Work

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

Year: 2023

Type: article

Source: Journal of Computational Science

DOI: 10.1016/j.jocs.2023.102006

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

Institutions: The University of Texas at Austin, Texas Advanced Computing Center, The University of Texas MD Anderson Cancer Center

Language: English

Topic: Mathematical Biology Tumor Growth

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Cancer Prevention and Research Institute of Texas, National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute, Moonshot Research and Development Program, Cancer Moonshot

Awards: G1861679096, Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy, G2307043461, INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER, Development and Dissemination of Clinical CEST MRI Acquisition and Analysis Methods for Cancer Imaging Applications, G3210188822, Systems Approaches to Understanding Subpopulation Heterogeneity in Therapeutic Resistance, G5562860700, Recruitment of Established Investigators, G6135052827, Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer, G6870013078, Personalizing immunotherapy in HER2+ breast cancer through quantitative imaging, G8141834623, Neoadjuvant Treatment Response Monitoring of Breast Cancer With Molecular Photoacoustic Imaging

OpenAlex ID: W4362634245

FWCI: 1.892

Cites: 36

Cited by: 9

Related to: 10