Date of Award

January 2019

Document Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Krystal Pollitt

Second Advisor

Vasilis Vasiliou

Abstract

Lung cancer has become the most commonly diagnosed cancer and the leading cause of cancer deaths globally. The major problem of the high mortality rate is the late diagnosis. Conventional methods utilized for clinical detection of lung cancer have employed expensive and invasive medical procedures that cause stress, discomfort, and pain to patients, and have demonstrated low sensitivity, substantial false negatives, and risk of radiation exposure. The drawbacks obviate their applicability to large-scale, population-wide screening efforts. This paper reviews the applications of using volatile organic compounds (VOCs) in exhaled breath as a potential approach for early lung cancer detection.

An electronic search was conducted in PubMed and Scopus. A total of 41 studies were included in this review. The sampling method of exhaled breath employed in most of the included studies were leak-proof Tedlar bags. Mass spectrometry and electronic noses were two main techniques used in breath sample detection. In the recent years, electronic noses gained more popularity due to their portability and cost-effectiveness. In this review, a total of 40 VOCs, originated from both endogenous and exogenous sources, were found to be significant in discriminating between lung cancer patients and healthy controls in two or more of the included studies. The included studies demonstrated substantial sensitivity, specificity, and accuracy of the method. Overall, the results showed that VOCs in exhaled breath is a promising biomarker for early detection of lung cancer. However, the large-scale practice of this method is constrained by the lack of standardized breath collection and analysis system and putative exhaled VOC biomarkers. Further studies with consistent sampling protocols should be used to demonstrate the reproducibility and repeatability of the detection tool before they are applied in clinical practice.

Comments

This thesis is restricted to Yale network users only. It will be made publicly available on 08/28/2021

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