Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Daniel L. McShan"'
Priority-driven plan optimization in locally advanced lung patients based on perfusion SPECT imaging
Autor:
Martha M. Matuszak, PhD, Charles Matrosic, MS, David Jarema, Daniel L. McShan, Matthew H. Stenmark, MD, Dawn Owen, MD, Shruti Jolly, MD, Feng-Ming (Spring) Kong, MD, Randall K. Ten Haken, PhD
Publikováno v:
Advances in Radiation Oncology, Vol 1, Iss 4, Pp 281-289 (2016)
Purpose: Limits on mean lung dose (MLD) allow for individualization of radiation doses at safe levels for patients with lung tumors. However, MLD does not account for individual differences in the extent or spatial distribution of pulmonary dysfuncti
Externí odkaz:
https://doaj.org/article/975d856a76364004a9e5bd42e5377d23
Autor:
Charles S. Mayo, PhD, Marc L. Kessler, PhD, Avraham Eisbruch, MD, Grant Weyburne, BS, Mary Feng, MD, James A. Hayman, MD, Shruti Jolly, MD, Issam El Naqa, PhD, Jean M. Moran, PhD, Martha M. Matuszak, PhD, Carlos J. Anderson, PhD, Lynn P. Holevinski, BS, Daniel L. McShan, PhD, Sue M. Merkel, MSA RT(R)(T), Sherry L. Machnak, MBA RT(T), Theodore S. Lawrence, MD PhD, Randall K. Ten Haken, PhD
Publikováno v:
Advances in Radiation Oncology, Vol 1, Iss 4, Pp 260-271 (2016)
Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge.
Externí odkaz:
https://doaj.org/article/8c1c148fd26c41019b2e9725732bed0a
Publikováno v:
Medical physicsREFERENCES.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c4aa2d51c83209b948778df49b61433
https://pubmed.ncbi.nlm.nih.gov/36427855
https://pubmed.ncbi.nlm.nih.gov/36427855
Autor:
Daniel L. McShan, Issam Ei Naqa, David A. Palma, Theodore S. Lawrence, Shruti Jolly, Randall K. Ten Haken, Huan-Hsin Tseng, Yi Luo, Gilmer Valdes
Publikováno v:
Phys Med
Purpose A situational awareness Bayesian network (SA-BN) approach is developed to improve physicians’ trust in the prediction of radiation outcomes and evaluate its performance for personalized adaptive radiotherapy (pART). Methods 118 non-small-ce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa1a8407a32d6d9d8362d478ac73786e
https://escholarship.org/uc/item/462857w7
https://escholarship.org/uc/item/462857w7
Autor:
Daniel L. McShan, Feng-Ming Kong, Dipankar Ray, Shruti Jolly, Randall K. Ten Haken, Martha M. Matuszak, Yi Luo, Issam El Naqa, Theodore Lawrence
Publikováno v:
IEEE Transactions on Radiation and Plasma Medical Sciences. 3:232-241
The purpose of this study is to demonstrate that a Bayesian network (BN) approach can explore hierarchical biophysical relationships that influence tumor response and predict tumor local control (LC) in non-small-cell lung cancer (NSCLC) patients bef
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f56103052b6a31bb04c8572f0259e0ea
https://doi.org/10.1109/trpms.2018.2832609
https://doi.org/10.1109/trpms.2018.2832609
Autor:
Dipankar Ray, Yi Luo, Martha M. Matuszak, Daniel L. McShan, Shruti Jolly, Theodore S. Lawrence, Randall K. Ten Haken, Issam El Naqa, Feng-Ming Kong
Publikováno v:
Medical Physics. 45:3980-3995
Purpose Individualization of therapeutic outcomes in NSCLC radiotherapy is likely to be compromised by the lack of proper balance of biophysical factors affecting both tumor local control (LC) and side effects such as radiation pneumonitis (RP), whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f8730415acc6ed631b6b933e46faefa
https://doi.org/10.1002/mp.13029
https://doi.org/10.1002/mp.13029
Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer
Publikováno v:
Phys Med Biol
Objective. Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64e368868fdb3e8a5844abc814e70116
https://doi.org/10.1088/1361-6560/ac2b80
https://doi.org/10.1088/1361-6560/ac2b80
Autor:
Reshma Jagsi, Jean M. Moran, Adam L. Liss, Virginia E. Rogers, James M. Balter, Kent A. Griffith, Nirav S. Kapadia, Kristy K. Brock, Kirk A. Frey, Kevin R. Flaherty, Lori J. Pierce, M.J. Schipper, Daniel L. McShan, Robin B. Marsh
Publikováno v:
International Journal of Radiation Oncology*Biology*Physics. 97:296-302
To quantify lung perfusion changes after breast/chest wall radiation therapy (RT) using pre- and post-RT single photon emission computed tomography/computed tomography (SPECT/CT) attenuation-corrected perfusion scans; and correlate decreased perfusio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d856d097d855c9cb535b58b352c7a64
https://doi.org/10.1016/j.ijrobp.2016.10.012
https://doi.org/10.1016/j.ijrobp.2016.10.012
Autor:
Huan-Hsin Tseng, Issam El Naqa, Julia M. Pakela, Daniel L. McShan, Martha M. Matuszak, Randall K. Ten Haken
Publikováno v:
Med Phys
Purpose Modern inverse radiotherapy treatment planning requires nonconvex, large-scale optimizations that must be solved within a clinically feasible timeframe. We have developed and tested a quantum-inspired, stochastic algorithm for intensity-modul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::531ba0d27ba05f4328a1eb1ea0893909
https://pubmed.ncbi.nlm.nih.gov/31574176
https://pubmed.ncbi.nlm.nih.gov/31574176
Autor:
Carlos J. R. Anderson, James A. Hayman, Randall K. Ten Haken, Sue M. Merkel, Marc L. Kessler, Theodore S. Lawrence, Charles S. Mayo, Avraham Eisbruch, Mary Feng, Grant Weyburne, Lynn Holevinski, Shruti Jolly, Martha M. Matuszak, Jean M. Moran, Sherry L. Machnak, Issam El Naqa, Daniel L. McShan
Publikováno v:
Advances in Radiation Oncology, Vol 1, Iss 4, Pp 260-271 (2016)
Advances in Radiation Oncology
Advances in Radiation Oncology
Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0c366b102633d7a70d0d0b15b54f381