Diagnostic value of conventional tumor markers in young patients with pulmonary nodules.

BACKGROUND: Lung cancer is one of the most common malignancies, and there is a trend of increasing incidence in young patients. The preoperative diagnosis of pulmonary nodules is mainly based on the combination of imaging and tumor markers. There is no relevant report on the diagnostic value of tumor markers in young pulmonary nodules. Our study was designed to explore the value of five tumor markers in young patients with pulmonary nodules. METHODS: We reviewed the medical records of 390 young patients (age ≤45 years) with pulmonary nodules treated at two separate centers from January 1, 2015, to January 1, 2021. Malignant pulmonary nodules were confirmed in 318 patients, and the other 72 patients were diagnosed with benign pulmonary nodules. The gold standard for diagnosis of pulmonary nodules was surgical biopsy. The conventional serum biomarkers included cytokeratin 19 (CYFRA21-1), pro-gastrin-releasing-peptide (ProGRP), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and squamous cell carcinoma-associated antigen (SCCA). The diagnostic values of five tumor markers were analyzed by receiver operating characteristic (ROC) curves. RESULTS: There were no significant differences in the expression of five tumor markers between the groups (p > 0.05). Single tumor marker (CYFRA21-1, ProGRP, CEA, NSE, and SCCA) showed a limited value in the diagnosis of malignant pulmonary nodules, with the AUC of 0.506, 0.503 0.532, 0.548, and 0.562, respectively. The AUC of the combined examination was only 0.502~0.596, which did not improve the diagnostic value. CONCLUSIONS: Five conventional tumor markers had a limited diagnostic value in young patients with pulmonary nodules.

A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules.

Addressing the high false-positive rate of conventional low-dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood-based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing- (NGS-) based cell-free DNA (cfDNA) mutation profiling, NGS-based cfDNA methylation profiling, and blood-based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high-risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98-patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29-patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%.

DNA methylation of PTGER4 in peripheral blood plasma helps to distinguish between lung cancer, benign pulmonary nodules and chronic obstructive pulmonary disease patients.

BACKGROUND/INTRODUCTION: In contrast to patients who present with advanced stage lung cancer and associated poor prognosis, patients with early-stage lung cancer may be candidates for curative treatments. The results of the NELSON lung cancer screening trial are expected to stimulate the development and implementation of a lung cancer screening strategy in most countries. Widespread use of chest computed tomography scans will also result in the detection of solitary pulmonary nodules. Because reliable biomarkers to distinguish between malignant and benign lesions are lacking, tissue-based histopathological diagnostics remain the gold standard. In this study, we aimed to establish a test to assess the predictive ability of DNA hypermethylation of SHOX2 and PTGER4 in plasma to discriminate between patients with 1.) lung cancer, 2.) benign lesions, and 3.) patients with chronic obstructive pulmonary disease (COPD). PATIENTS AND METHODS: We retrospectively analysed SHOX2 and PTGER4 methylation in 121 prospectively collected plasma samples of patients with lung cancer (group 1A), benign lesions (group 1B), and COPD without nodules (group 2). RESULTS: PTGER4 DNA hypermethylation was more frequently observed in patients with lung cancer than in controls (p = 0.0004). Results remained significant after correction for tumour volume, smoking status, age, and eligibility for the NELSON trial. CONCLUSIONS: Detection of methylated PTGER4 in plasma DNA may serve as a biomarker to support clinical decision-making in patients with pulmonary lesions at lung cancer screening in high-risk populations. Further exploration in prospective studies is warranted.

A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules.

BACKGROUND: Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. METHODS: We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike's information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. RESULTS: A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843-0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739-0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value. CONCLUSION: We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

[Establishment and Verification of A Novel Predictive Model of Malignancy
for Non-solid Pulmonary Nodules].

BACKGROUND: Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of ground-glass-opacity (GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an objective evidence for invasive procedure and avoid unnecessary operation and the consequences. METHODS: We retrospectively analyzed the basic demographics, serum tumor markers and imaging features of 362 cases of non-solid pulmonary nodule from January 2013 to April 2018. All nodules received biopsy or surgical resection, and got pathological diagnosis. Cases were randomly divided into two groups. The modeling group was used for univariate analysis and logistic regression to determine independent risk factors and establish the predictive model. Data of the validation group was used to validate the predictive value and make a comparison with other models. RESULTS: Of the 362 cases with non-solid pulmonary nodule, 313 (86.5%) cases were diagnosed as AAH/AIS, MIA or invasive adenocarcinoma, 49 cases were diagnosed as benign lesions. Age, serum tumor markers CEA and Cyfra21-1, consolidation tumor ratio value, lobulation and calcification were identified as independent risk factors. The AUC value of the ROC curve was 0.894, the predictive sensitivity and specificity were 87.6%, 69.7%, the positive and negative predictive value were 94.8%, 46.9%. The validated predictive value is significantly better than that of the VA, Brock and GMUFH models. CONCLUSIONS: Proved with high predictive sensitivity and positive predictive value, this novel model could help enable preliminarily screening of "high-risk" non-solid pulmonary nodules before biopsy or surgical excision, and minimize unnecessary invasive procedure. This model achieved preferable predictive value, might have great potential for clinical application.

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT.

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.

Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial.

BACKGROUND: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%. METHODS: A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made. RESULTS: A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified. CONCLUSIONS: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

Combining serum miRNAs, CEA, and CYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules: a pilot study : Xianfeng Li et al.: Combining biomarker, imaging, and clinical features to distinguish pulmonary nodules.

BACKGROUND: Our study was designed to improve the accuracy of determining whether pulmonary nodules are benign or malignant. METHODS: We evaluated the clinical and imaging features and serum markers: neuron specific enolase (NSE), carcino-embryonic antigen (CEA), cytokeratin fragment antigen 21-1 (CYFRA 21-1), miRNA-21-5p, and miR-574-5pof in 39 patients with pathology information. Factors that differed significantly between those with benign versus malignant pulmonary nodules were used to establish a prediction model for identifying malignant nodules. RESULTS: The studied nodules were 51.3% malignant and 48.7% benign. Age, smoking status, nodule diameter, history of emphysema, vascular sign, burr sign, CYFRA21-1, CEA, miRNA-21-5p, and miRNA-574-5p differed significantly between the benign and malignant nodule groups. Serum levels of CYRFA21-1 and CEA could be used to distinguish between malignant and benign nodules with a positive predictive value (PPV) of 80.0%, a negative predictive value (NPV) of 84.2%, and an area under the receiver operating characteristics curve (AUC) of 0.863. Using the serum levels of miRNA-21-5p and miRNA-574-5p, the PPV was 55%, the NPV was 84.2%, and the AUC was 0.797. When all four serum markers were combined, the PPV was 80%, the NPV was 89.5%, and the AUC was 0.921. We established a prediction model for malignant nodules, including clinical features, imaging features, and serum markers. In cross-validation, the ratio of discriminant conformance was 95%. CONCLUSIONS: Serum levels of miRNA-21-5p and miRNA-574-5p are significantly higher in patients with malignant nodules than in patients with benign nodules and are potential serum biomarkers. Our prediction model could improve malignant nodule diagnosis.

Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer.

INTRODUCTION: The incidence of pulmonary nodules is increasing with the movement toward screening for lung cancer by low-dose computed tomography. Given the large number of benign nodules detected by computed tomography, an adjunctive test capable of distinguishing malignant from benign nodules would benefit practitioners. The ability of the EarlyCDT-Lung blood test (Oncimmune Ltd., Nottingham, United Kingdom) to make this distinction by measuring autoantibodies to seven tumor-associated antigens was evaluated in a prospective registry. METHODS: Of the members of a cohort of 1987 individuals with Health Insurance Portability and Accountability Act authorization, those with pulmonary nodules detected, imaging, and pathology reports were reviewed. All patients for whom a nodule was identified within 6 months of testing by EarlyCDT-Lung were included. The additivity of the test to nodule size and nodule-based risk models was explored. RESULTS: A total of 451 patients (32%) had at least one nodule, leading to 296 eligible patients after exclusions, with a lung cancer prevalence of 25%. In 4- to 20-mm nodules, a positive test result represented a greater than twofold increased relative risk for development of lung cancer as compared with a negative test result. Also, when the "both-positive rule" for combining binary tests was used, adding EarlyCDT-Lung to risk models improved diagnostic performance with high specificity (>92%) and positive predictive value (>70%). CONCLUSIONS: A positive autoantibody test result reflects a significant increased risk for malignancy in lung nodules 4 to 20 mm in largest diameter. These data confirm that EarlyCDT-Lung may add value to the armamentarium of the practitioner in assessing the risk for malignancy in indeterminate pulmonary nodules.

Plasma cell-free DNA levels and integrity in patients with chest radiological findings: NSCLC versus benign lung nodules.

Effective discrimination between lung cancer and benign tumours is a common clinical problem in the differential diagnosis of solitary pulmonary nodules. The analysis of cell-free DNA (cfDNA) in blood may greatly aid the early detection of lung cancer by evaluating cancer-related alterations. The plasma cfDNA levels and integrity were analysed in 65 non-small cell lung cancer (NSCLC) patients, 28 subjects with benign lung tumours, and 16 healthy controls using real-time PCR. The NSCLC patients demonstrated significantly higher mean plasma cfDNA levels compared with those with benign tumours (P = 0.0009) and healthy controls (P < 0.0001). The plasma cfDNA integrity in healthy individuals was significantly different than that found in patients with NSCLC or benign lung tumours (P < 0.0003). In ROC curve analysis, plasma cfDNA levels >2.8 ng/ml provided 86.4% sensitivity and 61.4% specificity in discriminating NSCLC from benign lung pathologies and healthy controls. cfDNA integrity showed better discriminatory power (91% sensitivity, 68.2% specificity). These data demonstrate that plasma cfDNA concentration and integrity analyses can significantly differentiate between NSCLC and benign lung tumours. The diagnostic capacity of the quantitative cfDNA assay is comparable to the values presented by conventional imaging modalities used in clinical practice.

