102 Sharing Bad News: Understanding the communication processes of a lung cancer diagnosis

Lung Cancer ◽  
2014 ◽  
Vol 83 ◽  
pp. S38
Author(s):  
N.B. Ngwenya ◽  
M. Farquhar ◽  
J. Benson ◽  
D. Gilligan ◽  
S. Bailey ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Bad News ◽  
Lung Cancer Diagnosis ◽  
Communication Processes

scholarly journals Considering Patients’ Mental Capacity When Giving Them Bad News May Help Their Well-Being: A Case of Suicide Attempt after Being Informed of Lung Cancer Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-4
Author(s):  
Toshiyuki Kobayashi ◽  
Satoshi Kato ◽  
Mitsuo Takeuchi
Keyword(s):  
Lung Cancer ◽  
Suicide Attempt ◽  
Cancer Diagnosis ◽  
Attempted Suicide ◽  
Well Being ◽  
Mental Capacity ◽  
Bad News ◽  
Lung Cancer Diagnosis ◽  
Diagnosis Of Lung Cancer ◽  
Mental Well Being

Mental capacity is a central determinant of patients’ ability to make autonomous decisions about their care and deal with bad news. Physicians should be cognizant of this when giving patients bad news in efforts to help them to cope with the illness and to avoid a deterioration of their mental well-being. To show the importance of this concept, a case of suicide attempt with lung cancer is exemplified. A 76-year-old woman attempted suicide after receiving a diagnosis of lung cancer. Her recent life had been emotionally turbulent and she did not have sufficient mental capacity to accept and cope with this truth. She developed depression before attempting suicide.

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.

The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis

2021 ◽  
Author(s):  
Zhang-Yan Ke ◽  
Ya-Jing Ning ◽  
Zi-Feng Jiang ◽  
Ying-ying Zhu ◽  
Jia Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Raman Spectroscopy ◽  
Cancer Diagnosis ◽  
Meta Analysis ◽  
Lung Cancer Diagnosis
Sign in / Sign up

Export Citation Format

Share Document