자료유형  

 학위논문

Control Number  

0016931684

International Standard Book Number  

9798379707651

Dewey Decimal Classification Number  

616

Main Entry-Personal Name  

Douglas, Lindsay Nicole.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of Chicago., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(121 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Giger, Maryellen.

Dissertation Note  

Thesis (Ph.D.)--The University of Chicago, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약To enhance breast cancer screening practices, artificial intelligence (AI) systems have been developed to aid radiologists in a variety of tasks. Machine learning (ML) techniques for computer-aided diagnosis are based on human-engineered or deep learning methods, and they depend on accurate segmentation for useful feature extraction. As the use of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) has increased in breast imaging, particularly for high-risk screening, the potential for AI to provide significant clinical benefit has grown. There is need for a deeper understanding of how breast MRI can be used for diagnosis and risk assessment in order to develop robust, generalizable AI systems for quantifying clinically valuable breast characteristics.This dissertation presents novel methods for computerized assessment of background parenchymal enhancement (BPE), a known risk factor for breast cancer, from breast DCE-MRI. In Chapter 1, we introduce the background of breast cancer screening with a focus on AI applications to motivate the subsequent chapters. In Chapter 2, we investigate segmentation techniques for lesions and breast regions. In Chapter 3, we develop an ML technique for computer BPE scoring that includes electronic lesion removal. In Chapter 4, we perform an independent evaluation of the BPE scoring algorithm applied to high-risk patients. Ultimately, the results of this work have the potential to encourage future incorporation of quantitative image analysis into the clinical workflow for radiologists and therefore improve patient care.Segmentation of lesions and breasts: Methods for segmentation of breast lesions and breasts from DCE-MRI were investigated using a dataset of patients diagnosed with cancerous or benign mass- or nonmass-enhancing lesions. Lesion segmentation performances of U-Net convolutional neural networks were compared to the fuzzy c-means (FCM) clustering algorithm and to radiologist delineations. Breast segmentation was performed on post-contrast subtraction maximum intensity projection images. Results suggest that using a 2D U-Net on post-contrast subtraction DCE-MRIs is feasible and could be an effective alternative to FCM or 3D U-Net for lesion segmentation.Computerized assessment of BPE: An automatic computer BPE scoring method that includes electronic lesion removal was developed using a dataset of DCE-MRIs that had radiologist BPE ratings available from prior clinical review. Qualitative, radiologist-reported BPE ratings and quantitative, computer BPE scores were evaluated for different breast regions, and the effect of varying image types and magnet strengths was investigated. A statistically significant correlation was found between the radiologist and computer BPEs. Results demonstrated promising performances of the computerized method for classifying BPE levels across various viewing projections and DCE timepoints.BPE scoring on a high-risk dataset: The role of BPE in predicting breast cancer was explored for a dataset of high-risk screening DCE-MRIs. An independent validation of the BPE scoring algorithm reproduced findings from the initial dataset on an independent dataset. In addition, results found a statistically significant difference between the computer BPE scores of patients that developed cancer and those of non-cancer patients with low BPE. Future investigations involving enriched datasets would expand the understanding of the role that computer BPE scores can have in predicting cancer.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Oncology.

Subject Added Entry-Topical Term  

Bioinformatics.

Index Term-Uncontrolled  

BPE

Index Term-Uncontrolled  

Breast DCE-MRI

Index Term-Uncontrolled  

FCM clustering

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Segmentation

Index Term-Uncontrolled  

U-Net

Added Entry-Corporate Name  

The University of Chicago Medical Physics

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640179