Diagnosis Of Early Stage Lung Cancer Research Articles

Abstract B10: Feasibility of bronchial washing fluid-based approach to early-stage lung cancer diagnosis

Abstract Circulating tumor DNA has few applications for detecting early-stage lung cancers. Bronchial washing (BW) is minimally invasive, and BW fluids can contain tumor DNA. Thus, analysis of BW fluids could be used for precision medicine. Forty-eight paired specimens (primary tumor tissue, normal tissue, BW supernatant, and BW precipitate) from twelve patients with early-stage non-small cell lung cancer were analyzed using ultradeep next-generation sequencing with a custom panel containing 113 genes. In primary tumors, 130 missense mutations/indels (5–16 per patient) and 20 driver mutations (0–3) were found; 105 (80.8%) and 97 (74.6%) of these missense mutations/indels were identified in BW supernatants and precipitates, respectively, and 19 (95.0%) of the driver mutations in both BW supernatants and precipitates. Concordance between EGFR mutations in primary tumors, BW supernatants, and BW precipitates was 100%. The median allele frequencies (AFs) for missense mutations/indels in primary tumors, BW supernatants, and BW precipitates were 13.0%, 0.3%, and 0.1%, respectively. The AFs of missense mutations/indels in BW supernatants correlated with those in primary tumors and in BW precipitates (P<0.001). In conclusion, this study provides evidence that the BW fluids, especially supernatants, can be an alternative to overcome the limitation that clinical utility of ctDNA is often not reliable in early-stage NSCLC. Citation Format: Jeong Seon Ryu, Myoung Kyu Lee, Seung Jae Lee. Feasibility of bronchial washing fluid-based approach to early-stage lung cancer diagnosis [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B10.

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

Circulating Tumor Cells as a Screening and Diagnostic Marker for Early-Stage Non-Small Cell Lung Cancer.

BackgroundCirculating tumor cells (CTCs) have become potential diagnostic biomarker for several types of cancer, including lung cancer. In this study, we aim to determine whether CTCs detected by CellCollector can be used for early-stage diagnosis of lung cancer.MethodsIn this study, we recruited 64 volunteers, among whom 44 were suspected lung cancer patients requiring surgical treatment and 20 were healthy volunteers. We simultaneously analyzed PD-L1 expression in CTCs isolated using the GILUPI CellCollector and copy number variation by next-generation sequencing (NGS).ResultsWe enrolled a total of 44 patients with suspected lung cancer who required surgery and 20 healthy volunteers. The patients were classified into 4 groups based on their pathological results: benign disease, in situ cancer, microinvasive, and invasive. The CTCs detection rate for each group was 10.00% (1/10), 45% (5/11), 50% (7/14), and 67% (6/9), respectively. Among the patients with lung cancer, the CTCs detection rate increased with disease progression. The rate of CTCs positivity was 52.94% (18/34) in patients who were diagnosed with lung cancer by pathology and 10% (1/10) in patients with benign disease. CTCs were not detected in the control group. The area under the receiver operating characteristic (ROC) curve, a measure for distinguishing patients with primary lung cancer, was 0.715 (95% CI 0.549–0.880, P=0.041). The sensitivity and specificity of the in vivo CTCs detection strategy for the diagnosis of early-stage lung cancer were 52.94% and 90%, respectively. CTCs were associated with clinical pathology but not with the size and location of the nodules.ConclusionCTCs isolation using the CellCollector in vivo detection method might be effective for distinguishing between benign and malignant nodules and may be used for early-stage diagnosis of lung cancer.

