Diagnosis Of Malignant Pulmonary Nodules Research Articles

Diagnosis of Malignant Pulmonary Nodules Using a Combination of Tumor-associated Autoantibodies and Computed Tomography.

Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection. The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer. The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively. Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.

The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.

Invasive puncture biopsy is currently the main method of identifying benign and malignant pulmonary nodules (PNs). This study aimed to investigate the application effect of chest computed tomography (CT) images, tumor markers (TMs), and metabolomics in the identification of benign and malignant PNs (MPNs). A total of 110 patients with PNs who were hospitalized in Dongtai Hospital of Traditional Chinese Medicine from March 2021 to March 2022 were selected as the study cohort. A retrospective analysis study of chest CT imaging, serum TMs testing, and plasma fatty acid (FA) metabolomics was performed on all participants. According to the pathological results, participants were divided into a MPN group (n=72) and a benign PN (BPN) group (n=38). The morphological signs of CT images, the levels and positive rate of serum TMs, and the plasma FA indicator were compared between groups. There were significant differences between the MPN group and the BPN group in the CT morphological signs, including location of PN and the number of patients with or without lobulation sign, spicule sign, and vessel convergence sign (P<0.05). Serum carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC-Ag) were not significantly different between the 2 groups. The serum contents of CEA and CYFRA 21-1 in the MPN group were remarkably higher than those in the BPN group (P<0.05). The plasma levels of palmitic acid, total omega-3 polyunsaturated FA (W3), nervonic acid, stearic acid, docosatetraenoic acid, linolenic acid, eicosapentaenoic acid, total saturated FA, and total FA were much higher in the MPN group than the BPN group (P<0.05). In conclusion, chest CT images and TMs, combined with metabolomics, has a good application effect in the diagnosis of BPNs and MPNs, and is worthy of further promotion.

The value of folate receptor-positive circulating tumor cells in the diagnosis of lung cancer and its correlation with clinical characteristics.

The aim of this research is to investigate the feasibility of folate receptor-positive circulating tumor cells (FR+CTCs) as a biomarker for the diagnosis of malignant pulmonary nodules and the correlation between clinicopathological factors and FR+CTC levels. Patients initially diagnosed with one or more pulmonary nodules from a computed tomography scan were prospectively included. Three milliliters of peripheral blood was collected from each participant for FR+CTC analysis prior to surgery. Clinical and pathological parameters and FR+CTC levels were compared between patients with lung cancer and benign diseases. Six hundred fifty-three patients had lung cancer and the other 124 had benign lung diseases based on pathological examinations of the resected specimens. The median FR+CTC value of the lung cancer group was 12.0(95% CI 9.6-16.2)FU/3mL and that of the benign group was 7.2(95% CI 5.78-11.2)FU/3mL. The difference was statistically significant (P < 0.0001). In a receiver operating characteristic analysis to distinguish the two groups, the area under curve of FR+CTC was 0.7457 (95% CI 0.6893-0.8021; P < 0.0001) using a cutoff of 8.65 FU/3mL. The sensitivity was 86.37%, and the specificity was 74.19%. Combined with conventional serum tumor biomarkers, the area under curve was 0.922 (0.499-0.963). The sensitivity was 92.20%, and the specificity was 83.05%. FR+CTC levels were related to tumor staging (P4< 0.001), the degree of tumor invasion both in single (P = 0.011) and multiple lesions (P = 0.022), pathological subtypes (P = 0.013), and maximum tumor diameter (P = 0.014). FR+CTC is an effective and reliable biomarker for the diagnosis of lung cancer. Further, FR+CTC level is correlated with tumor staging, degree of invasion, pathological subtypes, and tumor size.

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

ObjectiveTo explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).MethodsA total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.ResultsAttenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.ConclusionIt is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.

