Zobrazeno 1 - 10
of 500
pro vyhledávání: '"Martin J. Yaffe"'
Autor:
Martin J. Yaffe, James G. Mainprize
Publikováno v:
Current Oncology, Vol 30, Iss 11, Pp 9475-9483 (2023)
Guidelines vary for the age at which to begin breast cancer screening and the interval between examinations. A validated computer model was used to compare estimated outcomes between various screening regimens. The OncoSim-Breast microsimulation mode
Externí odkaz:
https://doaj.org/article/74f402ff6fa54318a56d72727a4d2666
Autor:
Anna N. Wilkinson, Jean M. Seely, Moira Rushton, Phillip Williams, Erin Cordeiro, Alexandra Allard-Coutu, Nicole J. Look Hong, Nikitha Moideen, Jessica Robinson, Julie Renaud, James G. Mainprize, Martin J. Yaffe
Publikováno v:
Current Oncology, Vol 30, Iss 9, Pp 7860-7873 (2023)
Background: Breast cancer (BC) treatment is rapidly evolving with new and costly therapeutics. Existing costing models have a limited ability to capture current treatment costs. We used an Activity-Based Costing (ABC) method to determine a per-case c
Externí odkaz:
https://doaj.org/article/a139af6229364d7ba8fe235233275357
Autor:
Laurel A. Habel, Stacey E. Alexeeff, Ninah Achacoso, Vignesh A. Arasu, Aimilia Gastounioti, Lawrence Gerstley, Robert J. Klein, Rhea Y. Liang, Jafi A. Lipson, Walter Mankowski, Laurie R. Margolies, Joseph H. Rothstein, Daniel L. Rubin, Li Shen, Adriana Sistig, Xiaoyu Song, Marvella A. Villaseñor, Mark Westley, Alice S. Whittemore, Martin J. Yaffe, Pei Wang, Despina Kontos, Weiva Sieh
Publikováno v:
Breast Cancer Research, Vol 25, Iss 1, Pp 1-9 (2023)
Abstract Background Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long
Externí odkaz:
https://doaj.org/article/c80b8409fd6744bfba5b869ab41e08e7
Autor:
Martin J. Yaffe, James G. Mainprize
Publikováno v:
Current Oncology, Vol 29, Iss 6, Pp 3894-3910 (2022)
Overdetection (often referred to as overdiagnosis) of cancer is the detection of disease, such as through a screening program, that would otherwise remain occult through an individual’s life. In the context of screening, this could occur for cancer
Externí odkaz:
https://doaj.org/article/e4bb4d877eb14343bc5cd5061155d464
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract Cellular profiling with multiplexed immunofluorescence (MxIF) images can contribute to a more accurate patient stratification for immunotherapy. Accurate cell segmentation of the MxIF images is an essential step. We propose a deep learning p
Externí odkaz:
https://doaj.org/article/0368311db3374bc2ac4d471feaeed32d
Autor:
Jean H. E. Yong, Claude Nadeau, William M. Flanagan, Andrew J. Coldman, Keiko Asakawa, Rochelle Garner, Natalie Fitzgerald, Martin J. Yaffe, Anthony B. Miller
Publikováno v:
Current Oncology, Vol 29, Iss 3, Pp 1619-1633 (2022)
Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breas
Externí odkaz:
https://doaj.org/article/999932c21b3e40cfb2a51e03f931a1a8
Autor:
Martin J. Yaffe
Publikováno v:
Cancers, Vol 15, Iss 18, p 4576 (2023)
Observational studies of cancer screening are subject to bias associated with the self-selection of screening participants for whom the underlying probability of cancer death may be different from those who do not participate. Dibden et al. reviewed
Externí odkaz:
https://doaj.org/article/ddb4d44946064c2090fdf2763a1fd863
Autor:
Weiva Sieh, Joseph H. Rothstein, Robert J. Klein, Stacey E. Alexeeff, Lori C. Sakoda, Eric Jorgenson, Russell B. McBride, Rebecca E. Graff, Valerie McGuire, Ninah Achacoso, Luana Acton, Rhea Y. Liang, Jafi A. Lipson, Daniel L. Rubin, Martin J. Yaffe, Douglas F. Easton, Catherine Schaefer, Neil Risch, Alice S. Whittemore, Laurel A. Habel
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Mammographic density represents one the strongest predictors of breast cancer risk. Here the authors perform genome-wide association study meta-analysis of women screened with full-field digital mammography and identify 31 previously unreported loci
Externí odkaz:
https://doaj.org/article/455a1267540b4504a0c8a2bad60954fb
Publikováno v:
Breast Cancer Research, Vol 21, Iss 1, Pp 1-9 (2019)
Abstract Background Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a
Externí odkaz:
https://doaj.org/article/3a01f82fc5574621883fee7946066a52
Autor:
Norman Boyd, Hal Berman, Jie Zhu, Lisa J. Martin, Martin J. Yaffe, Sofia Chavez, Greg Stanisz, Greg Hislop, Anna M. Chiarelli, Salomon Minkin, Andrew D. Paterson
Publikováno v:
Breast Cancer Research, Vol 20, Iss 1, Pp 1-13 (2018)
Abstract Background Our purpose is to develop a testable biological hypothesis to explain the known increased risk of breast cancer associated with extensive percent mammographic density (PMD), and to reconcile the apparent paradox that although PMD
Externí odkaz:
https://doaj.org/article/1f6f476df89746698b4b3bcb000a4fa5