Integration of IgA and IgG Autoantigens Improves Performance of Biomarker Panels for Early Diagnosis of Lung Cancer

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. In our previous study, we surveyed both IgG and IgM-bound serological biomarkers and validated a panel of IgG-bound autoantigens for early LC diagnosis with 50% sensitivity at 90% specificity. To further improve the performance of these serological biomarkers, we surveyed HuProt arrays, comprised of 20,240 human proteins, for IgA-bound autoantigens because IgAs are a major immunoglobulin isotype in the lung. Integrating with IgG-bound autoantigens, we discovered and validated a combined biomarker panel using ELISA-format tests. Specifically, in Phase I, we obtained IgA-based autoimmune profiles of 69 early stage LC patients, 30 healthy subjects and 25 patients with lung benign lesions (LBL) on HuProt arrays and identified 28 proteins as candidate autoantigens that were significantly associated with early stage LC. In Phase II, we re-purified the autoantigens and converted them into an ELISA-format testing to profile an additional large cohort, comprised of 136 early stage LC patients, 58 healthy individuals, and 29 LBL patients. Integration of IgG autoimmune profiles allowed us to identify and validate a biomarker panel of three IgA autoantigens (i.e. BCL7A, and TRIM33 and MTERF4) and three IgG autoantigens (i.e. CTAG1A, DDX4 and MAGEC2) for diagnosis of early stage LC with 73.5% sensitivity at >85% specificity. In Phase III, the performance of this biomarker panel was confirmed with an independent cohort, comprised of 88 early stage LC patients, 18 LBL patients, and 36 healthy subjects. Finally, a blind test on 178 serum samples was conducted to confirm the performance of the biomarker panel. In summary, this study demonstrates for the first time that an integrated panel of IgA/IgG autoantigens can serve as valuable biomarkers to further improve the performance of early diagnosis of LC.

Bronchoscopic navigation and tissue diagnosis.

Diagnosis of early-stage lung cancer has become increasingly important as the detection of peripheral pulmonary lesions (PPLs) grows with widespread adoption of CT-based lung cancer screening. Although CT-guided transthoracic needle aspiration has been the standard diagnostic approach for PPLs, transbronchial sampling by bronchoscopy is often performed due to its reduced rate of adverse events. However, the diagnostic yield of conventional bronchoscopy is often poor. Various bronchoscopic technologies have emerged over recent years to address this limitation, including thin/ultrathin bronchoscopes, radial probe endobronchial ultrasound (RP-EBUS), virtual navigation bronchoscopy (VBN), electromagnetic navigation bronchoscopy (ENB), and robotic bronchoscopy. Bronchoscopic transparenchymal nodule access (BTPNA) and transbronchial access tool (TBAT) are novel techniques that leverage navigational bronchoscopic technologies to further improve access to lesions throughout the lung. The devices used for sampling tissue have similarly evolved, such as the introduction of cryobiopsy. These innovative bronchoscopic techniques allows higher diagnostic yield even in small PPLs. Given the complexity of these new techniques and technologies, it is important for physicians to understand their strengths and limitations.

Disparities in Early-Stage Lung Cancer Diagnosis Before and after Implementation of Screening Guidelines

Nobel, Tamar B. MD; Sihag, Smita MD, MPH; Boffetta, Paolo MD; Adusumilli, Prasad MD, FACS; Isbell, James M. MD, MSCI, FACS; Bott, Matthew MD; Rocco, Gaetano MD; Rusch, Valerie MD, FACS; Jones, David R. MD, FACS; Molena, Daniela MD, FACS Author Information

Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies.

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Feasibility of Bronchial Washing Fluid-Based Approach to Early-Stage Lung Cancer Diagnosis.

A blood-based approach such as circulating tumor DNA remains challenging in diagnosis for early-stage disease. Bronchial washing (BW) is a minimally invasive procedure that yields fluids that may contain tumor DNA. Therefore, we prospectively enrolled 12 patients with early-stage non-small cell lung cancer without endoscopically visible tumors. Somatic mutations were analyzed using ultra-deep next-generation sequencing in 48 paired specimens (primary tumor tissue, normal tissue, BW supernatant, and BW precipitate). In primary tumors, 130 missense mutations/indels (5-16 per patient) and 20 driver mutations (0-3 per patient) were found. Concordance of driver mutations between BW fluids and primary tumors was 95.0%. The allele frequencies for missense mutations/indels in BW supernatants significantly correlated with those in primary tumors and were higher than those in BW precipitates. These findings suggest that BW supernatants are reflective of tumor-associated mutations and could be used for early-stage lung cancer diagnosis.

