TCIA Analysis Results
TCGA Breast Phenotype Research Group Data sets
TCGA Breast Phentotype Research Group Data sets
Created by Ashish Sharma, last modified by Justin Kirby on Apr 30, 2015
http://dx.doi.org/10.7937/K9/TCIA.2014.8SIPIY6G
Maryellen L. Giger et. al. Add all readers and anyone else who contributed to creation of any of the data mentioned below.
At the time of our study, 108 cases with breast MRI data were available in the TCGA-BRCA collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee,Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the TCGA Data Portal (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+. Various types of analyses were conducted using the combined imaging, genomic, and clinical data. Those analyses are described within several manuscripts created by the group (cited above).
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Radiologist Annotations/Markup
Not publicly available at this time; please contact the TCIA Helpdesk to request access
Computer-extracted image phentotypes
Multi-gene assays including MammaPrint, Ocotype DX, and PAM50
TCGA Clinical Data (from TCGA Data Portal, archived in case of subsequent updates made by TCGA)
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Source Shared List
Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage.