Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Julie C. DiCarlo"'
Autor:
John Virostko, Anna G. Sorace, Kalina P. Slavkova, Anum S. Kazerouni, Angela M. Jarrett, Julie C. DiCarlo, Stefanie Woodard, Sarah Avery, Boone Goodgame, Debra Patt, Thomas E. Yankeelov
Publikováno v:
Breast Cancer Research, Vol 23, Iss 1, Pp 1-12 (2021)
Abstract Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual
Externí odkaz:
https://doaj.org/article/82deaf63a0e6424a945d9c5d7d128036
Autor:
Kalina P. Slavkova, Julie C. DiCarlo, Anum S. Kazerouni, John Virostko, Anna G. Sorace, Debra Patt, Boone Goodgame, Thomas E. Yankeelov
Publikováno v:
Tomography, Vol 7, Iss 3, Pp 253-267 (2021)
This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant.
Externí odkaz:
https://doaj.org/article/244a6d56c90448bab12b3edcaba5617e
Autor:
Angela M. Jarrett, David A. Hormuth, II, Chengyue Wu, Anum S. Kazerouni, David A. Ekrut, John Virostko, Anna G. Sorace, Julie C. DiCarlo, Jeanne Kowalski, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov
Publikováno v:
Neoplasia: An International Journal for Oncology Research, Vol 22, Iss 12, Pp 820-830 (2020)
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitativ
Externí odkaz:
https://doaj.org/article/17a79e5efe4e466a8d55e196250c77f9
Autor:
Julie C. DiCarlo, Angela M. Jarrett, Anum S. Kazerouni, John Virostko, Anna Sorace, Kalina P. Slavkova, Stefanie Woodard, Sarah Avery, Debra Patt, Boone Goodgame, Thomas E. Yankeelov
Publikováno v:
Magnetic Resonance in Medicine. 89:1134-1150
A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition.Simplicial complexes are generated from the Kety-Tofts model pharmacokin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0469b7b25cc64068d16a394335895c30
https://doi.org/10.1002/mrm.29511
https://doi.org/10.1002/mrm.29511
Autor:
Anna G. Sorace, Sarah Avery, David A. Ekrut, Boone Goodgame, Debra A. Patt, Chengyue Wu, Anum S. Kazerouni, John Virostko, Angela M. Jarrett, Thomas E. Yankeelov, Julie C. DiCarlo, David A. Hormuth
Publikováno v:
Nat Protoc
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care settin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a21d4539787fdce4e6098529ded032a
https://doi.org/10.1038/s41596-021-00617-y
https://doi.org/10.1038/s41596-021-00617-y
Autor:
Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, John Virostko, Thomas E. Yankeelov, Jonathan I. Tamir
Publikováno v:
Magnetic resonance in medicineREFERENCES.
To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.The ConvDecoder (CD) neural network was trained with a physics-based regularization term i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::495ebf80d942467455c7f938545ecb6f
https://pubmed.ncbi.nlm.nih.gov/36468624
https://pubmed.ncbi.nlm.nih.gov/36468624
Autor:
Anna G. Sorace, Angela M. Jarrett, Thomas E. Yankeelov, Debra A. Patt, Jeanne Kowalski, Boone Goodgame, Sarah Avery, John Virostko, Chengyue Wu, Julie C DiCarlo, David A. Hormuth, Anum K. Syed
Publikováno v:
Cancer Research. 81:PS13-18
Background: This study evaluates the ability to predict the response of locally advanced breast cancers to neoadjuvant therapy (NAT) using patient-specific magnetic resonance imaging (MRI) data and a biophysical mathematical model. The 3D mathematica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cb350d71842b6e0c3cc93c27f648005
https://doi.org/10.1158/1538-7445.sabcs20-ps13-18
https://doi.org/10.1158/1538-7445.sabcs20-ps13-18
Autor:
Anna G. Sorace, Julie C DiCarlo, Kalina P Slavkova, Anum K. Syed, Chengyue Wu, John Virostko, Thomas E. Yankeelov
Publikováno v:
Cancer Research. 81:PS3-26
Introduction. X-ray mammography is the standard-of-care screening protocol for breast cancer due to its low cost, widespread availability, and greater specificity. While magnetic resonance imaging (MRI) has lower specificity, it has superior tissue c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5f6d05f5c4f5c9d18542a0cc8aff3ee
https://doi.org/10.1158/1538-7445.sabcs20-ps3-26
https://doi.org/10.1158/1538-7445.sabcs20-ps3-26
Autor:
Thomas E. Yankeelov, Anum S. Kazerouni, Boone Goodgame, John Virostko, Debra A. Patt, Sarah Avery, David A. Hormuth, Chengyue Wu, Anna G. Sorace, Jeanne Kowalski, Julie C DiCarlo, David A. Ekrut, Angela M. Jarrett
Publikováno v:
Neoplasia (New York, N.Y.)
Neoplasia: An International Journal for Oncology Research, Vol 22, Iss 12, Pp 820-830 (2020)
Neoplasia: An International Journal for Oncology Research, Vol 22, Iss 12, Pp 820-830 (2020)
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b24583f1570c4a517b71e479f6ea604
https://doi.org/10.1016/j.neo.2020.10.011
https://doi.org/10.1016/j.neo.2020.10.011
Autor:
Anna G. Sorace, David A. Hormuth, John Virostko, Julie C DiCarlo, Debra A. Patt, Angela M. Jarrett, Boone Goodgame, Thomas E. Yankeelov, Sarah Avery, Chengyue Wu
Publikováno v:
Cancer Research. 80:P2-16
Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd2f797473db6eb0bd4ae9b11b1b9f04
https://doi.org/10.1158/1538-7445.sabcs19-p2-16-17
https://doi.org/10.1158/1538-7445.sabcs19-p2-16-17