Role of chest MRI for the diagnosis of malignant pulmonary nodules: a systematic review and a meta-analysis

AIM: To evaluate the ability of magnetic resonance imaging (MRI) of the chest to detect malignant pulmonary nodules compared to compute tomography (CT).&#x0D; MATERIALS AND METHODS: We searched the following databases with the final date of search on April 7th, 2021: PubMed, Google Scholar. We selected studies according to the inclusion and exclusion criteria that assessed the detection of malignant lung nodules by MRI and CT and included information about sensitivity and specificity. Method of the analysis and data grouping was chosen with regard to statistical heterogeneity of the studies included in the analysis. We used the 2 test and I2 statistic to evaluate the heterogeneity.&#x0D; RESULTS: We selected 168 articles for the systematic review from the PubMed and Google Scholar databases. We included 21 studies on 1,188 patients in the meta-analysis and revealed statistically significant heterogeneity (р0,00001 for 2 test; I2=99%) for sensitivity and specificity. Hence, we used a random-effect model for further analysis. As a result, values of sensitivity for detection of pulmonary nodules with MRI of 70.4%100%, specificity ― from 60.6% to 100%.&#x0D; CONCLUSIONS: Thus, MRI has sufficient sensitivity and specificity for detecting malignant pulmonary nodules primarily discovered with CT.

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

BackgroundLung 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.MethodsWe 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.ResultsThere 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.ConclusionsFive conventional tumor markers had a limited diagnostic value in young patients with pulmonary nodules.

Liquid biopsies to distinguish malignant from benign pulmonary nodules.

Over the past decades, low‐dose computed tomography (LD‐CT) screening has been widely used for the early detection of lung cancer. Increasing numbers of indeterminate pulmonary nodules are now being discovered. However, it remains challenging to distinguish malignant from benign pulmonary nodules, especially those considered to be small or ground‐glass (GGN) nodules. Liquid biopsies have been successfully applied in the diagnosis of advanced lung cancer, and the potential value for early detection of lung cancer has made great progress. Recent studies have demonstrated the value of various blood‐based tumor biomarkers in determining the nature of pulmonary nodules, including cell‐free DNA (cfDNA), microRNAs (miRNAs), circulating tumor cells (CTCs) and tumor‐associated autoantibodies (AAbs). In this review, we summarize the latest progress of liquid biopsies, and their potential applications and challenges in the diagnosis of malignant pulmonary nodules.

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

BackgroundLung 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.MethodsWe 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.ResultsA 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.ConclusionWe 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.

Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.

BackgroundLung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.MethodsA retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer‐Lemeshow test in both the training and validation cohorts.ResultsA radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’ age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793‐0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745‐0.872). The Hosmer‐Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram.ConclusionsThe established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

Diagnostic Accuracy of Maximum Intensity Projection in Diagnosis of Malignant Pulmonary Nodules.

IntroductionPulmonary nodules are frequently encountered during chest imaging, and its evaluation is usually done by chest radiograph and computed tomography (CT) scan of chest. High resolution of multidetector CT (MDCT) has improved the nodule detection. Post processing techniques such as maximum intensity projection (MIP) can further improve the sensitivity of MDCT for nodule detection. Failure to diagnose malignancy in pulmonary nodules can delay the treatment. Therefore, the aim of this study was to determine the diagnostic accuracy of MIP in the diagnosis of malignant pulmonary nodules taking histopathology findings as gold standard.Materials and methodsA retrospective cross-sectional study was conducted at Dow Institute of Radiology, Dow University of Health Sciences, from 1 December 2018 till 30 June 2019. Both male and female patients aged 18 years and above who underwent CT scan of chest with suspicion of pulmonary nodules were included. Patients already diagnosed with malignant pulmonary nodules and presenting for follow-up were excluded. Contrast-enhanced CT chest was performed on a multi-slice scanner. MIP reconstruction and evaluation was performed on the workstation. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP were calculated taking histopathology findings as gold standard.ResultsA total of 202 patients were included in this study. The mean age of the patients was 55.87 ± 13.08 years. A total of 103 patients (51.0%) were males and 99 patients (49.0%) were females. There were 131 (64.9%) nodules with smooth margins and 71 (35.1%) nodules with irregular margins. The mean size of nodule was 3.1 ± 0.7 cm. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP in diagnosing malignant pulmonary nodules were found to be 85.82%, 82.35%, 90.55%, 74.67%, and 84.65%, respectively, taking histopathology findings as gold standard. The nodules >3 cm in size had a higher sensitivity for diagnosing malignant pulmonary nodules. Smooth margin nodule had high sensitivity, specificity, and diagnostic accuracy for diagnosing malignant pulmonary nodules.ConclusionMIP images have high sensitivity, specificity, and diagnostic accuracy in the diagnosis of malignant pulmonary nodules. The utilization of MIP images can aid in the detection of malignant pulmonary nodules and help in formulating early treatment strategies for the patients. Other post processing techniques such as volume rendering and computer-aided detection can help in further improving patient care.

