Differential Diagnosis Of Lung Cancer Research Articles

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Human epididymis protein 4, a novel potential biomarker for diagnostic and prognosis monitoring of lung cancer.

This study aimed to explore the application value of human epididymis protein 4 (HE4) in diagnosing and monitoring the prognosis of lung cancer. First, TCGA (The Cancer Genome Atlas) databases were used to analyze whey-acidic-protein 4-disulfide bond core domain 2 (WFDC2) gene expression levels in lung cancer tissues. Then, a total of 160 individuals were enrolled, categorized into three groups: the lung cancer group (n = 80), the benign lesions group (n = 40), and the healthy controls group (n = 40). Serum HE4 levels and other biomarkers were quantified using an electro-chemiluminescent immunoassay. Additionally, the expression of HE4 in tissues was analyzed through immunohistochemistry (IHC). In vitro cultures of human airway epithelial (human bronchial epithelial [HBE]) cells and various lung cancer cell lines (SPC/PC9/A594/H520) were utilized to detect HE4 levels via western blot (WB). Analysis of the TCGA and UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal) databases showed that WFDC2 gene expression levels were upregulated in lung cancer tissues (p < 0.01). Compared with the control group and the benign group, HE4 was significantly higher in the serum of patients with lung cancer (p < 0.001). Receiver operating characteristic (ROC) analysis confirmed that HE4 had better diagnostic efficacy than classical markers in the differential diagnosis of lung cancer and benign lesions and had the highest diagnostic value in lung adenocarcinoma (area under the ROC curve [AUC] = 0.826). HE4 increased in early lung cancer and positively correlated with poor prognosis (p < 0.001). Moreover, the results of WB and IHC revealed that the expression of HE4 was increased in lung cancer cells (SPC/A549/H520) and lung cancer tissues but decreased in PC9 cells with a lack of exon EGFR19 (p < 0.05). Serum HE4 emerges as a promising novel biomarker for the diagnosis and prognosis assessment of lung cancer.

Improvement of differential diagnosis of lung cancer by use of multiple protein tumor marker combinations.

Differential diagnosis of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in hospitalized patients is crucial for appropriate treatment choice. To investigate the relevance of serum tumor markers (STMs) and their combinations for the differentiation of NSCLC and SCLC subtypes. Between 2000 and 2003, 10 established STMs were assessed retrospectively in 311 patients with NSCLC, 128 with SCLC prior systemic first-line therapy and 51 controls with benign lung diseases (BLD), by automatized electrochemiluminescence immunoassay technology. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic efficacy of both individual and multiple STMs with corresponding sensitivities at 90% specificity. Standards for Reporting of Diagnostic Accuracy (STARD guidelines) were followed. CYFRA 21-1 (cytokeratin-19 fragment), CEA (carcinoembryonic antigen) and NSE (neuron specific enolase) were significantly higher in all lung cancers vs BLD, reaching AUCs of 0.81 (95% CI 0.76-0.87), 0.78 (0.73-0.84), and 0.88 (0.84-0.93), respectively. By the three marker combination, the discrimination between benign and all malignant cases was improved resulting in an AUC of 0.93 (95% CI 0.90-0.96). In NSCLC vs. BLD, CYFRA 21-1, CEA and NSE were best discriminative STMs, with AUCs of 0.86 (95% CI 0.81-0.91), 0.80 (0.74-0.85), and 0.85 (0.79-0.91). The three marker combination also improved the AUC: 0.92; 95% CI 0.89-0.96). In SCLC vs. BLD, ProGRP (pro-gastrin-releasing peptide) and NSE were best discriminative STMs, with AUCs of 0.89 (95% CI 0.84-0.94) and 0.96 (0.93-0.98), respectively, and slightly improved AUC of 0.97 (95% CI 0.95-0.99) when in combination. Finally, discrimination between SCLC and NSCLC was possible by ProGRP (AUC 0.86; 95% CI 0.81-0.91), NSE (AUC 0.83; 0.78-0.88) and CYFRA 21-1 (AUC 0.69; 0.64-0.75) and by the combination of the 3 STMs (AUC 0.93; 0.91-0.96), with a sensitivity of 88% at 90% specificity. The results confirm the power of STM combinations for the differential diagnosis of lung cancer from benign lesions and between histological lung cancer subtypes.

