The Cancer Imaging Archive (TCIA) Public Access

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  1. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-A77.

  2. Pope WB. Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 2015;25(1):105-19.

  3. Colen R, Foster I, Gatenby R, Giger ME, Gillies R, Gutman D, Heller M, Jain R, Madabhushi A, Madhavan S, Napel S, Rao A, Saltz J, Tatum J, Verhaak R, Whitman G. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology. 2014;7(5):556-69. doi: http://dx.doi.org/10.1016/j.tranon.2014.07.007.
  4. Rao A. Exploring relationships between multivariate radiological phenotypes and genetic features: A case-study in Glioblastoma using the Cancer Genome Atlas, Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE.
  5. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 2012;264(2):387-96. Epub 2012/06/23. doi: 10.1148/radiol.12111607. PubMed PMID: 22723499; PubMed Central PMCID: PMCPMC3401348.

  6. Feldman, M., M. G. Piazza, et al. (2015). " 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target." Neurosurgery 62: 209-210.

  7. Gutman, D. A., W. D. Dunn Jr, et al. (2015). "Somatic mutations associated with MRI-derived volumetric features in glioblastoma." Neuroradiology: 1-11.

  8. Zhu, Y., H. Li, et al. (2015). "TU-CD-BRB-06: Deciphering Genomic Underpinnings of Quantitative MRI-Based Radiomic Phenotypes of Invasive Breast Carcinoma." Medical physics 42(6): 3603-3603.

  9. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. http://dx.doi.org/10.1007/s40484-016-0061-6

Radiomics

  1.  Parmar, C., P. Grossmann, et al. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.

  2. Parmar, C., R. T. Leijenaar, et al. (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer." Sci Rep 5: 11044.

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