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

Publications

Core 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, 1-10. [4800]. https://doi.org/10.1038/s41598-019-41344-5

    More information about this publication

  • 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

    More information about this publication

  • 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

    More information about this publication

  • 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

    More information about this publication

  • 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

    More information about this publication

  • 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), [0217536]. https://doi.org/10.1371/journal.pone.0217536

    More information about this publication

  • 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), [20170926]. https://doi.org/10.1259/bjr.20170926

    More information about this publication

  • 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

    More information about this publication

  • 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, [108736]. https://doi.org/10.1016/j.ejrad.2019.108736

    More information about this publication

Most recent publications:

  • Keek, S. A., Kayan, E., Chatterjee, A., Belderbos, J. S. A., Bootsma, G., van den Borne, B., Dingemans, A-M. C., Gietema, H. A., Groen, H. J. M., Herder, J., Pitz, C., Praag, J., De Ruysscher, D., Schoenmaekers, J., Smit, H. J. M., Stigt, J., Westenend, M., Zeng, H., Woodruff, H. C., ... Hendriks, L. (2022). Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC. Therapeutic Advances in Medical Oncology, 14, [17588359221116605]. https://doi.org/10.1177/17588359221116605

    More information about this publication

  • Granzier, R. W. Y., Ibrahim, A., Primakov, S., Keek, S. A., Halilaj, I., Zwanenburg, A., Engelen, S. M. E., Lobbes, M. B. I., Lambin, P., Woodruff, H. C., & Smidt, M. L. (2022). Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability. Journal of Magnetic Resonance Imaging, 56(2), 592-604. https://doi.org/10.1002/jmri.28027

    More information about this publication

  • Keek, S. A., Beuque, M., Primakov, S., Woodruff, H. C., Chatterjee, A., van Timmeren, J. E., Vallières, M., Hendriks, L. E. L., Kraft, J., Andratschke, N., Braunstein, S. E., Morin, O., & Lambin, P. (2022). Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Frontiers in Oncology, 12, [920393]. https://doi.org/10.3389/fonc.2022.920393

    More information about this publication

  • Refaee, T., Salahuddin, Z., Frix, A-N., Yan, C., Wu, G., Woodruff, H. C., Gietema, H., Meunier, P., Louis, R., Guiot, J., & Lambin, P. (2022). Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning. Frontiers in medicine, 9, [915243]. https://doi.org/10.3389/fmed.2022.915243

    More information about this publication

  • Primakov, S. P., Ibrahim, A., van Timmeren, J. E., Wu, G. Y., Keek, S. A., Beuque, M., Granzier, R. W. Y., Lavrova, E., Scrivener, M., Sanduleanu, S., Kayan, E., Halilaj, I., Lenaers, A., Wu, J. L., Monshouwer, R., Geets, X., Gietema, H. A., Hendriks, L. E. L., Morin, O., ... Lambin, P. (2022). Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications, 13(1), [3423]. https://doi.org/10.1038/s41467-022-30841-3

    More information about this publication

  • Nan, Y., Del Ser, J., Walsh, S., Schonlieb, C., Roberts, M., Selby, I., Howard, K., Owen, J., Neville, J., Guiot, J., Ernst, B., Pastor, A., Alberich-Bayarri, A., Menzel, M. I., Vos, W., Flerin, N., Charbonnier, J. P., van Rikxoort, E., Chatterjee, A., ... Yang, G. (2022). Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. Information Fusion, 82, 99-122. https://doi.org/10.1016/j.inffus.2022.01.001

    More information about this publication

  • Yan, C. G., Hao, P., Wu, G. Y., Lin, J., Xu, J., Zhang, T. J., Li, X. Y., Li, H. X., Wang, S. B., Xu, Y. K., Woodruff, H. C., & Lambin, P. (2022). Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients. Annals of translational medicine, 10(9), [514]. https://doi.org/10.21037/atm-21-4980

    More information about this publication

  • Refaee, T., Salahuddin, Z., Widaatalla, Y., Primakov, S., Woodruff, H. C., Hustinx, R., Mottaghy, F. M., Ibrahim, A., & Lambin, P. (2022). CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. Journal of Personalized Medicine, 12(4), [553]. https://doi.org/10.3390/jpm12040553

    More information about this publication

  • Yan, C. G., Wang, L. F., Lin, J., Xu, J., Zhang, T. J., Qi, J., Li, X. Y., Ni, W., Wu, G. Y., Huang, J. B., Xu, Y. K., Woodruff, H. C., & Lambin, P. (2022). A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis. European Radiology, 32(4), 2188-2199. https://doi.org/10.1007/s00330-021-08365-z

    More information about this publication

  • Sforazzini, F., Salome, P., Moustafa, M., Zhou, C., Schwager, C., Rein, K., Bougatf, N., Kudak, A., Woodruff, H., Dubois, L., Lambin, P., Debus, J., Abdollahi, A., & Knoll, M. (2022). Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice. Radiology: Artificial Intelligence, 4(2), [210095]. https://doi.org/10.1148/ryai.210095

    More information about this publication

Go to all publications

Other publications:

https://iopscience.iop.org/article/10.1088/0004-637X/692/1/924/meta

https://www.aanda.org/articles/aa/abs/2004/26/aa0826/aa0826.html

https://iopscience.iop.org/article/10.1086/523936/meta

https://www.sciencedirect.com/science/article/pii/S036030161503062X

https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4816384

https://iopscience.iop.org/article/10.1088/0004-637X/691/2/1328/meta

https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4817484

https://iopscience.iop.org/article/10.1088/0004-637X/707/1/632/meta

https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4937599

https://link.springer.com/article/10.1186/s13014-016-0682-y

https://iopscience.iop.org/article/10.1088/0031-9155/59/1/61/meta

https://iopscience.iop.org/article/10.1088/1742-6596/444/1/012042/meta

https://www.birpublications.org/doi/full/10.1259/bjr.20190948

https://onlinelibrary.wiley.com/doi/full/10.1002/jcsm.12512

https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.12199

https://www.karger.com/Article/Abstract/505429

https://www.sciencedirect.com/science/article/pii/S0720048X19303869

https://www.sciencedirect.com/science/article/pii/S1046202320301110

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232639

https://link.springer.com/content/pdf/10.1007/s00330-019-06597-8.pdf

https://www.researchgate.net/profile/Henry_Woodruff

H.C.A. Woodruff

Deputy Head of The D-Lab

D-lab, Precision Medicine, GROW, School for Oncology & Reproduction, Fac. Health, Medicine and Life Sciences
ORCID
+31(0)43 3885081
LinkedIn