, respectively. Functional enrichment analysis showed that various pathways were associated at different time point berfore LC diagnosis. CpGs of which DNAm was changed two years before LC diagnosis were associated with telomerase extension by telomerase involving ANKRD28/TERT/TERF2/PPP6R3/WRAP53. Transcriptional regulation by TP53 was associated with DNAm changes before both 5 and 14 years of LC diagnosis via POLR2A. Conclusions: We identified the trajectories of DNA methylation up to 18 years prior to LC diagnosis, and found that a greater number of CpG sites showed DNAm changes adjacent to the time point of LC diagnosis. CpG sites associated with specific time point before LC diagnosis may elucidate the mechanisms of tumorigenesis and also have potential for LC prediction markers. Citation Format: Yoon-Jung Choi, Ki-Jae Hong, Yeon-Su Lee, Woo Jin Kim, Yeol Kim, Sun Ha Jee, Jeongseon Kim. Trajectory of DNA methylation in blood up to 18 years prior to lung cancer diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7029.