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Risk Stratification for Lung Cancer Screening Using an Epidemiologic, Molecular, and Quantitative Imaging Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Risk Stratification for Lung Cancer Screening Using an Epidemiologic, Molecular, and Quantitative Imaging Approach./
作者:	Warkentin, Matthew T.
面頁冊數:	1 online resource (344 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068457click for full text (PQDT)
ISBN:	9798357545763

Risk Stratification for Lung Cancer Screening Using an Epidemiologic, Molecular, and Quantitative Imaging Approach.
Warkentin, Matthew T.

Risk Stratification for Lung Cancer Screening Using an Epidemiologic, Molecular, and Quantitative Imaging Approach. - 1 online resource (344 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2022.

Includes bibliographical references

Lung cancer is the most common cancer in the world and leading cause of cancer mortality globally. This dissertation investigates three complementary aims related to the development of lung cancer and lung cancer screening using data from the UK Biobank, China Kadoorie Biobank, National Lung Screening Trial, International Early Lung Cancer Action Program, Pittsburgh Lung Screening Study, and PanCanadian Early Detection of Lung Cancer Study.Regular screening with computed tomography has demonstrated promising reductions in lung cancer mortality. However, questions remain about the optimal approach to identify high-risk individuals most likely to benefit from screening. Widely-used enrollment criteria exclude light or never-smokers due to an absence of extensive smoking history, despite making up a meaningful proportion of lung cancers. Most findings in lung cancer screening programs are among individuals with ultimately benign determinations. The clinical management of indeterminate screen-detected pulmonary nodules remains an important barrier to optimal screening program efficiency.The lung function analysis in Manuscript 1 identifies important lifecourse exposures that shape the trajectory of pulmonary health for never-smokers and advances our understanding of risk factors for impaired lung function in the absence of primary smoking history. Manuscripts 1 and 2 are based on two of the largest population-based cohorts ever assembled. Methodological contributions of this work includes the development of risk-prediction models for never and ever-smokers, including one of the first models developed specifically in an Asian population, which may help to identify high-risk individuals who would benefit from early detection initiatives. Manuscript 3 is based on data from four international lung cancer screening programs. We performed quantitative image analysis to extract radiological features from medical images to accurately assess indeterminate pulmonary nodules. Contributions of this work include the development of highly-accurate nodule malignancy assessment models based on robust and high-throughput radiomic features, validated across multiple independent screening studies.In summary, this dissertation provides insights into how lifecourse exposures effect pulmonary function among never-smokers, how statistical models can help prioritize high-risk never and ever-smokers for lung cancer screening, and how nodule assessment models based on quantitative image features can improve clinical decision-making and management of screen-detected pulmonary nodules.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357545763Subjects--Topical Terms:

568544
Epidemiology.
Subjects--Index Terms:

BiostatisticsIndex Terms--Genre/Form:

542853
Electronic books.

Risk Stratification for Lung Cancer Screening Using an Epidemiologic, Molecular, and Quantitative Imaging Approach.
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520 $a Lung cancer is the most common cancer in the world and leading cause of cancer mortality globally. This dissertation investigates three complementary aims related to the development of lung cancer and lung cancer screening using data from the UK Biobank, China Kadoorie Biobank, National Lung Screening Trial, International Early Lung Cancer Action Program, Pittsburgh Lung Screening Study, and PanCanadian Early Detection of Lung Cancer Study.Regular screening with computed tomography has demonstrated promising reductions in lung cancer mortality. However, questions remain about the optimal approach to identify high-risk individuals most likely to benefit from screening. Widely-used enrollment criteria exclude light or never-smokers due to an absence of extensive smoking history, despite making up a meaningful proportion of lung cancers. Most findings in lung cancer screening programs are among individuals with ultimately benign determinations. The clinical management of indeterminate screen-detected pulmonary nodules remains an important barrier to optimal screening program efficiency.The lung function analysis in Manuscript 1 identifies important lifecourse exposures that shape the trajectory of pulmonary health for never-smokers and advances our understanding of risk factors for impaired lung function in the absence of primary smoking history. Manuscripts 1 and 2 are based on two of the largest population-based cohorts ever assembled. Methodological contributions of this work includes the development of risk-prediction models for never and ever-smokers, including one of the first models developed specifically in an Asian population, which may help to identify high-risk individuals who would benefit from early detection initiatives. Manuscript 3 is based on data from four international lung cancer screening programs. We performed quantitative image analysis to extract radiological features from medical images to accurately assess indeterminate pulmonary nodules. Contributions of this work include the development of highly-accurate nodule malignancy assessment models based on robust and high-throughput radiomic features, validated across multiple independent screening studies.In summary, this dissertation provides insights into how lifecourse exposures effect pulmonary function among never-smokers, how statistical models can help prioritize high-risk never and ever-smokers for lung cancer screening, and how nodule assessment models based on quantitative image features can improve clinical decision-making and management of screen-detected pulmonary nodules.
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