Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
2016 · npj Breast Cancer · 353 citations
https://doi.org/10.1038/npjbcancer.2016.12
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Elizabeth J. Sutton
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Elizabeth J. Sutton is an academic researcher. The author has contributed to research in topics: Radiomics and Machine Learning in Medical Imaging & MRI in cancer diagnosis & Breast Cancer Treatment Studies. The author has an h-index of 31, co-authored 140 publications.
ORCID: 0000-0002-1407-7012Papers by this author
worksA machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy
2020 · Breast Cancer Research · 98 citations
https://doi.org/10.1186/s13058-020-01291-w
Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes
2019 · Molecular Imaging and Biology · 82 citations
https://doi.org/10.1007/s11307-019-01383-w
Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer
2020 · Breast Cancer Research · 71 citations
https://doi.org/10.1186/s13058-020-01292-9
Apparent diffusion coefficient in estrogen receptor‐positive and lymph node‐negative invasive breast cancers at 3.0T DW‐MRI: A potential predictor for an oncotype Dx test recurrence score
2017 · Journal of Magnetic Resonance Imaging · 36 citations
https://doi.org/10.1002/jmri.25796
Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes
2017 · European Radiology Experimental · 36 citations
https://doi.org/10.1186/s41747-017-0025-2
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