The Cancer Imaging Archive (TCIA) Public Access

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  1. Williamson JF, Das SK, Goodsitt MS, Deasy JO. Introducing the Medical Physics Dataset Article. Med. Phys. (2017) 44(2)349-350. doi: 10.1002/mp.12003
  2. Nida, N; Khan, M. Efficient Colorization of Medical Imaging based on Colour Transfer Method. U.G. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 53(4); 253-261. (2016). (link)
  3. Kalpathy-Cramer J, Zhao B, et al. A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging (2016). 29(4):476-487. DOI: 10.1007/s10278-016-9859-z

  4. Parks CL, Monson KL. Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications. Journal of Digital Imaging. 2016:1-11. DOI: 10.1007/s10278-016-9932-7

  5. Huang BE, Mulyasasmita W, Rajagopal G. The Path from Big Data to Precision Medicine. Expert Review of Precision Medicine and Drug Development (2016). 1(2):129-143. (link)

  6. Chatellier G, Varlet V, Blachier-Poisson C. "Big data" and "open data": What kind of access should researchers enjoy? Therapie. 2016 Feb;71(1):97-105, 107-14.(link)
  7. Benedict SH, Hoffman K, et al. Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data. Int J Radiat Oncol Biol Phys. 2016. 95(3):873-879 (link)
  8. Toga AW, Dinov ID. Sharing big biomedical data. Journal of Big Data. 2015;2(1):1-12. (link)
  9. Moore SM, Maffitt DR, Smith KE, Kirby JS, Clark KW, Freymann JB, Vendt BA, Tarbox LR, Prior FW. De-identification of Medical Images with Retention of Scientific Research Value. RadioGraphics. 2015;35(3):727-35. doi: doi: 10.1148/rg.2015140244.
  10. Mayo CS, Deasy JO, et al. How Can We Effect Culture Change Toward Data-Driven Medicine? Int J Radiat Oncol Biol Phys. 2016. 95(3):916-21. (link)
  11. Kirby, J., L. Tarbox, et al. (2015). "TU-AB-BRA-03: The Cancer Imaging Archive: Supporting Radiomic and Imaging Genomic Research with Open-Access Data Sets." Medical physics 42(6): 3587-3587.  DOI: 10.1118/1.4925508
  12. GIllies RJ, Kinahan PE, et al. Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016. 278(2):563-77. (link)
  13. Fedorov A, Clunie D, et al. DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research PeerJ, 2016. (link)
  14. Commean PK, Rathmell JM, Clark KW, Maffitt DR, Prior FW. A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial. Journal of Digital Imaging. 2015:1-9. (paper)
  15. Bourne PE. DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 2015;275(1):3-4. link. PubMed PMID: 25799330.
  16. Armato SG, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP. Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Journal of Medical Imaging. 2015;2(2):020103-.
  17. Herskovits EH. Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 2014;124:1-30. (link)
  18. Gutman DA, Dunn Jr WD, Cobb J, Stoner RM, Kalpathy-Cramer J, Erickson B. Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer. Frontiers in Neuroinformatics. 2014;8.(paper)
  19. Erickson BJ, Fajnwaks P, Langer SG, and Perry J. Multisite Image Data Collection and Management Using the RSNA Image Sharing Network., Translational oncology, 2014. 7(1):36-39. (paper)
  20. Prior FW, Clark K, Commean P, Freymann J, Jaffe C, Kirby J, Moore S, Smith K, Tarbox L, Vendt B. TCIA: an information resource to enable open science. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE; 2013. (paper)
  21. Gutman DA, Cobb J, Somanna D, et al. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data., Journal of the American Medical Informatics Association, 2013. 20(6): p. 1091-1098. doi: 10.1136/amiajnl-2012-001469 (paper)
  22. 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, 26(6), December, 2013, pp 1045-1057. (paper)
  23. Villani L and Prati RC. Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário. Congresso Brasileiro de Informática em Saúde (CBIS’2012). Citado na. 2012.(paper)
  24. Mongkolwat P, Channin DS, Kleper V, Rubin DL. Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder.Radiographics .2012. 32(4):1223-32. (paper).
  25. Jaffe, C Carl. Imaging and Genomics: Is There a Synergy?. Radiology. 2012. 264(2):329-31.(paper).
  26. Freymann JB, Kirby JS, Perry JH, Clunie DA, and Jaffe CC. Image data sharing for biomedical research—meeting HIPAA requirements for de-identification.Journal of Digital Imaging 25, no. 1 (2012): 14-24. (paper)

  27. Kohli, M., Morrison, J. J., Wawira, J., Morgan, M. B., & Hostetter, J., Genereaux, B., Hussain, M., Langer S. G. (2017). Creation and curation of the society of imaging informatics in medicine hackathon dataset. Journal of Digital Imaging, 1-4. doi:10.1007/s10278-017-0003-5

     

Radiogenomics

  1. Demerath T, Simon-Gabriel CP, Kellner E, et al. Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J. 2017;30(1):36-47. doi: 10.1177/1971400916678225

  2. Liu TT, Achrol AS, Mitchell LA, Rodriguez SA, Feroze A, Iv M, Kim C, Chaudhary N, Gevaert O, Stuart JM, Harsh GR, Chang SD, Rubin DL. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro-Oncology. 2016:1-11. doi: 10.1093/neuonc/now270

  3. Schrock M, Batar B, Lee J, Druck T, Ferguson B, Cho J, Akakpo K, Hagrass H, Heerema N, Xia F. Wwox–Brca1 interaction: role in DNA repair pathway choice. Oncogene. 2016:1-13. doi: 10.1038/onc.2016.389.

  4. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiologica. 2016:0284185116673119.

  5. McCann SM, Jiang Y, Fan X, Wang J, et al. Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. AJR Am J Roentgenol (2016). 206(3):559-565 (link)

  6. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. (link)  

  7. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. Imaging genomics in cancer research: limitations and promises. The British Journal of Radiology. 2016:20151030. doi:10.1259/bjr.20151030

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

  10. Shinegare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear renal cell carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdominal Imaging (2015). 40(6)1684-1692. (link)

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

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

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

  14. 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: 10.1016/j.tranon.2014.07.007.
  15. 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.
  16. 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.  (link)

  17. Lehrer, M., Bhadra, A., Ravikumar, V., Chen, J. Y., Wintermark, M., Hwang, S. N., Holder, C. A., Huang, E. P., Fevrier-Sullivan, B., Freymann, J. B., Rao, A., & TCGA Glioma Phenotype Research Group. (2017). Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma. Oncoscience, 4, 57-66. doi:10.18632/oncoscience.353

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