Expression of early lung cancer detection marker: hnRNP-A2/B1 and its relation to microsatellite alteration in non-small cell lung cancer

Lung Cancer ◽  
2001 ◽  
Vol 34 (3) ◽  
pp. 341-350 ◽  
Author(s):  
Jun Zhou ◽  
Liang Nong ◽  
Marek Wloch ◽  
Alan Cantor ◽  
James L Mulshine ◽  
...  
2012 ◽  
Vol 7 (3) ◽  
pp. 149 ◽  
Author(s):  
Mohsen Kolahdouzan ◽  
SayyedMozaffar Hashemi ◽  
Elham Amjad ◽  
Gholamreza Mohajeri ◽  
MohammadHossein Sanei ◽  
...  

Author(s):  
Jay Jawarkar ◽  
Nishit Solanki ◽  
Meet Vaishnav ◽  
Harsh Vichare ◽  
Sheshang Degadwala

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.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8519-8519
Author(s):  
Dimitrios Mathios ◽  
Jakob Sidenius Johansen ◽  
Stephen Cristiano ◽  
Jamie Medina ◽  
Jillian Phallen ◽  
...  

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]


2017 ◽  
Vol 213 (11) ◽  
pp. 1384-1387 ◽  
Author(s):  
Tomasz Powrózek ◽  
Barbara Kuźnar-Kamińska ◽  
Marcin Dziedzic ◽  
Radosław Mlak ◽  
Halina Batura-Gabryel ◽  
...  

Lung Cancer ◽  
2016 ◽  
Vol 100 ◽  
pp. 71-76 ◽  
Author(s):  
Agnieszka Klupczynska ◽  
Paweł Dereziński ◽  
Wojciech Dyszkiewicz ◽  
Krystian Pawlak ◽  
Mariusz Kasprzyk ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dimitrios Mathios ◽  
Jakob Sidenius Johansen ◽  
Stephen Cristiano ◽  
Jamie E. Medina ◽  
Jillian Phallen ◽  
...  

AbstractNon-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.


Sign in / Sign up

Export Citation Format

Share Document