Development of a Noninvasive Method for Early Stage Lung Cancer Diagnosis Based on Methylation Analysis of SHOX2 and PTGER4

AbstractThe oncogenesis of lung cancer is often accompanied by significant methylation abnormalities, which offer a practical and efficient method for diagnosis. Considering that early diagnosis is of great significance for improving the therapeutic effect of lung cancer, the current study aims to develop a diagnostic method based on methylation level analysis. A specific quantitative polymerase chain reaction system, which quantitatively determines the methylation level of SHOX2 and PTGER4 genes, is constructed. Samples used in this reaction system are obtained through noninvasive means, that is, by enriching cell‐free DNA from peripheral blood. 1303 peripheral blood samples, 475 samples from healthy controls and 828 from patients with nonmalignant lung diseases or lung cancer are collected. The specificity and sensitivity of this system to diagnose early stage lung cancer (stage Tis0 and I) are 95.4% and 68.6%, respectively. When the system is used to detect samples from healthy controls or benign lung disease patients, the specificity and sensitivity reach as high as 96.0% and 92.4%, respectively. These findings suggest that screening early stage lung cancer lesions through determining the methylation of SHOX2 and PTGER4 genes in cell‐free DNA represents a promising tool for clinical application.

Non-invasive diagnosis of early-stage lung cancer using high-throughput targeted DNA methylation sequencing of circulating tumor DNA (ctDNA).

Rational: LDCT screening can identify early-stage lung cancers yet introduces excessive false positives and it remains a great challenge to differentiate malignant tumors from benign solitary pulmonary nodules, which calls for better non-invasive diagnostic tools.Methods: We performed DNA methylation profiling by high throughput DNA bisulfite sequencing in tissue samples (nodule size < 3 cm in diameter) to learn methylation patterns that differentiate cancerous tumors from benign lesions. Then we filtered out methylation patterns exhibiting high background in circulating tumor DNA (ctDNA) and built an assay for plasma sample classification.Results: We first performed methylation profiling of 230 tissue samples to learn cancer-specific methylation patterns which achieved a sensitivity of 92.7% (88.3% - 97.1%) and a specificity of 92.8% (89.3% - 96.3%). These tissue-derived DNA methylation markers were further filtered using a training set of 66 plasma samples and 9 markers were selected to build a diagnostic prediction model. From an independent validation set of additional 66 plasma samples, this model obtained a sensitivity of 79.5% (63.5% - 90.7%) and a specificity of 85.2% (66.3% - 95.8%) for differentiating patients with malignant tumor (n = 39) from patients with benign lesions (n = 27). Additionally, when tested on gender and age matched asymptomatic normal individuals (n = 118), our model achieved a specificity of 93.2% (89.0% - 98.3%). Specifically, our assay is highly sensitive towards early‐stage lung cancer, with a sensitivity of 75.0% (55.0%-90.0%) in 20 stage Ia lung cancer patients and 85.7% (57.1%-100.0%) in 7 stage Ib lung cancer patients.Conclusions: We have developed a novel sensitive blood based non‐invasive diagnostic assay for detecting early stage lung cancer as well as differentiating lung cancers from benign pulmonary nodules.

Abstract A22: Establishing a deep cfDNA methylation sequencing-based signature for noninvasive early-stage lung cancer diagnosis

Abstract Although mortality rate has been greatly reduced by low-dose computed tomography (LDCT) screening of high-risk patients, the benefit is compromised by the high false-positive rate, thus necessitating the development of biomarkers differentiating benign and malignant nodules. The role of aberrant DNA methylation in the process of tumorigenesis both at individual genes and a genome-wide scale has been well elucidated. It occurs very early in cancer development, and thus is capable of serving as a diagnostic biomarker. In this prospective study, we evaluated the feasibility and performance of assessing cfDNA methylation status as an early diagnostic marker for lung cancer. We developed a novel targeted bisulfite sequencing assay called ELSA and designed a methylation panel based on TCGA data on 838 lung cancer patients and 74 normal controls. We then interrogated the methylation status of 7,572 regions consisting of about 100,000 CpG sites from plasma samples of 163 newly recruited Chinese patients with early-stage NSCLC and 41 with benign nodules using our panel. A region-based methylation statistic was used in the classification model. We constructed diagnostic classification models using elastic net and random forest based on 3,000 DMRs selected by logistic regression between benign and malignant nodules using FFPE samples. Within this cohort, 54 patients had malignant tumor (39 stage I, 6 stage II and 9 stage IIIa) and 30 patients had benign nodules. We performed 5-fold cross-validation with 20 time repeats to gain a robust estimation of model performance, achieving a sensitivity of 92.6%, specificity of 100% and AUC of 98.1%. We further refined the DMRs selected above using 68 healthy baseline plasmas, and subsequently constructed a weighting decision tree model for plasma samples obtained from a separate cohort consisting of 163 patients with malignant tumor (127 stage I, 36 stage II and above) and 41 patients with hamartoma, chronic inflammation, granuloma, or other lung benign lesions, achieving a sensitivity of 84.0%, specificity of 85.7% and AUC of 90.7% based on 170 markers. We further analyzed PPVs according to stage and tumor size. PPVs for patients with stage I, II, and III diseases are 82.4%, 84.6%, and 94.4%, respectively; and for tumor diameter less than 1 cm, between 1-2 cm, and greater than 2 cm are 78.5%, 87.1%, and 88.2%, respectively. Citation Format: Zhihong Zhang, Naixin Liang, Wanglong Deng, Chenyang Wang, Tao Zheng, Wenjun Li, Jian Hu, Jinh Su, Han Han-Zhang, Bingsi Li, Hao Liu, Shanqing Li. Establishing a deep cfDNA methylation sequencing-based signature for noninvasive early-stage lung cancer diagnosis [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr A22.

Identification of Serological Biomarkers for Early Diagnosis of Lung Cancer Using a Protein Array-Based Approach

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.

Abstract 5383: Non-invasive diagnosis of early-stage lung cancer via targeted high-throughput DNA methylation sequencing of circulating tumor DNA (ctDNA)

Abstract Current state-of-the-art lung cancer early screening involves using low-dose CT scan to identify lung nodules smaller than 3cm in diameter. However, it’s still a clinical dilemma to differentiate between malignant and benign nodules. We took the approach of methylation profiling by high-throughput bisulfite DNA sequencing in tissue samples to identify specific methylation signatures. We learned methylation patterns that differentiate malignant vs. benign lesions from tissue samples by in-depth data mining, and then used pattern matching to classify plasma samples. Given the usual low amount of ctDNA in plasma, we also developed an ultra-sensitive library preparation method to perform targeted bisulfite DNA sequencing from as low as 1ng of cfDNA or 1mL of plasma. From a training set of 88 tissue samples, which includes 60 malignant specimens with different subtypes (invasive adenomas, IA; minimally invasive adenomas, MIA; atypical adenomatous hyperplasia, AAH; adenocarcinoma in situ, AIS; carcinoid; lymphoepithelioma-like carcinoma of the lung, LELC; squamous carcinoma, SC; as well as 28 benign specimens in different categories including hematomas, granuloma, tuberculosis, inflammatory pseudotumor (IPT), sclerosing hemangiomas (SHL) and infections, we were able to achieve a sensitivity of 95% for identification of malignant lesions, with a specificity of 78.6%. From an independent validation set of 45 plasma samples, we achieved a sensitivity of 94.7% and a specificity of 85.7% for differentiating patients with malignant tumor from patients with benign lesions. Specifically, our assay is demonstrated to be highly sensitive towards early-stage lung cancer detection, with a sensitivity of 93.3% in a total of 15 patients with stage IA/B lung cancer. In summary, we have developed a highly sensitive blood-based non-invasive diagnostic assay for identification of early stage lung cancer, which can aid clinical decisions for patients with a CT scan positive for lung nodules. This approach can also be extended to non-invasive early screening for various cancer types. Citation Format: Xuyu Cai, Yangbin Gao, Hui Shen, Peter Laird, Jian-bing Fan, Weihong Xu, Wenhua Liang, Jianxing He. Non-invasive diagnosis of early-stage lung cancer via targeted high-throughput DNA methylation sequencing of circulating tumor DNA (ctDNA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5383. doi:10.1158/1538-7445.AM2017-5383

