scholarly journals Small non-coding RNA biomarkers in sputum for lung cancer diagnosis

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Yun Su ◽  
Maria A. Guarnera ◽  
HongBin Fang ◽  
Feng Jiang
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Non Coding Rna ◽  
Rna Biomarkers

scholarly journals microRNA-based diagnostic and therapeutic opportunities in lung cancer

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Jianfei Zhao ◽  
Yan Cai ◽  
Xi Liu
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Expression Profiles ◽  
Pathological Process ◽  
Prognostic Information ◽  
Future Directions ◽  
Lung Cancer Diagnosis ◽  
Non Coding Rna ◽  
Mirna Expression Profiles ◽  
Cancer Diagnosis And Therapy

AbstractmicroRNAs (miRNAs) are a class of non-coding RNA which suppress target gene expression. miRNAs are involved in most physiological and pathological process, including carcinogenesis. miRNA expression profiles help to improve lung cancer diagnosis, classification and prognostic information. Tumor suppressive and oncogenic miRNAs have been discovered and their functions have been investigated. Emphasis is placed on the development of miRNA-based methods for lung cancer diagnosis and therapy and future directions are proposed.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Biased Lung Cancer Risk Perceptions: Smokers are Misinformed

2018 ◽  
Vol 238 (5) ◽  
pp. 395-421 ◽  
Author(s):  
Nicolas R. Ziebarth
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Cancer Diagnosis ◽  
Mortality Risk ◽  
Risk Perceptions ◽  
Lung Cancer Risk ◽  
Lifetime Risk ◽  
Lung Cancer Diagnosis ◽  
Cancer Risk Perceptions ◽  
Overall Mortality

Abstract This paper empirically investigates biased beliefs about the risks of smoking. First, it confirms the established tendency of people to overestimate the lifetime risk of a smoker to contract lung cancer. In this paper’s survey, almost half of all respondents overestimate this risk. However, 80% underestimate lung cancer deadliness. In reality, less than one in five patients survive five years after a lung cancer diagnosis. Due to the broad underestimation of the lung cancer deadliness, the lifetime risk of a smoker to die of lung cancer is underestimated by almost half of all respondents. Smokers who do not plan to quit are significantly more likely to underestimate this overall mortality risk.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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