Validation of a multiprotein plasma classifier to identify benign lung nodules.

INTRODUCTION: Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs. METHODS: A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples. RESULTS: The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model. CONCLUSIONS: This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

Distinguishing patients with stage I lung cancer versus control individuals using serum mass profiling.

Serum mass profiling can discern physiological changes associated with specific disease states and their progression. Sera (86 total) from control individuals and patients with stage I nonsmall cell lung cancer or benign small pulmonary nodules were discriminated retrospectively by serum changes discerned by mass profiling. Control individuals were distinguished from patients with Stage I lung cancer or benign nodules with test sensitivities of 89% and 83%. Lung cancer patients versus those with benign nodules were distinguished with 80% sensitivity. This study exhibits progress toward a minimally-invasive aid in early detection of lung cancer and monitoring small pulmonary nodules for malignancy.

Development and validation of a plasma biomarker panel for discerning clinical significance of indeterminate pulmonary nodules.

INTRODUCTION: The recent findings of the National Lung Screening Trial showed 24.2% of individuals at high risk for lung cancer having one or more indeterminate nodules detected by low-dose computed tomography-based screening, 96.4% of which were eventually confirmed as false positives. These positive scans necessitate additional diagnostic procedures to establish a definitive diagnosis that adds cost and risk to the paradigm. A plasma test able to assign benign versus malignant pathology in high-risk patients would be an invaluable tool to complement low-dose computed tomography-based screening and promote its rapid implementation. METHODS: We evaluated 17 biomarkers, previously shown to have value in detecting lung cancer, against a discovery cohort, comprising benign (n = 67) cases and lung cancer (n = 69) cases. A Random Forest method based analysis was used to identify the optimal biomarker panel for assigning disease status, which was then validated against a cohort from the Mayo Clinic, comprising patients with benign (n = 61) or malignant (n = 20) indeterminate lung nodules. RESULTS: Our discovery efforts produced a seven-analyte plasma biomarker panel consisting of interleukin 6 (IL-6), IL-10, IL-1ra, sIL-2Rα, stromal cell-derived factor-1α+ß, tumor necrosis factor α, and macrophage inflammatory protein 1 α. The sensitivity and specificity of our panel in our validation cohort is 95.0% and 23.3%, respectively. The validated negative predictive value of our panel was 93.8%. CONCLUSION: We developed a seven-analyte plasma biomarker panel able to identify benign nodules, otherwise deemed indeterminate, with a high degree of accuracy. This panel may have clinical utility in risk-stratifying screen-detected lung nodules, decrease unnecessary follow-up imaging or invasive procedures, and potentially avoid unnecessary morbidity, mortality, and health care costs.

Diagnostic characteristics of a serum biomarker in patients with positron emission tomography scans.

BACKGROUND: Surgery for pulmonary nodules results in a benign diagnosis in 10% to 30% of cases. Computed tomography and fluorodeoxyglucose-positron emission tomography (FDG-PET) are highly sensitive but less specific. High-risk patients (age > 55 years and smoke > 15 pack-years) for lung cancer with negative FDG-PET scans, or low-risk patients (age < 55 years or smoke < 15 pack-years) with FDG-PET-avid lesions may have higher rates of benign nodules. We hypothesized that our serum biomarker improves diagnostic accuracy by providing greater specificity. METHODS: Fifty-eight patients with pulmonary nodules (< or = 3 cm) were prospectively enrolled. We tested the accuracy of our proteomic biomarker in the serum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Malignancy rates, contingency tables, sensitivity, and specificity analyses were calculated for the entire group and in a subset of patients at high risk for benign disease. RESULTS: We identified 46 (79%) lung cancers and 12 (21%) benign lesions. Forty-five nodules were FDG-PET-avid. In 36 high-risk patients with FDG-PET-avid lesions, 32 (89%) had cancer. Of the remaining 22 lower-risk patients, 14 (64%) had cancer (p = 0.02). The serum biomarker sensitivity was 26.1%, specificity was 91.7%, positive predictive value was 92%, negative predictive value was 24%, and overall accuracy was 40%. The serum signature accurately predicted all eight benign nodules in this 22-patient subset. CONCLUSIONS: The serum protein biomarker has a high specificity. This biomarker has a high positive predictive value but low negative predictive value and may improve noninvasive evaluation of lung nodules. Validation in a larger population is warranted.