Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Kathryn P. Lowry"'
Autor:
Janie M. Lee, Kathryn P. Lowry, Jessica E. Cott Chubiz, J. Shannon Swan, Tina Motazedi, Elkan F. Halpern, Anna N.A. Tosteson, G. Scott Gazelle, Karen Donelan
Publikováno v:
Breast, Vol 50, Iss , Pp 104-112 (2020)
Objective: The impact of mammography screening recall on quality-of-life (QOL) has been studied in women at average risk for breast cancer, but it is unknown whether these effects differ by breast cancer risk level. We used a vignette-based survey to
Externí odkaz:
https://doaj.org/article/4b3b821ba3b34b719688367f10093978
Autor:
Kathryn P. Lowry, Laura Ichikawa, Rebecca A. Hubbard, Diana S. M. Buist, Erin J. A. Bowles, Louise M. Henderson, Karla Kerlikowske, Jennifer M. Specht, Brian L. Sprague, Karen J. Wernli, Janie M. Lee
Publikováno v:
Cancer. 129:1173-1182
Autor:
Yu-Ru Su, Diana S.M. Buist, Janie M. Lee, Laura Ichikawa, Diana L. Miglioretti, Erin J. Aiello Bowles, Karen J. Wernli, Karla Kerlikowske, Anna Tosteson, Kathryn P. Lowry, Louise M. Henderson, Brian L. Sprague, Rebecca A. Hubbard
Publikováno v:
Cancer Epidemiology, Biomarkers & Prevention. 32:561-571
Background: Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive perf
Autor:
Nathaniel Hendrix, Kathryn P. Lowry, Joann G. Elmore, William Lotter, Gregory Sorensen, William Hsu, Geraldine J. Liao, Sana Parsian, Suzanne Kolb, Arash Naeim, Christoph I. Lee
Publikováno v:
Hendrix, N, Lowry, K P, Elmore, J G, Lotter, W, Sorensen, G, Hsu, W, Liao, G J, Parsian, S, Kolb, S, Naeim, A & Lee, C I 2022, ' Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation ', Journal of the American College of Radiology : JACR, vol. 19, no. 10, pp. 1098-1110 . https://doi.org/10.1016/j.jacr.2022.06.019
Journal of the American College of Radiology : JACR, 19(10), 1098-1110. Elsevier BV
Journal of the American College of Radiology : JACR, 19(10), 1098-1110. Elsevier BV
BACKGROUND: Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown. PURPOSE: To quantify how different att
Autor:
Isabelle L. Crary, Elizabeth U. Parker, Kathryn P. Lowry, Pranav P. Patwardhan, Thing Rinda Soong, Sara H. Javid, Kristine E. Calhoun, Meghan R. Flanagan
Publikováno v:
Annals of Surgical Oncology. 29:6350-6358
Autor:
Rebecca A. Hubbard, Brian L. Sprague, Louise M. Henderson, Kathryn P. Lowry, Anna Tosteson, Karla Kerlikowske, Karen J. Wernli, Erin J. Aiello Bowles, Diana L. Miglioretti, Laura Ichikawa, Janie M. Lee, Diana S.M. Buist, Yu-Ru Su
Supplementary Table S1 shows the calibration assessment in a sensitivity analysis on LASSO and Elastic-net by enforcing the adjustment of the matching factor.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a968cccd14d412b11c342b2936e1082
https://doi.org/10.1158/1055-9965.22494787
https://doi.org/10.1158/1055-9965.22494787
Autor:
Rebecca A. Hubbard, Brian L. Sprague, Louise M. Henderson, Kathryn P. Lowry, Anna Tosteson, Karla Kerlikowske, Karen J. Wernli, Erin J. Aiello Bowles, Diana L. Miglioretti, Laura Ichikawa, Janie M. Lee, Diana S.M. Buist, Yu-Ru Su
Supplementary Figure S1 shows frequency of predictor inclusion in regression-based models for surveillance failure and benefit across imputed datasets.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0beae06a87d35acb285a23ef3a668132
https://doi.org/10.1158/1055-9965.22494799.v1
https://doi.org/10.1158/1055-9965.22494799.v1
Autor:
Rebecca A. Hubbard, Brian L. Sprague, Louise M. Henderson, Kathryn P. Lowry, Anna Tosteson, Karla Kerlikowske, Karen J. Wernli, Erin J. Aiello Bowles, Diana L. Miglioretti, Laura Ichikawa, Janie M. Lee, Diana S.M. Buist, Yu-Ru Su
Background:Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive perfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::528f974dce8fcbe9b320e7053c2cf2d1
https://doi.org/10.1158/1055-9965.c.6534625
https://doi.org/10.1158/1055-9965.c.6534625
Autor:
Rebecca A. Hubbard, Brian L. Sprague, Louise M. Henderson, Kathryn P. Lowry, Anna Tosteson, Karla Kerlikowske, Karen J. Wernli, Erin J. Aiello Bowles, Diana L. Miglioretti, Laura Ichikawa, Janie M. Lee, Diana S.M. Buist, Yu-Ru Su
Supplementary Methods show the detailed information of model fitting using the machine learning approaches in this work.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19af56dd0187cf683a6118c406f16d5d
https://doi.org/10.1158/1055-9965.22494790
https://doi.org/10.1158/1055-9965.22494790
Autor:
Isabelle L. Crary, Elizabeth U. Parker, Kathryn P. Lowry, Pranav P. Patwardhan, Thing Rinda Soong, Sara H. Javid, Kristine E. Calhoun, Meghan R. Flanagan
Publikováno v:
Annals of surgical oncology. 29(10)