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Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
Burnside ES, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton EJ, Brandt KR, Whitman GJ, Conzen SD, Lan L, Ji Y, Zhu Y, Jaffe CC, Huang EP, Freymann JB, Kirby JS, Morris EA, Giger ML. Burnside ES, et al. Among authors: conzen sd. Cancer. 2016 Mar 1;122(5):748-57. doi: 10.1002/cncr.29791. Epub 2015 Nov 30. Cancer. 2016. PMID: 26619259 Free PMC article.
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML. Li H, et al. Among authors: conzen sd. Radiology. 2016 Nov;281(2):382-391. doi: 10.1148/radiol.2016152110. Epub 2016 May 5. Radiology. 2016. PMID: 27144536 Free PMC article.
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML. Li H, et al. Among authors: conzen sd. NPJ Breast Cancer. 2016;2:16012-. doi: 10.1038/npjbcancer.2016.12. Epub 2016 May 11. NPJ Breast Cancer. 2016. PMID: 27853751 Free PMC article.
94 results