Info |
---|
| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: https://doi.org/10.1007/s10278-013-9622-7 |
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:
Info |
---|
title | Publication Citation |
---|
| Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML, Ji Y. (2015) Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Medical Imaging 2(4), 041007 (Oct-Dec 2015). doi: 10.1117/1.JMI.2.4.041007 |
Info |
---|
title | Publication Citation |
---|
| 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 |
Info |
---|
title | Publication Citation |
---|
| 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. |
Info |
---|
title | Publication Citation |
---|
| 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 |
Info |
---|
title | Publication Citation |
---|
| 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. |
Info |
---|
| 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.” |
Other Publications Using This DataTCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |