國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy./
Author:	Antropova, Natalia.
Description:	1 online resource (143 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
Subject:	Medical imaging. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (143 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438083431Subjects--Topical Terms:

1180167
Medical imaging.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
LDR:04597ntm a2200385Ki 4500 001 916882
005 20180928111502.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780438083431
035 $a (MiAaPQ)AAI10784160
035 $a (MiAaPQ)uchicago:14239
035 $a AAI10784160
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Antropova, Natalia. $3 1190742
245 1 0 $a Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
264 0 $c 2018
300 $a 1 online resource (143 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Medical imaging. $3 1180167
650 4 $a Artificial intelligence. $3 559380
650 4 $a Oncology. $3 593951
655 7 $a Electronic books. $2 local $3 554714
690 $a 0574
690 $a 0800
690 $a 0992
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Chicago. $b Medical Physics. $3 1190743
773 0 $t Dissertation Abstracts International $g 79-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10784160 $z click for full text (PQDT)