External validation of a panel of plasma microRNA biomarkers for lung cancer

2019 ◽  
Vol 13 (18) ◽  
pp. 1557-1564 ◽  
Author(s):  
Jin Li ◽  
HongBin Fang ◽  
Fang Jiang ◽  
Yang Ning
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
External Validation ◽  
Early Stage Lung Cancer ◽  
Combined Use ◽  
Diagnostic Values ◽  
Plasma Microrna ◽  
Diagnosis Of Lung Cancer ◽  
Lung Cancer Detection ◽  
Plasma Mirnas

Aim: We externally validate plasma miRNAs biomarkers for lung cancer in a large and retrospective sample set collected from a geographically distant population. Methods: Plasma samples are tested blindly to the clinical annotations by using PCR for quantitation of the four miRNAs in cohort 1 consisting of 232 lung cancer cases and 243 controls and cohort 2 comprising 239 cases and 246 controls. Results: Combined use of the four plasma miRNAs has 91% sensitivity and 95% specificity for diagnosis of lung cancer, and 85% sensitivity for early-stage lung cancer, while maintaining a specificity of 95%. Conclusion: The diagnostic values of the biomarkers are reproducibly confirmed in the independent and large sample sets, providing an assay for lung cancer detection.

scholarly journals Classification of Lung Nodules using Improved Residual Convolutional Neural Network

2020 ◽  
Vol 1 (1) ◽  
pp. 15-21
Author(s):  
Salah Eldeen Babiker ◽  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Early Stage ◽  
Lung Nodule ◽  
Early Stage Lung Cancer ◽  
Evolutionary Neural Network ◽  
Diagnosis Of Lung Cancer ◽  
Experimental Findings ◽  
Lung Cancer Detection

The most common cancer of the lung cannot be ignored and can cause late-health death. Now CT can be used to help clinicians diagnose early-stage lung cancer. In certain cases the diagnosis of lung cancer detection is based on doctors' intuition, which can neglect other patients and cause complications. Deep learning in most other areas of medical diagnosis has proven to be a common and powerful tool. This research is planned for improving the residual evolutionary neural network (IRCNN). These networks apply with some changes to the benign and malignant lung nodule to the CT image classification task. The segmenting of the nodule is performed here by clustering k-means. The LIDC-IDRI database analysed those networks. Experimental findings show that the IRCNN network archived the best performance of lung nodule classification, which findings best among established methods.

Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-stage Lung Cancer Diagnosis

2020 ◽  
Author(s):  
Lingling Wan ◽  
Yutong He ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Diagnosis ◽  
Cancer Patients ◽  
Early Stage ◽  
Untargeted Metabolomics ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Diagnosis Of Lung Cancer ◽  
Metabolomics Analysis ◽  
Stage Lung Cancer

Abstract Background: Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis. Methods: In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients, and the early diagnostic value of these assays in lung cancer was analyzed. Results: 62.5% (30/48) of lung cancer patients had ≥ 1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC > 0.9). Conclusions: Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer. This possibility requires more in-depth large-sample research for verification.

Abstract 2607: Promotion of low-dose computed tomography for early-stage lung cancer detection

2021 ◽  
Author(s):  
Monica Saravana Vela ◽  
Joseph Berei ◽  
Katrina Dovalovsky ◽  
Shylendra Sreenivasappa ◽  
Joseph Ross ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Detection ◽  
Low Dose ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Low Dose Computed Tomography ◽  
Lung Cancer Detection ◽  
Stage Lung Cancer

scholarly journals Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-Stage Lung Cancer Diagnosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Lingling Wan ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
Guangjie Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Diagnosis ◽  
Cancer Patients ◽  
Early Stage ◽  
Untargeted Metabolomics ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Diagnosis Of Lung Cancer ◽  
Metabolomics Analysis ◽  
Stage Lung Cancer

BackgroundLung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis.MethodsIn this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients. To screen potential diagnostic markers for early lung cancer.Results62.5% (30/48) of lung cancer patients had ≥1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC >0.9).ConclusionsOur results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer.

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