Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Kinh Gian, Do"'
Autor:
Ghassan K. Abou-Alfa, Tim Meyer, Richard Kinh Gian Do, Sarina A. Piha-Paul, Joseph S. Light, Scott Sherrin, Amin Yaqubie, Alison Clemens O’Neill, James J. Harding, Raed Al-Rajabi, Crystal S. Denlinger, Pablo Cano, Albert S. Cornelius, Eileen M. O'Reilly, Daniel DiPrimeo, Lisa D. Eli, John D. Gordan, David B. Solit
Publikováno v:
Liver Cancer, Pp 1-10 (2024)
Introduction: Fibrolamellar carcinoma (FLC) displays upregulation of several oncogenes, including HER2, and multiple immune-suppressive mechanisms. We investigated the efficacy and safety of the pan-HER tyrosine kinase inhibitor neratinib as monother
Externí odkaz:
https://doaj.org/article/060277f2d7054affa161c9c60698ca95
Autor:
Richa Patel, Anum Aslam, Neehar D. Parikh, Benjamin Mervak, Eman Mubarak, Lily Higgins, Kayli Lala, Jack F. Conner, Valerie Khaykin, Mustafa Bashir, Richard Kinh Gian Do, Lauren M. B. Burke, Elainea N. Smith, Charles Y. Kim, Kimberly L. Shampain, Dawn Owen, Mishal Mendiratta‐Lala
Publikováno v:
Journal of Magnetic Resonance Imaging. 57:1641-1654
Publikováno v:
Insights into Imaging, Vol 9, Iss 4, Pp 611-629 (2018)
Abstract Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically an
Externí odkaz:
https://doaj.org/article/dc12a8d883e9458db742fd7c1c320370
Publikováno v:
Clin Imaging
PURPOSE: To examine outcomes of incidental liver lesions on baseline breast magnetic resonance imaging (MRI) that were further evaluated with dedicated abdominal imaging. METHODS: Consecutive breast MRI reports from 2011–2016 were retrospectively r
Autor:
Nitya Raj, Kelley Coffman, Tiffany Le, Richard Kinh Gian Do, Johnathan Rafailov, Ye Choi, Joanne F. Chou, Marinela Capanu, Mark Dunphy, Josef J. Fox, Ravinder K. Grewal, Ryan P. Reddy, Christopher Riedl, Heiko Schoder, Lisa Bodei, Diane Reidy-Lagunes
Publikováno v:
Neuroendocrinology
Introduction: Lutetium-177 (177Lu)-DOTATATE received FDA approval in 2018 to treat somatostatin receptor-positive gastroenteropancreatic neuroendocrine tumors (NETs). Little data are available on response and outcomes for well-differentiated (WD) hig
Autor:
Marsha Reyngold, Sana D. Karam, Carla Hajj, Abraham J. Wu, John Cuaron, Stephanie Lobaugh, Ellen D. Yorke, Shannan Dickinson, Bernard Jones, Yevgeniy Vinogradskiy, Amita Shukla-Dave, Richard Kinh Gian Do, Carlie Sigel, Zhigang Zhang, Christopher H. Crane, Karyn A. Goodman
Publikováno v:
International Journal of Radiation Oncology*Biology*Physics.
Autor:
Ramin Jafari, Richard Kinh Gian Do, Maria Divina LaGratta, Maggie Fung, Ersin Bayram, Ty Cashen, Ricardo Otazo
Publikováno v:
NMR in Biomedicine. 36
The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories
Autor:
Amani Shah, Elizabeth A. Sadowski, Kerry Thomas, Kathryn J. Fowler, Richard Kinh Gian Do, Sharon D’Souza, Parvati Ramchandani, Priyanka Jha
Publikováno v:
Abdominal radiology (New York), vol 47, iss 7
Purpose To evaluate the gender and racial diversity of plenary session speakers in the annual meetings of Society of Abdominal Radiology (SAR) over 2016 to 2020. Materials and methods The brochures of the SAR annual meetings were reviewed for plenary
Autor:
Bradley, Spieler, Carl, Sabottke, Ahmed W, Moawad, Ahmed M, Gabr, Mustafa R, Bashir, Richard Kinh Gian, Do, Vahid, Yaghmai, Radu, Rozenberg, Marielia, Gerena, Joseph, Yacoub, Khaled M, Elsayes
Publikováno v:
Abdominal radiology (New York). 46(8)
Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates
Autor:
Ian Pan, Hao-Hsin Shih, Krishna Juluru, Pierre Elnajjar, Krishna Nand Keshavamurthy, Amin El-Rowmeim, Kinh Gian Do
Publikováno v:
Proc SPIE Int Soc Opt Eng
Categorization of radiological images according to characteristics such as modality, scanner parameters, body part etc, is important for quality control, clinical efficiency and research. The metadata associated with images stored in the DICOM format