東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep Learning and Radiomics of Breas...

Antropova, Natalia.

FindBook

Google Book

Amazon

博客來

Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy./
作者:	Antropova, Natalia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	143 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Medical imaging. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10784160
ISBN:	9780438083431

Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
Antropova, Natalia.

Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 143 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--The University of Chicago, 2018.

Breast cancer is found in one in eight women in the United States and is expected to be the most frequently diagnosed form of cancer among them in 2018. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer management, especially for high-risk patients. The interpretation of the DCE-MR images remains labor-intensive and can lead to erroneous clinical results with subsequent unnecessary biopsies and patient stress. Computer-aided detection (CADe) and diagnosis (CADx) systems, or radiomics, have been developing to help reduce these errors. The conventional CADx methods involve automatic segmentation of a lesion from the neighboring background and extraction of intuitive hand-crafted features, previously developed by the engineers and domain experts. Such features describe lesion's size, shape, texture, and enhancement patters. The recent advances in machine learning techniques have provided an alternative method for image assessment, where images are analyzed directly by deep learning models.

ISBN: 9780438083431Subjects--Topical Terms:

3172799
Medical imaging.

Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
LDR:04314nmm a2200349 4500 001 2162365
005 20180928111502.5
008 190424s2018 ||||||||||||||||| ||eng d
020 $a 9780438083431
035 $a (MiAaPQ)AAI10784160
035 $a (MiAaPQ)uchicago:14239
035 $a AAI10784160
040 $a MiAaPQ $c MiAaPQ
100 1 $a Antropova, Natalia. $3 3350349
245 1 0 $a Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 143 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 $a Adviser: Maryellen Giger.
502 $a Thesis (Ph.D.)--The University of Chicago, 2018.
520 $a Breast cancer is found in one in eight women in the United States and is expected to be the most frequently diagnosed form of cancer among them in 2018. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer management, especially for high-risk patients. The interpretation of the DCE-MR images remains labor-intensive and can lead to erroneous clinical results with subsequent unnecessary biopsies and patient stress. Computer-aided detection (CADe) and diagnosis (CADx) systems, or radiomics, have been developing to help reduce these errors. The conventional CADx methods involve automatic segmentation of a lesion from the neighboring background and extraction of intuitive hand-crafted features, previously developed by the engineers and domain experts. Such features describe lesion's size, shape, texture, and enhancement patters. The recent advances in machine learning techniques have provided an alternative method for image assessment, where images are analyzed directly by deep learning models.
520 $a Radiomcs has strong potential to lead clinicians towards more accurate and rapid image interpretation. Furthermore, it can serve as a "virtual digital biopsy", allowing for the discovery of relationships between radiomics and the pathology/genomics from actual biopsies, for ultimate use when biopsies are not possible. The objective of this research is to analyze the conventional and design new radiomics methods for breast DCE-MRI, in order to improve image-based clinical decisions. Specifically, part of the dissertation studies the robustness of conventional radiomics methods across MRI scanners of different manufacturers. Another focus of the research is developing accurate and robust deep learning-based models for automated breast lesion characterization, tailored to the complex 4-dimentional (4D) DCE-MRI data. These models are applied to two clinical tasks, lesion malignancy assessment and prediction of cancer response to therapy. The research is concluded by testing the ultimate hypothesis that incorporating the two types of radiomics, deep learning-CADx and conventional-CADx, will enhance lesion characterization within the tasks of diagnosis and treatment response.
520 $a The research presented the following results. First, the robustness analysis revealed radiomics features that are generalizable across datasets acquired with MRI scanners of two major manufacturers. Specifically, features that characterize lesions in terms of size are robust in their average values. Furthermore, entropy feature, which quantifies randomness of pixel values of the lesion image, is robust in its classification performance in multiple clinical tasks. Second, a novel deep learning-based method was developed to assess breast lesion malignancy and response to therapy based on the DCE-MRI sequence. Finally, the results demonstrated that deep learning-based methods are complimentary to the conventional radiomics.
520 $a The medical significance of this research is that it has potential to improve DCE-MRI-based breast cancer management. The developed deep learning methods and their fusion with conventional radiomics can reduce human burden and allow for more rapid and accurate analysis of the images.
590 $a School code: 0330.
650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
650 4 $a Oncology. $3 751006
690 $a 0574
690 $a 0800
690 $a 0992
710 2 $a The University of Chicago. $b Medical Physics. $3 1671030
773 0 $t Dissertation Abstracts International $g 79-11B(E).
790 $a 0330
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10784160