The diagnostic role of plasma circulating precursors of miRNA-944 and miRNA-3662 for non-small cell lung cancer detection

Abstract Objective: Recent studies indicated sputum miRNAs may provide a promising approach for non-small cell lung cancer (NSCLC) diagnosis. But some results were still inconsistent. So, we performed meta-analysis to evaluate the diagnostic role of sputum miRNAs for the detection of NSCLC.Methods: Eligible studies that estimated the diagnostic accuracy of sputum miRNAs in NSCLC were searched in Pubmed, Embase and Web of Science and Chinese National Knowledge Infrastructure (CNKI). Data from the eligible studies were collected and pooled; sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios, weighted symmetric summary ROC curve and the area under the curve (AUC) were calculated by bi-variate random effects model. The between-study heterogeneity was evaluated by Q test and I2 statistics.Results: 30 studies from 16 articles were included for analysis. The overall analysis yielded the sensitivity of 0.77 (95% CI: 0.73–0.81) and specificity of 0.87 (95% CI: 0.83–0.90), with an area under the SROC curve (AUC) of 0.89 (95% CI: 0.86–0.91). Subgroup analysis revealed the diagnostic accuracy in multiple miRNAs studies was higher than single miRNA (the sensitivity, specifcity and an AUC of multiple miRNAs were 0.76, 0.88 and 0.90; and for single miRNA, it was 0.74, 0.74, and 0.80). The diagnostic performance in early stage NSCLC was also very high (the sensitivity, specifcity and an AUC of stage I/II was 0.76, 0.88 and 0.91; and for stage I, it was 0.79, 0.85, and 0.87). We also found miR-210, miR-21, miR-31 and miR-126-3p might serve as potential biomarkers for lung cancer.Conclusion: Sputum miRNAs was useful noninvasive biomarkers for NSCLC diagnosis.

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Diagnostic role of circulating extracellular matrix-related proteins in non-small cell lung cancer

BMC Cancer ◽

10.1186/s12885-018-4772-0 ◽

2018 ◽

Vol 18 (1) ◽

Cited By ~ 12

Author(s):

Francesca Andriani ◽

Elena Landoni ◽

Mavis Mensah ◽

Federica Facchinetti ◽

Rosalba Miceli ◽

...

Keyword(s):

Lung Cancer ◽

Extracellular Matrix ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Diagnostic Role ◽

Related Proteins

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Micrometastasis in non-small-cell lung cancer: Detection and staging

Annals of Thoracic Medicine ◽

10.4103/1817-1737.98848 ◽

2012 ◽

Vol 7 (3) ◽

pp. 149 ◽

Cited By ~ 4

Author(s):

Mohsen Kolahdouzan ◽

SayyedMozaffar Hashemi ◽

Elham Amjad ◽

Gholamreza Mohajeri ◽

MohammadHossein Sanei ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cancer Detection ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Lung Cancer Detection

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A Survey on Multistage lung cancer Detection and Classification

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit20631110 ◽

2020 ◽

pp. 313-320

Author(s):

Jay Jawarkar ◽

Nishit Solanki ◽

Meet Vaishnav ◽

Harsh Vichare ◽

Sheshang Degadwala

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cancer Detection ◽

Cell Lung Cancer ◽

Small Cell ◽

The Body ◽

Future Application ◽

Small Cell Lung ◽

Lung Cancer Detection

Earlier, Lung cancer is the primary cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Lung Cancer have been widest difficulty faced by humans over recent couple of decades. When a person has lung cancer, they have abnormal cells that cluster together to form a tumor. A cancerous tumor is a group of cancer cells that can grow into and destroy nearby tissue. It can also spread to other parts of the body. There are two main types of lung cancer:1. Non-small cell lung cancer, 2. Small cell lung cancer. Non- small cell lung cancer has four main stages. In this research we are classifying four stages of lung cancer. Lung cancer detection at early stage has become very important. Currently many techniques are used based on image processing and deep learning techniques for lung cancer classification. For that lung patient Computer Tomography (CT) scan images are used to detect and lung nodules and classify lung cancer stage of that nodules. In this re- search we compare different Machine learning (SVM, KNN, RF etc.) techniques with deep learning (CNN, CDNN) techniques using different parameters accuracy, precision and recall. In this Research paper we com- pare all existing approach and find our better result for future application.

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The promises and challenges of early non‐small cell lung cancer detection: patient perceptions, low‐dose CT screening, bronchoscopy and biomarkers

Molecular Oncology ◽

10.1002/1878-0261.12864 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lukas Kalinke ◽

Ricky Thakrar ◽

Sam M. Janes

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cancer Detection ◽

Cell Lung Cancer ◽

Low Dose ◽

Small Cell ◽

Patient Perceptions ◽

Small Cell Lung ◽

Low Dose Ct ◽

Lung Cancer Detection

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Early detection of lung cancer using cfDNA fragmentation.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.8519 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 8519-8519

Author(s):

Dimitrios Mathios ◽

Jakob Sidenius Johansen ◽

Stephen Cristiano ◽

Jamie Medina ◽

Jillian Phallen ◽

...

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Small Cell Lung Cancer ◽

Cancer Detection ◽

Cell Lung Cancer ◽

Late Stage ◽

Small Cell ◽

Small Cell Lung ◽

Genome Wide ◽

Lung Cancer Detection

8519 Background: Lung cancer incidence and mortality are increasing worldwide despite more effective treatments. This is primarily due to the late stage of diagnosis when treatments are less effective. Although large randomized trials have demonstrated a significant decrease in lung cancer mortality through screening of high-risk individuals with chest low dose computed tomography (LDCT), LDCT has made little impact in the community, mainly due to lack of accessibility. There is therefore an unmet clinical need for development of cost-effective and easily implemented tests for early lung cancer detection. Methods: We have previously shown that altered genome-wide fragmentation of cell free DNA (cfDNA) is a common characteristic of many cancers. In this study, we leverage this knowledge to increase the sensitivity of lung cancer detection by interrogating characteristics of the size distribution of cfDNA fragments across the genome using machine learning methods. The approach we present, called DELFI (DNA evaluation of fragments for early interception) generates a score that reflects the presence of tumor-derived DNA in plasma based on a multi-feature genomic analysis that assesses millions of cfDNA fragments for tumor-derived genomic and epigenomic changes in a small amount of blood (2-4 mls) via inexpensive low coverage (1-2x) whole genome sequencing. We applied this methodology in a prospectively collected cohort of 365 individuals under investigation for lung cancer and we prospectively validated it in a separate case-control cohort of patients with newly diagnosed early stage lung cancer as well as individuals without cancer (n=427). Results: These analyses revealed high performance for detection of early and late stage disease (Table). When DELFI was used as a prescreen for LDCT it increased specificity from 58% with CT imaging alone to 80% using the combined approach. The DELFI score was significantly associated with T and N stage in lung cancer cases (p<0.0001) as well as with overall survival (p=0.003). In a multivariable analysis including age, histology and stage, DELFI score was an independent prognostic factor of overall survival (HR=2.53; p=0.0003). Finally, we determined that genome-wide fragmentation profiles can be used to distinguish small cell lung cancer from non-small cell lung cancer with high accuracy (AUC 0.98). Conclusions: These findings provide key insights into cfDNA fragmentation in patients with cancer and a new and easily accessible avenue for non-invasive diagnosis and molecular profiling of lung cancer.[Table: see text]

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