Utility of auto fluorescence-guided biopsy in suspected lung cancer patients with bronchial mucosal lesions

BackgroundBronchoscopy is currently the most common technique for lung cancer diagnosis. Patients suspected of malignancy often undergo bronchoscopic examination, and biopsy is routinely used in patients with visible bronchial lesions. However, it is difficult to differentially diagnose lung cancer in patients with bronchial mucosal lesions. Thus, this study was conducted to investigate the utility of fluorescence-guided biopsy in suspected lung cancer patients with bronchial mucosal lesions. MethodsWe conducted a retrospective study in a single screening center to assess the sensitivity and specificity of fluorescence-guided biopsy compared with white light bronchoscopy (WLB) in patients with bronchial mucosal lesions. ResultsA total of 301 patients with bronchial mucosal lesions were enrolled in this study. The sensitivity for patients with fluorescence-guided biopsy was 60.3 % (95 % confidence interval [CI]: 53.1 %–67.1 %), which was higher than that of patients with WLB alone (45.2 %, 95 % CI: 38.2–52.4 %) (P = 0.0026). Additionally, compared with the WLB group, the fluorescence -guided biopsy group was found to have a significantly higher specificity (100 %, 95 % CI: 95.5–100 % versus 69.6 %, 95 % CI: 59.6–78.1 %), positive predictive value (100 %, 95 % CI: 96.1–100 % versus 74.3 %, 95 % CI: 65.5–81.7 %) and negative predictive value (56.3 %, 95 % CI: 48.8–63.6 % versus 39.4 %, 95 % CI: 32.3–47.0 %). ConclusionFluorescence-guided biopsy can serve as an important adjunct to WLB for the differential diagnosis of lung cancer in patients with bronchial mucosal lesions.

Pulmonary candidiasis presenting as tumor-like shadow

Lung fungal infections are rare and carry a high risk of death in critically ill patients. Further investigations including radiological imaging are needed for differential diagnosis. In this article, we present a patient with a tumor-like shadow on chest X-ray and a mildly necrotic soft tissue mass of 4 cm in the first computed tomography (CT) examination of the thorax, who was therefore prediagnosed with Lung Cancer. Due to the poor general condition of the patient, his family did not accept further examinations for cancer. Approximately 40 days later, the patient was taken to the intensive care unit with the complaint of respiratory distress. The bronchial sample was negative for malignant cells and Candida Tropicalis was detected in cultures. The size of the mass decreased in CT controls after treatment with antifungal agents. We thought that the image on CT was due to Candida Tropicalis infection and regressed with antifungal treatment. In conclusion, we can conclude that pulmonary candidiasis may present with atypical radiological findings. Fungal infections should be kept in mind in the differential diagnosis of lung cancer in cases with confusing mass-like images.

A-242 Integrating Respiratory Metagenomics and Metatranscriptomics for Diagnosis of Lung Cancer and Infection in Patients with Pulmonary Diseases

Abstract Background Advances in unbiased metagenomic next generation sequencing (mNGS) technologies have enabled the study of microbial and host genetic material (DNA and RNA) in one test. In this study, we aimed to develop machine learning-based differential diagnostic models (MLBDDMs) using the metagenomic and human transcriptomic data generated by an affordable bronchoalveolar lavage fluid (BLAF) mNGS assay and investigated their clinical utility for early differential diagnosis of lung cancer and pulmonary infection in patients with pulmonary diseases. Methods We recruited 775 patients with respiratory disease, including 160 pathologically diagnosed lung cancer and clinically diagnosed 615 infectious causes (131 tuberculosis, 172 fungal pneumonia and 312 bacterial pneumonia). An affordable mNGS assay on BALF samples collected from these patients on admission were performed. Using the generated mNGS data, we compared the differences in microbial diversity and host gene expression between lung cancer patients and pulmonary infection patients. The BLAF mNGS datasets of lung cancer group and each infection group were then randomly divided into a training dataset and a validation dataset at a ratio of approximately 3:1 for developing optimal MLBDDMs that can be used to distinguish lung cancer from various pulmonary infections. Results By comparing the BALF mNGS data of lung cancer (n = 160) and pulmonary infection (n = 615), we found that the infection group had higher microbial diversity than lung cancer group (P-value &amp;lt; 0.05). Respiratory colonizing microorganisms (e.g., Corynebacterium propinquum and Bacteroides uniformis) and pathogen (Mycobacterium tuberculosis and Cryptococcus neoformans) were found as differential microbes (adjusted p-value &amp;lt; 0.05, LDA score &amp;gt; 2). From BALF gene expression data, we detected 175 genes enriched in NOD-like receptor signaling pathway and chemokine signaling pathway differentially expressed between lung cancer and pulmonary infection groups (False Discovery Rate, FDR &amp;lt; 0.05). Cell composition analysis revealed that macrophage M1 was higher in lung infection group (P-value &amp;lt; 0.001), whereas mast cell activated and DCs activated were higher in lung cancer group (P-value &amp;lt; 0.001, P-value = 0.016). We integrated the metagenomic (microbial composition and human copy number variation) and transcriptomic data (host differentially expressed genes and cell composition) generated by the BALF mNGS assay with eleven machine learning classifiers to establish diagnosis models for distinguishing lung cancer from pulmonary infection (we named LC/PI model). The results showed that a Random Forest diagnostic model (the RF-LC/PI model) had optimal performance, with a sensitivity and specificity of 86.7% and 87.8%, respectively, in distinguishing lung cancer from pulmonary infection (area under the receiver operating characteristic curve [AUC] = 0.838 in the training dataset; AUC = 0.79 in a held-out validation dataset). Similar to the establishment of the LC/PI model, we further developed three diagnostic models for distinguishing lung cancer and tuberculosis (LC/TB model), lung cancer and fungal pneumonia (LC/FP model), and lung cancer and bacterial pneumonia (LC/BP model), respectively. The AUC of these three models were 0.91, 0.88, 0.91, respectively, showing a high differential diagnosis accuracy. Conclusions We have established MLBDDMs using BALF metagenomic and metatranscriptomic data and achieved superior accuracy for differentiating lung cancer and pulmonary infections, which could promote early diagnosis of pulmonary diseases and benefit more patients with one test.

Differential diagnosis of lung cancer and tuberculosis based on 18 F-fluorodeoxyglucose PET/CT multi-time points imaging.

To investigate the value of 18 F-fluorodeoxyglucose(FDG) PET/CT multi-time points imaging (MTPI) on the differential diagnosis between lung cancer (LC) and tuberculosis (TB). Sixty-four patients underwent 18 F-FDG PET/CT MTPI. The stdSUVmax, stdSUVavg, retention index, metabolic tumor volume, total lesion glycolysis at four-time points and slope of metabolic curve were measured and calculated, and the sex, age, and uniformity of FDG uptake were recorded. The difference in each index between LC and TB was analyzed, and dynamic metabolic curves (DMCs) of LC and TB were fitted by significance indexes. Artificial neural network (ANN) prediction models were established between squamous cell carcinoma (SCC) and TB, as well as between adenocarcinomas and TB. Differences between SCC and TB, stdSUVmax/avg at four-time points, total lesion glycolysis, stdSUVmax/avg slope (1-2 h,1-3 h and 1-4 h), uniformity of FDG uptake and age were significant. stdSUVavg has the largest area under the 4 h curve; age was only significant between adenocarcinomas and TB. DMCs at 1-4 h fitted by stdSUVavg were more helpful in differentiating LC and TB than stdSUVmax. stdSUVavg(1 h and 4 h), stdSUVavg slope 1-4 h, age, and uniformity of FDG uptake were selected to establish an ANN prediction model between SCC and TB; the area under the curve (AUC) was 100.0%. The same indices were used to establish the prediction model between adenocarcinomas and TB; the AUC was up to 83.5, and after adding stdSUVavg (2 and 4 h) to adenocarcinomas and TB models, the AUC was 87.7%. 18 F-FDG PET/CT MTPI fitting DMCs and establishing an ANN prediction model would distinguish SCC from TB relatively accurately and provide certain help in the differentiation between adenocarcinomas and TB.

Mesenchymal cystic hamartoma of the lung mimicking simple pulmonary aspergilloma: a case report

BackgroundMesenchymal cystic hamartoma is a rare pulmonary neoplasm, which occasionally presents with severe symptoms such as pneumothorax or hemothorax, and shows favorable outcomes following surgical resection. It presents as solitary or multiple cystic lesions and requires differentiation from cystic malignancies, lymphangiomyomatosis, and cystic adenomatoid malformation.Case presentationWe encountered a 46-year-old woman with mesenchymal cystic hamartoma mimicking simple pulmonary aspergilloma on diagnostic imaging. Chest computed tomography showed a cystic lesion 1.6 cm in diameter, with an intracavitary nodule. Surgical resection proved neither fungus nor malignancy, and the final pathological diagnosis of mesenchymal cystic hamartoma was made.ConclusionsMesenchymal cystic hamartoma might show aspergilloma-like features on diagnostic imaging, in addition to the differential diagnosis of lung cancer.

Value of Brand A Dual-source CT Scanning in the Differential Diagnosis of Lung Cancer

Value of Brand A Dual-source CT Scanning in the Differential Diagnosis of Lung Cancer

Significance of detecting the levels of miR-29a, survivin and interferon gamma release assay in patients with lung cancer and tuberculosis.

When lung cancer is combined with concurrent tuberculosis (TB), it increases the difficulty of diagnosis and treatment, leading to missed and/or misdiagnosed cases. To provide reference markers for the clinical diagnosis of patients with lung cancer complicated by active pulmonary TB (APT). The concentration of survivin in diseased tissue, and miR-29a and IGRAs interferon gamma (IFN-γ) in serum were evaluated in 25 patients with non-small cell lung carcinoma (NSCLC) complicated by APT, 32 patients with NSCLC and 30 patients with APT. The expression of miR-29a in serum of patients with APT was higher than in patients with NSCLC complicated by APT (least significant difference (LSD)-t = 4.724, p < 0.001), and the NSCLC group (LSD-t = 6.619, p < 0.001). Furthermore, patients with NSCLC complicated by APT had higher miR-29a concentration than the NSCLC group. The rate of positive survivin expression in NSCLC (χ2 = 23.418, p < 0.001) and NSCLC combined with APT group (χ2 = 17.160, p < 0.001) was significantly higher than in patients with APT. The concentration of IFN-γ in serum of the NSCLC complicated by APT group (LSD-t = 2.912, p = 0.004) and the APT group (LSD-t = 4.452, p < 0.001) was higher than in the NSCLC group. The level of IFN-γ in serum of the NSCLC complicated by APT group were higher than in the APT group, but there was no statistical difference. The levels of MiR-29a, Survivin and IFN-γ was helpful for differential diagnosis of lung cancer and tuberculosis.

Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study.

BackgroundSaliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non‐invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL).Materials and MethodsUnstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.ResultsThe profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516–0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back‐selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598–0.861, p = 0.003).ConclusionThe four salivary metabolites can serve as potential non‐invasive biomarkers for distinguishing LC from BLL.

The value of combined detection of CEA, CYFRA21-1, SCC-Ag, and pro-GRP in the differential diagnosis of lung cancer.

BackgroundTo investigate the value of carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), squamous cell carcinoma antigen (SCC-Ag), and gastrin-releasing peptide (pro-GRP) in the differential diagnosis of lung cancer.MethodsWe enrolled 120 patients with malignant lung cancer who were treated at our hospital between June 2018 to June 2020. A further 58 patients with benign lung tumors and 60 healthy volunteers were also enrolled. Serum levels of CEA, CYFRA21-1, SCC-Ag, and pro-GRP were determined and compared across different populations, different pathological types, and different TNM stages. An ROC curve was drawn to evaluate the value of the four indicators when combined for the diagnosis of lung cancer.ResultsThe levels of CEA, CYFRA21-1, SCC-Ag, and pro-GRP in the malignant group were significantly higher than those in the benign and healthy groups (P<0.05). CEA in adenocarcinoma was significantly higher than that in squamous cell carcinoma (SCC) and small cell carcinoma (P>0.05), and CYFRA21-1 in non-small cell carcinoma was significantly higher than that in small cell carcinoma (P<0.05). Pro-GRP in small cell carcinoma was significantly higher than that in non-small cell carcinoma (P<0.05), and the SCC-Ag level in SCC was significantly higher than that in small cell carcinoma and adenocarcinoma (P<0.05). There was no statistically significant difference in CEA among various pathological types (P>0.05). However, there were significant differences in the levels of CEA and pro-GRP in different TMN stages (P<0.05), and in the levels of CEA and pro-GRP in different TMN stages (P<0.05) where from stage I to stage IV CEA, pro -GRP levels increased. There was a significant difference in CYFRA21-1 levels in stages I–III (P<0.05), and in stages III and IV, although there was no statistically significant difference in SCC-Ag in different stages (P>0.05). The area under the curve (AUC) for the combined diagnosis of lung cancer with the four markers was 0.9250 (95% CI: 0.8866-0.9634), the sensitivity was 93.29%, and the specificity was 84.32%.ConclusionsJoint inspection of CEA, CYFRA21-1, SCC-Ag, and pro-GRP levels has certain clinical value for the differential diagnosis of lung cancer.

