자료유형  

 학위논문

Control Number  

0016931397

International Standard Book Number  

9798379964917

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Nene, Aishwarya K.

Publication, Distribution, etc. (Imprint  

[S.l.] : Yale University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(49 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

General Note  

Advisor: Aneja, Sanjay;Krumholz, Harlan.

Dissertation Note  

Thesis (M.D.)--Yale University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Cancer patients are a unique population with increased mortality from cardiovascular disease, however only half of high-risk patients are medically optimized. Physicians ascertain cardiovascular risk from several risk predictors using demographic information, family history, and imaging data. The Agatston score, a measure of total calcium burden in coronary arteries on CT scans, is the current best predictor for major adverse cardiac events (MACE). Yet, the score is limited as it does not provide information on atherosclerotic plaque characteristics or distribution. In this study, we use deep learning techniques to develop an imaging-based biomarker that can robustly predict MACE in lung cancer patients. We selected participants with screen-detected lung cancer from the National Lung Screening Trial (NLST) and used cardiovascular mortality as our primary outcome. We applied automated segmentation algorithms to low-dose chest CT scans from NLST participants to segment cardiac substructures. Following segmentation, we extracted radiomic features from selected cardiac structures. We then used this dataset to train a regression model to predict cardiovascular death. We used a pre-trained nnU-Net model to successfully segment large cardiac structures on CT scans. These automated large cardiac structures had features that were predictive of MACE. We then successfully extract radiomic features from our areas of interest and use this high-dimensional dataset to train a regression model to predict MACE. We demonstrated that automated segmentation algorithms can result in low-cost non-invasive predictive biomarkers for MACE. We were able to demonstrate that radiomic feature extraction from segmented substructures can be used to develop a high-dimensional biomarker. We hope that such a scoring system can help physicians adequately determine cardiovascular risk and intervene, resulting in better patient outcomes.

Subject Added Entry-Topical Term  

Medicine.

Subject Added Entry-Topical Term  

Oncology.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Medical imaging.

Index Term-Uncontrolled  

Biomarkers

Index Term-Uncontrolled  

Cancer

Index Term-Uncontrolled  

Cardiology

Index Term-Uncontrolled  

Deep learning

Index Term-Uncontrolled  

Radiomics

Added Entry-Corporate Name  

Yale University Yale School of Medicine

Host Item Entry  

Dissertations Abstracts International. 85-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642171