Approach

We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with Magnetic Resonance Imaging (MRI) data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Statistically significant associations (adjust p-value <= 0.05) have been identified between quantitative MRI phenotypes of tumors (such as tumor size, shape, margin, and blood flow kinetics) and their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation).

Results

We provide all the identified associations as a freely available resource for the research community. We expect our analysis results can help (1) discover the genetic mechanism that leads to the formation of specific tumor imaging phenotype and (2) improve MRI techniques as potential non-invasive approaches to probe the cancer molecular status.

Contact

• Yitan Zhu: zhuyitan@gmail.com

Download

All the identified statistically significant associations have been compiled into an excel file. [Download]. For details of the data, method, and analysis, please refer to the following paper. Please cite the paper, if you use our results in your publication.

Citation

Y. Zhu, H. Li, W. Guo, K. Drukker, L. Lan, M. L. Giger, Y. Ji, Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma, Scientific Reports, in press.

Collaborators

Maryellen Giger's group (https://radiology.uchicago.edu/page/maryellen-l-giger-lab) at The University of Chicago.