Early Detection of Lung Cancer by Molecular Markers in Endobronchial Epithelial-Lining Fluid

Early detection of malignancies in the lung by less-invasive methods aims at achieving efficient intervention and subsequently a reduction of the high mortality rate. We investigated whether biomarker analysis in endobronchial epithelial-lining fluid (ELF) collected by bronchoscopic microsampling (BMS) may be useful for a definitive preoperative diagnosis. ELF was collected from subsegmental bronchi close to the indeterminate pulmonary nodule, which was detected by computed tomography, and from the contralateral lung. Diagnosis was confirmed by transbronchial biopsy or surgery. The study includes 142 ELF samples from 51 non-small-cell lung cancer patients and 20 benign cases. Microarray analysis was done with a patient subset (n = 15) to narrow down genes associated with a malignant phenotype. Thirteen potential biomarkers have been further analyzed by quantitative real-time polymerases chain reaction in an independent patient cohort (n = 56). All patients underwent BMS without complications. Gene-expression analyses by microarrays and quantitative real-time polymerases chain reaction could be reliably applied to ELF samples, and resulted in potential biomarkers for malignant pulmonary nodules. Four genes (tenascin-C, [C-X-C motif] ligand 14, S100 calcium binding protein A9, and keratin 17) were found to be upregulated in ELF of non-small-cell lung cancer patients with adenocarcinoma or squamous cell carcinoma. Combined analysis of tenascin-C expression and the nodule size improved the prediction of malignancy in this patient cohort. Our study suggests that the analysis of specific biomarkers in ELF collected by BMS could be a potentially useful adjunct to other diagnostic techniques aiming at the preoperative diagnosis of malignant pulmonary nodules.

Characterization of pulmonary nodules and mediastinal staging of bronchogenic carcinoma with F-18 fluorodeoxyglucose positron emission tomography.

To evaluate F-18 fluorodeoxyglucose positron emission tomography (PET) in terms of its sensitivity and specificity in diagnosing malignant pulmonary nodules and staging bronchogenic carcinoma. A retrospective review of any patient that presented to the VA Palo Alto Health Care System with a pulmonary nodule between 9/94 and 3/96 revealed 49 patients (four female, 45 male) age 37-85 (mean 63) with 54 pulmonary nodules who had: chest CT scan, PET scan; and tissue characterization of the nodule. Characterization of each nodule was achieved by histopathologic (N = 44) or cytopathologic (N = 10) analysis. Of the 49 patients, 18 had bronchogenic carcinoma which was adequately staged. Mediastinal PET and CT findings in these 18 patients were compared with the surgical pathology results. N2 disease was defined as mediastinal lymph node involvement by the American Thoracic Society's classification system. Mediastinal lymph nodes were interpreted as positive by CT if they were larger that 1.0 cm in the short-axis diameter. Sensitivity and specificity for the diagnosis of malignant pulmonary nodules using PET was 93 and 70%, respectively. All nodules (N = 3) that were falsely positive by PET scan were infectious in origin. All nodules (N = 4) that were falsely negative by PET were technically limited studies (outdated scanner, no attenuation correction, hyperglycemia) except for one case of metastatic adenocarcinoma. The sensitivity and specificity of PET in diagnosing N2 disease was 67 and 100%, compared with 56% and 100% for CT scan (not statistically significant). However, one more patient with N2 disease was correctly diagnosed by PET than by CT scan. PET is a valuable tool in the diagnosis and management of pulmonary nodules and may more accurately stage patients with bronchogenic carcinoma than CT scanning alone.