scholarly journals Back Cover: Development and in vivo test of a miniature Raman probe for early cancer detection in the peripheral lung (J. Biophotonics 11/2018)

2018 ◽  
Vol 11 (11) ◽  
pp. e201870169
Author(s):  
Hanna C. McGregor ◽  
Michael A. Short ◽  
Stephen Lam ◽  
Tawimas Shaipanich ◽  
Eve-Léa Beaudoin ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
In Vivo Test ◽  
Back Cover ◽  
Peripheral Lung ◽  
Raman Probe

Development and in vivo test of a miniature Raman probe for early cancer detection in the peripheral lung

2018 ◽  
Vol 11 (11) ◽  
pp. e201800055 ◽  
Author(s):  
Hanna C. McGregor ◽  
Michael A. Short ◽  
Stephen Lam ◽  
Tawimas Shaipanich ◽  
Eve-Léa Beaudoin ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
In Vivo Test ◽  
Peripheral Lung ◽  
Raman Probe

Real-time In Vivo Tissue Raman Spectroscopy for Early Cancer Detection

2016 ◽  
Author(s):  
Haishan Zeng ◽  
Jianhua Zhao ◽  
Michael A. Short ◽  
David I. McLean ◽  
Stephen Lam ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Real Time ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection

scholarly journals Small-area data on socioeconomic status and immigrant groups for evaluating equity of early cancer detection and care

2021 ◽  
pp. 1-6
Author(s):  
Ulf Strömberg ◽  
Brandon L. Parkes ◽  
Amir Baigi ◽  
Carl Bonander ◽  
Anders Holmén ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cancer Detection ◽  
Small Area ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Area Data

Dying To Be Men: Masculinity and Early Cancer Detection Among Nigerian Men

2021 ◽  
pp. 0272684X2110049
Author(s):  
Darlingtina Esiaka ◽  
Candidus Nwakasi ◽  
Kelsey Brodie ◽  
Aaron Philip ◽  
Kalu Ogba
Keyword(s):  
Public Health ◽  
Cancer Detection ◽  
Attachment Styles ◽  
Early Cancer ◽  
Sub Saharan Africa ◽  
Early Cancer Detection ◽  
Anxious Attachment ◽  
Incidence And Mortality ◽  
Nigerian Men ◽  
Sub Saharan

Cancer incidence and mortality in Nigeria are increasing at an alarming rate, especially among Nigerian men. Despite the numerous public health campaigns and education on the importance of early cancer detection in Nigeria, there exist high rate of fatal/advanced stage cancer diagnoses among Nigerian men, even among affluent Nigerian men. However, there is limited information on patterns of cancer screening and psychosocial predictors of early cancer detection behaviors among Nigerian men. In this cross-sectional study, we examined demographic and psychosocial factors influencing early cancer detection behaviors among Nigerian men. Participants (N = 143; Mage = 44.73) responded to survey assessing: masculinity, attachment styles, current and future cancer detection behaviors, and sociodemographic characteristics. We found that among the participants studied, education, masculinity and anxious attachment were significantly associated with current cancer detection behaviors. Additionally, education and anxious attachment were significantly associated with future cancer detection behaviors. Our finding is best served for clinicians and public health professionals, especially those in the field of oncology in Sub-Saharan Africa. Also, the study may be used as a groundwork for future research and health intervention programs targeting men in Sub-Saharan Africa.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

2021 ◽  
Author(s):  
Lin Huang ◽  
Kun Qian
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cancer Detection ◽  
High Performance ◽  
Large Scale ◽  
Early Cancer ◽  
Early Stage ◽  
Early Cancer Detection ◽  
Ionization Mass ◽  
Efficient Test

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

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