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  1. 2018
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    Press, R. H., Shu, H-K. G., Shim, H., Mountz, J. M., Kurland, B. F., Wahl, R. L., ... Buatti, J. M. (2018). The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. International Journal of Radiation Oncology Biology Physics, 102(4), 1219-1235. DOI: 10.1016/j.ijrobp.2018.06.023
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    Salem, A., Asselin, M-C., Reymen, B., Jackson, A., Lambin, P., West, C. M. L., ... Faivre-Finn, C. (2018). Targeting Hypoxia to Improve Non-Small Cell Lung Cancer Outcome. Journal of the National Cancer Institute, 110(1). DOI: 10.1093/jnci/djx160
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  18. 2017
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    Tunali, I., Stringfield, O., Guvenis, A., Wang, H., Liu, Y., Balagurunathan, Y., ... Schabath, M. B. (2017). Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients. Oncotarget, 8(56), 96013-96026. DOI: 10.18632/oncotarget.21629
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    Hanzouli-Ben Salah, H., Lapuyade-Lahorgue, J., Bert, J., Benoit, D., Lambin, P., Van Baardwijk, A., ... Hatt, M. (2017). A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation. Medical Physics, 44(11), 5835-5848. DOI: 10.1002/mp.12531
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    Alitto, A. R., Gatta, R., Vanneste, B. G. L., Vallati, M., Meldolesi, E., Damiani, A., ... Mantini, G. (2017). PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncology, 13(24), 2171-2181. DOI: 10.2217/fon-2017-0142
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    Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Velazquez, E. R., Parmar, C., ... Aerts, H. J. W. L. (2017). Defining the biological basis of radiomic phenotypes in lung cancer. Elife, 6, [23421]. DOI: 10.7554/eLife.23421
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    O'Connor, J. P. B., Aboagye, E. O., Adams, J. E., Aerts, H. J. W. L., Barrington, S. F., Beer, A. J., ... Waterton, J. C. (2017). Imaging biomarker roadmap for cancer studies. Nature Reviews Clinical Oncology, 14(3), 169-186. DOI: 10.1038/nrclinonc.2016.162
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