Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting-Reference-Cited by-同舟云学术

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Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting

Published:2023-05 Issue: Volume:69 Page:102006
ISSN:1877-7503
Container-title:Journal of Computational Science
language:en
Short-container-title:Journal of Computational Science

Author:

Phillips C.M.,Lima E.A.B.F.^ORCID,Wu C.,Jarrett A.M.,Zhou Z.,Elshafeey N.,Ma J.,Rauch G.M.,Yankeelov T.E.

Funder

Cancer Prevention and Research Institute of Texas

University of Texas MD Anderson Cancer Center

National Cancer Institute

National Institutes of Health

Publisher

Elsevier BV

Subject

Modeling and Simulation,General Computer Science,Theoretical Computer Science

Reference38 articles.

1. M.A. Media, G. Oza, A. Sharma, L.G. Arriaga, J.M. Hernández, V.M. Rotello, J.T. Ramirez, Triple-negative breast cancer: A review of conventional and advanced therapeutic strategies, Int. J. Environ. Res. Public Health 17, 6, http://dx.doi.org/10.3390/ijerph17062078.

2. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease;Bianchini;Nat. Rev. Clin. Oncol.,2016

3. Pembrolizumab for early triple-negative breast cancer;Schmid;N. Engl. J. Med.,2020

4. Simultaneous pet–mri in oncology: a solution looking for a problem?;Yankeelov;Magn. Reson. Imaging,2012

5. Predicting the response of breast cancer to neoadjuvant therapy using a mechanically coupled reaction–diffusion model;Weis;Cancer Res.,2015

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1. Deep learning based capsule networks for breast cancer classification using ultrasound images;Current Cancer Reports;2024-08-27

2. Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy;Journal of Controlled Release;2024-06

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