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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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Recent publications
  • Mali, S. A., Rad, N. M., Woodruff, H. C., Depeursinge, A., Andrearczyk, V., & Lambin, P. (2025). Harmonizing CT scanner acquisition variability in an anthropomorphic phantom: A comparative study of image-level and feature-level harmonization using GAN, ComBat, and their combination. PLOS ONE, 20(5), Article e0322365. https://doi.org/10.1371/journal.pone.0322365
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  • Van Camp, A., Woodruff, H. C., Cockmartin, L., Lobbes, M., Majer, M., Balleyguier, C., Marshall, N. W., Bosmans, H., & Lambin, P. (2025). Impact of synthetic data on training a deep learning model for lesion detection and classification in contrast-enhanced mammography. Journal of medical imaging (Bellingham, Wash.), 12(Suppl 2), Article 22006. https://doi.org/10.1117/1.JMI.12.S2.S22006
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  • van den Ende, T., Kuijper, S. C., Widaatalla, Y., Noortman, W. A., van Velden, F. H. P., Woodruff, H. C., van der Pol, Y., Moldovan, N., Pegtel, D. M., Derks, S., Bijlsma, M. F., Mouliere, F., de Geus-Oei, P. L.-F., Lambin, P. P., & van Laarhoven, P. H. W. M. (2025). Integrating Clinical Variables, Radiomics, and Tumor-derived Cell-free DNA for Enhanced Prediction of Resectable Esophageal Adenocarcinoma Outcomes. International Journal of Radiation Oncology Biology Physics, 121(4), 963-974. https://doi.org/10.1016/j.ijrobp.2024.10.010
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  • Widaatalla, Y., Wolswijk, T., Khan, M. D., Halilaj, I., Mosterd, K., Woodruff, H. C., & Lambin, P. (2025). Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study. Cancers, 17(5), Article 768. https://doi.org/10.3390/cancers17050768
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  • Chen, Y., Pasquier, D., Verstappen, D., Woodruff, H. C., & Lambin, P. (2025). An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy. Journal of Cancer Research and Clinical Oncology, 151(2), Article 84. https://doi.org/10.1007/s00432-025-06119-8
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  • Van Camp, A., Punter, E., Houbrechts, K., Cockmartin, L., Prevos, R., Marshall, N. W., Woodruff, H. C., Lambin, P., & Bosmans, H. (2025). An automated toolbox for microcalcification cluster modeling for mammographic imaging. Medical Physics, 52(2), 1335-1349. https://doi.org/10.1002/mp.17521
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  • Budginaite, E., Magee, D. R., Kloft, M., Woodruff, H. C., & Grabsch, H. I. (2024). Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review. Journal of Pathology Informatics, 15, Article 100367. https://doi.org/10.1016/j.jpi.2024.100367
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  • Verstappen, D., Pasquier, D., Chen, Y., Lambin, P., & Woodruff, H. C. (2024). Radiomic feature robustness in CT scans affected by fiducial marker induced streak artifacts for patients with hepatocellular carcinoma. Medical Physics, 51(11), 8362-8370. https://doi.org/10.1002/mp.17329
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  • Giraldo, D. L., Khan, H., Pineda, G., Liang, Z., Lozano-Castillo, A., Van Wijmeersch, B., Woodruff, H. C., Lambin, P., Romero, E., Peeters, L. M., & Sijbers, J. (2024). Perceptual super-resolution in multiple sclerosis MRI. Frontiers in Neuroscience, 18, Article 1473132. https://doi.org/10.3389/fnins.2024.1473132
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  • Peeken, J. C., Etzel, L., Tomov, T., Münch, S., Schüttrumpf, L., Shaktour, J. H., Kiechle, J., Knebel, C., Schaub, S. K., Mayr, N. A., Woodruff, H. C., Lambin, P., Gersing, A. S., Bernhardt, D., Nyflot, M. J., Menze, B., Combs, S. E., & Navarro, F. (2024). Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas. Radiotherapy and Oncology, 197, Article 110338. https://doi.org/10.1016/j.radonc.2024.110338
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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