Combination Assessment of Diffusion-Weighted Imaging and T2-Weighted Imaging Is Acceptable for the Differential Diagnosis of Lung Cancer from Benign Pulmonary Nodules and Masses.

Simple SummaryThe purpose of this study is to determine whether the combination assessment of DWI and T2WI improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). As using the OCV (1.470 × 10−3 mm2/s) for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV (2.45) for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.The purpose of this study is to determine whether the combination assessment of DWI and T2-weighted imaging (T2WI) improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). The optimal cut-off value (OCV) for differential diagnosis was set at 1.470 × 10−3 mm2/s for apparent diffusion coefficient (ADC), and at 2.45 for T2 contrast ratio (T2 CR). The ADC (1.24 ± 0.29 × 10−3 mm2/s) of lung cancer was significantly lower than that (1.69 ± 0.58 × 10−3 mm2/s) of BPNM. The T2 CR (2.01 ± 0.52) of lung cancer was significantly lower than that (2.74 ± 1.02) of BPNM. As using the OCV for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Comparison of Differential Diagnosis of Lung Cancer by Diffuse Weighted Imaging and Sagittal Imaging with Short Inversion Recovery Sequence

The differential diagnosis of advanced lung cancer is difficult in clinical practice. Our study aims to compare the value of diffusion weighted imaging (DWI) with short-term inversion recovery sequence (STIR) for sagittal imaging in the differential diagnosis of lung cancer. 149 patients with non-small cell lung carcinoma (NSCLC) were enrolled and underwent DWI and STIR sagittal imaging. To quantify cancer types, we evaluated the apparent diffusion coefficient (ADC) value on DWI and the contrast ratio (CRs) on sagittal imaging. The ADC values of subclasses in NSCLC were significantly higher than small cell lung carcinoma (SCLC) (p &lt;0.01). The mean CRs were 1.59 for SCLC and 1.30 for NSCLC with a significant difference (p &lt; 0.01). Large cell carcinomas (LCC) and adenocarcinomas have significant differences compared to small cell carcinomas (SCC) without difference between squamous cell carcinomas (p &gt; 0.05); this is also the case for CRs. Squamous cell carcinoma and adenocarcinoma have significant differences compared to SCC without difference in LCC (p &gt; 0.05). Qualitative evaluation of the feasible thresholds DWI and STIR showed that the thresholds were 0.9810−3 mm2/s and 1.37 respectively. The specificity and accuracy was 78.5% is 85.3% for DWI, which was significantly higher than STIR (56.3% and 61.0%). The combination of DWI and STIR sequences was superior to DWI alone with an accuracy rate of 94.3%. DWI is more helpful than STIR in differentiating SCLC and NSCLC, and their combined use can significantly improve diagnosis accuracy.

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

AbstractLung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non‐Gaussian Convolutional Neural Network (KNG‐CNN). KNG‐CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non‐Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC‐IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG‐CNN method is used for effectual classification of tumour &gt; 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.

Spectral Computed Tomography Imaging in the Differential Diagnosis of Lung Cancer and Inflammatory Myofibroblastic Tumor.

The aim of this study was to explore the value of spectral computed tomography (CT) imaging in differentiating lung cancer from inflammatory myofibroblastic tumor (IMT). One hundred twelve patients with 96 lung cancers and 16 IMTs underwent spectral CT during arterial phase (AP) and venous phase (VP). The normalized iodine concentration in AP (NICAP) and VP (NICVP), slope of spectral Hounsfield unit curve in AP (λAP) and VP (λVP), and normalized iodine concentration difference between AP and VP (ICD) were calculated. The 2-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Receiver operating characteristic curves were generated to calculate sensitivity and specificity. Sensitivity and specificity of the qualitative and quantitative studies were compared. The patients with IMT had significantly higher NICAP, NICVP, λAP, λVP, and ICD than did the patients with lung cancer (P < 0.05). The threshold NICVP of 0.425 would yield the highest sensitivity and specificity of 92.7% and 81.3%, respectively, for differentiating lung cancer from IMT. The logistic regression model produced from combining quantitative parameters NICAP, NICVP, λAP, and λVP provided a sensitivity and specificity of 100% and 81.3%, respectively, for differentiating lung cancer from IMT. Spectral CT imaging with the quantitative analysis may help to increase the accuracy of differentiating lung cancer from IMT.

