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020 ▼a 9780438083431
035 ▼a (MiAaPQ)AAI10784160
035 ▼a (MiAaPQ)uchicago:14239
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 616
1001 ▼a Antropova, Natalia.
24510 ▼a Deep Learning and Radiomics of Breast Cancer on DCE-MRI in Assessment of Malignancy and Response to Therapy.
260 ▼a [S.l.] : ▼b The University of Chicago., ▼c 2018
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.
5021 ▼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 b
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/genom
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 lesio
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 rap
590 ▼a School code: 0330.
650 4 ▼a Medical imaging.
650 4 ▼a Artificial intelligence.
650 4 ▼a Oncology.
690 ▼a 0574
690 ▼a 0800
690 ▼a 0992
71020 ▼a The University of Chicago. ▼b Medical Physics.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0330
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997249 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자