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  1. Aerts, H. J., E. R. Velazquez, et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. TCIA. Saint Louis, MO. (link)
  2. Armato, S. G. and K. Drukker ( 2015). SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset. TCIA. Saint Louis, MO. (link)
  3. Colen RR, Wang J, et al. (2014). Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. TCIA. Saint Louis, MO. (link)
  4. Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. TCIA. Saint Louis, MO. (link)

  5. Gevaert, O., J. Xu, et al. (2014). Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. TCIA. Saint Louis, MO. (link)
  6. Grove O, Berglund AE, et al. (2015). Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. TCIA. Saint Louis. MO. (link)
  7. Gutman DA, Cooper LA, et al. (2014). MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. TCIA. Saint Louis, MO. (link)

  8. Huang, W., X. Li, et al. (2014). Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. TCIA. Saint Louis, MO. (link)

  9. Jain, R., L. M. Poisson, et al. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. TCIA. Saint Louis, MO. (link)

  10. Kalpathy-Cramer, J., S. Napel, et al. ( 2015). QIN multi-site collection of Lung CT data with Nodule Segmentations. TCIA. Saint Louis, MO. (link)

  11. Mazurowski, M. A., J. Zhang, et al. (2014). Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. TCIA. Saint Louis, MO. (link)
  12. Messay T, Hardie RC, et al. (2014). S egmentation Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset. TCIA. Saint Louis, MO. (link)

  13. Roth H, Lu L, et al. ( 2015). A new 2.5D representation for lymph node detection in CT. TCIA. Saint Louis, MO. (link)

  14. Shinagare AB, Vikram R, et al. ( 2015). Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group. TCIA. Saint Louis, MO. (link)

  15. Vallières, M., C. Freeman, et al. ( 2015). Data from: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. TCIA. Saint Louis, MO. (link)


  1. Clarke, L. P., R. J. Nordstrom, et al. (2014). "The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals." Translational Oncology 7(1): 1-4. (link)

  2. Huang, W., X. Li, et al. (2014). "Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge." Transl Oncol 7(1): 153-166.

  3. Kalpathy-Cramer, J., J. B. Freymann, et al. (2014). "Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive." Translational oncology 7(1): 147-152.

  4. Levy, M. A., J. B. Freymann, et al. (2012). "Informatics methods to enable sharing of quantitative imaging research data." Magnetic Resonance Imaging.