Henry Woodruff (H.C.A.)

Research profile

Henry C Woodruff  employs machine learning techniques and advanced quantitative image analysis methods to bring precision medicine closer to clinical implementation. Currently he is working on dozens of projects focused on finding prognostic and diagnostic imaging biomarkers related to diseases such as cancer, diabetes, Alzheimer's, and stroke

Key publications
Peerlings, J., Woodruff, H. C., Winfield, J. M., Ibrahim, A., Van Beers, B. E., Heerschap, A., Jackson, A., Wildberger, J. E., Mottaghy, F. M., DeSouza, N. M., & Lambin, P. (2019). Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Scientific Reports, 9(1), 1-10. Article 4800. https://doi.org/10.1038/s41598-019-41344-5
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Sanduleanu, S., Woodruff, H. C., de Jong, E. E. C., van Timmeren, J. E., Jochems, A., Dubois, L., & Lambin, P. (2018). Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. Radiotherapy and Oncology, 127(3), 349-360. https://doi.org/10.1016/j.radonc.2018.03.033
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Morin, O., Vallieres, M., Jochems, A., Woodruff, H. C., Valdes, G., Braunstein, S. E., Wildberger, J. E., Villanueva-Meyer, J. E., Kearney, V., Yom, S. S., Solberg, T. D., & Lambin, P. (2018). A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. International Journal of Radiation Oncology Biology Physics, 102(4), 1074-1082. https://doi.org/10.1016/j.ijrobp.2018.08.032
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Kipritidis, J., Tahir, B. A., Cazoulat, G., Hofman, M. S., Siva, S., Callahan, J., Hardcastle, N., Yamamoto, T., Christensen, G. E., Reinhardt, J. M., Kadoya, N., Patton, T. J., Gerard, S. E., Duarte, I., Archibald-Heeren, B., Byrne, M., Sims, R., Ramsay, S., Booth, J. T., ... Keall, P. J. (2019). The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging. Medical Physics, 46(3), 1198-1217. https://doi.org/10.1002/mp.13346
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Ibrahim, A., Vallieres, M., Woodruff, H., Primakov, S., Beheshti, M., Keek, S., Refaee, T., Sanduleanu, S., Walsh, S., Morin, O., Lambin, P., Hustinx, R., & Mottaghy, F. M. (2019). Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Seminars in Nuclear Medicine, 49(5), 438-449. https://doi.org/10.1053/j.semnuclmed.2019.06.005
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van Timmeren, J. E., Carvalho, S., Leijenaar, R. T. H., Troost, E. G. C., van Elmpt, W., de Ruysscher, D., Muratet, J.-P., Denis, F., Schimek-Jasch, T. A., Nestle, U., Jochems, A., Woodruff, H. C., Oberije, C., & Lambin, P. (2019). Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics. PLOS ONE, 14(6), Article 0217536. https://doi.org/10.1371/journal.pone.0217536
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Keek, S. A., Leijenaar, R. T., Jochems, A., & Woodruff, H. C. (2018). A review on radiomics and the future of theranostics for patient selection in precision medicine. British Journal of Radiology, 91(1091), Article 20170926. https://doi.org/10.1259/bjr.20170926
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Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., van Wijk, Y., Woodruff, H., van Soest, J., Lustberg, T., Roelofs, E., van Elmpt, W., Dekker, A., Mottaghy, F. M., Wildberger, J. E., & Walsh, S. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12), 749-762. https://doi.org/10.1038/nrclinonc.2017.141
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Granzier, R. W. Y., van Nijnatten, T. J. A., Woodruff, H. C., Smidt, M. L., & Lobbes, M. B. (2019). Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review. European Journal of Radiology, 121, Article 108736. https://doi.org/10.1016/j.ejrad.2019.108736
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