LncRNA16 is a potential biomarker for diagnosis of early-stage lung cancer that promotes cell proliferation by regulating the cell cycle.

Early diagnosis of lung cancer greatly reduces mortality; however, the lack of suitable plasma biomarkers presents a major obstacle. Recent studies showed that long noncoding RNAs (lncRNAs) played important roles in cancer initiation and development. Here, we identified differentially expressed lncRNAs in 20 lung cancer samples by using custom designed microarray and evaluated their expression in 118 lung cancer samples by real-time PCR (qRT-PCR). lncRNA16 (ENST00000539303) expression was significantly higher in lung cancer tissues (80/118) than in adjacent matched normal tissues. Importantly, this increase was similar to that in plasma (53/84) of lung cancer patients, including early stage. The role of lncRNA16 in lung cancer was studied in vitro and in vivo by using the lung cancer cell lines and xenograft mouse models. The results reveal that knockdown of lncRNA16 inhibited proliferation of PC9 cells in vitro and also inhibited tumor growth in xenograft mouse models. Overexpression of lncRNA16 promoted proliferation of A549 cells in vitro and also promoted tumor growth in xenograft mouse models. Specifically, we showed that lncRNA16 promoted G2/M transition by regulating cyclin B1 transcription. Together, our findings suggest that lncRNA16 is a promising biomarker suitable for early diagnosis of lung cancer, and a potential target for lung cancer treatment.

Motion Planning for a Three-Stage Multilumen Transoral Lung Access System.

Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios.

"Sentinel" circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a risk factor for lung cancer. Migration of circulating tumor cells (CTCs) into the blood stream is an early event that occurs during carcinogenesis. We aimed to examine the presence of CTCs in complement to CT-scan in COPD patients without clinically detectable lung cancer as a first step to identify a new marker for early lung cancer diagnosis. The presence of CTCs was examined by an ISET filtration-enrichment technique, for 245 subjects without cancer, including 168 (68.6%) COPD patients, and 77 subjects without COPD (31.4%), including 42 control smokers and 35 non-smoking healthy individuals. CTCs were identified by cytomorphological analysis and characterized by studying their expression of epithelial and mesenchymal markers. COPD patients were monitored annually by low-dose spiral CT. CTCs were detected in 3% of COPD patients (5 out of 168 patients). The annual surveillance of the CTC-positive COPD patients by CT-scan screening detected lung nodules 1 to 4 years after CTC detection, leading to prompt surgical resection and histopathological diagnosis of early-stage lung cancer. Follow-up of the 5 patients by CT-scan and ISET 12 month after surgery showed no tumor recurrence. CTCs detected in COPD patients had a heterogeneous expression of epithelial and mesenchymal markers, which was similar to the corresponding lung tumor phenotype. No CTCs were detected in control smoking and non-smoking healthy individuals. CTCs can be detected in patients with COPD without clinically detectable lung cancer. Monitoring “sentinel” CTC-positive COPD patients may allow early diagnosis of lung cancer.

Spiral computed tomography screening for the diagnosis of early-stage lung cancer.

Spiral computed tomography screening for the diagnosis of early-stage lung cancer.