Page History

Morris, E., Burnside, E., Whitman, G., Zuley, M., Bonaccio, E., Ganott, M., Sutton, E., Net, J., Brandt, K., Li, H., Drukker, K., Perou, C., & Giger, M. L. (2014). Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2014.8SIPIY6G

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Guo, W., Li, H., Zhu, Y., Lan, L., Yang, S., Drukker, K., Morris, E., Burnside, E., Whitman, G., Giger, M. L., Ji, Y., & TCGA Breast Phenotype Research Group. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging, 2(4), 041007. https://doi.org/10.1117/1.jmi.2.4.041007

Burnside E, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton E, Brandt K, Whitman G, Conzen S, Lan L, Ji Y, Zhu Y, Jaffe C, Huang E, Freymann J, Kirby J, Morris EA, Giger ML. (2016)  Using computer-extracted image phenotypes from tumors on breast MRI to predict breast cancer pathologic stage. Cancer 122(5): 748-757 . DOI: 10.1002/cncr.29791

Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML*, Ji Y*:  Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma.  Nature – Scientific Reports 5:17787. doi: 10.1038/srep17787, 2015.

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. (2016) MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of gene assays of MammaPrint, Oncotype DX, and PAM50.  Radiology 281(2):382-391. doi: 10.1148/radiol.2016152110

Li H, Zhu Y, Burnside ES, …. Perou CM, Ji Y, Giger ML:  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA Dataset. npj Breast Cancer (2016) 2, 16012; doi:10.1038/npjbcancer.2016.12; published online 11 May 2016.

Please also include the following acknowledgement:

“The authors would like to thank the TCGA Breast Phenotype Research Group for providing the computer-extracted tumor segmentation data used in this study. The tumor segmentation data comes from the University of Chicago lab of Maryellen Giger,
whose lab members participated in the TCGA Breast Phenotype Research Group. In any presentation, poster, paper, etc, the segmentations should be identified as “Chicago Dynamic MRI Explorer 2005 Version”. We would also like to acknowledge The Cancer Imaging Archive and The Cancer Genome Atlas initiatives for making the imaging and the clinical data used in this study publicly available.”