The level of sialic acids and imidazole compounds in the saliva of patients with lung cancer of different histological types

In recent years, the possibility of using known and new tumor markers in primary and differential diagnosis of lung cancer has been actively studied.The purpose of the studywas to study the level of sialic acids, the total content of imidazole compounds and seromucoids in the saliva of patients with lung cancer, depending on the histological type of tumor.Material and Methods. Total of 478 people took part in in the case-control study. They were divided into 3 groups: the main group (lung cancer, n=218), the comparison group (nonmalignant lung pathologies, n=60) and the control group (conditionally healthy, n=200).Results.Patients with non-malignant lung pathologies exhibited increased levels of imidazole compounds and seromucoids and decreased levels of sialic acids. A statistically significant decrease in the level of sialic acids was observed in patients with non-small cell lung cancer. The concentration of imidazole compounds was significantly higher in all study groups, except for carcinoid tumors. The nature of the changes in the studied parameters were ambiguous and depended on both the histological type of the tumor and the stage of the disease, including the presence / absence of distant and regional metastasis.

Differential diagnosis of lung cancer and tuberculosis: review

The neglect of lung cancer and the unsatisfactory results of its treatment depend to a large extent on untimely diagnosis in the early stages of the disease. The reason for this is the absence of pathognomonic clinical signs and a specific radiographic picture of early lung cancer, which has to be differentiated from other respiratory diseases, including those occurring with infiltrative changes. According to statistical data, hyperdiagnosis of infiltrative pulmonary tuberculosis in patients with oncological diseases of the respiratory system was from 14 to 39%, which leads to long-term monitoring of patients with lung cancer in phthisiological institutions with anti-tuberculosis therapy, which causes delays in the treatment of patients with lung cancer. The pronounced visual similarity of various pathological processes of the lungs, the mixed incidence and immunodeficiency combined with subjective factors, such as gaps in the training of doctors, omissions in the survey method and the lack of continuity, can collectively be the cause of differential diagnostic errors. This article describes the similarities and differences between these diseases, the main causes of erroneous diagnosis, as well as the prospects for the development of modern diagnostic methods.

Sialic Acids of Saliva in Primary and Differential Diagnosis of Lung Cancer

Sialic Acids of Saliva in Primary and Differential Diagnosis of Lung Cancer

Methylation of L1RE1, RARB, and RASSF1 function as possible biomarkers for the differential diagnosis of lung cancer.

BackgroundLung cancer is the major cause of cancer-related deaths worldwide. Differential diagnosis can be difficult, especially when only small samples are available. Epigenetic changes are frequently tissue-specific events in carcinogenesis and hence may serve as diagnostic biomarkers.Material and methods138 representative formalin-fixed, paraffin-embedded (FFPE) tissues (116 lung cancer cases and 22 benign controls) were used for targeted DNA methylation analysis via pyrosequencing of ten literature-derived methylation markers (APC, CDH1, CDKN2A, EFEMP1, FHIT, L1RE1, MGMT, PTEN, RARB, and RASSF1). Methylation levels were analyzed with the Classification and Regression Tree Algorithm (CART), Conditional Interference Trees (ctree) and ROC. Validation was performed with additional 27 lung cancer cases and 38 benign controls. TCGA data for 282 lung cancer cases was included in the analysis.ResultsCART and ctree analysis identified the combination of L1RE1 and RARB as well as L1RE1 and RASSF1 as independent methylation markers with high discriminative power between tumor and benign tissue (for each combination, 91% specificity and 100% sensitivity). L1RE1 methylation associated significantly with tumor type and grade (p<0.001) with highest methylation in the control group. The opposite was found for RARB (p<0.001). RASSF1 methylation increased with tumor type and grade (p<0.001) with strongest methylation in neuroendocrine tumors (NET).ConclusionHypomethylation of L1RE1 is frequent in tumors compared to benign controls and associates with higher grade, whereas increasing methylation of RARB is an independent marker for tumors and higher grade. RASSF1 hypermethylation was frequent in tumors and most prominent in NET making it an auxiliary marker for separation of NSCLC and NET. L1RE1 in combination with either RARB or RASSF1 could function as biomarkers for separating lung cancer and non-cancerous tissue and could be useful for samples of limited size such as biopsies.