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Henry Woodruff (H.C.)

Research profile

I lead The D-Lab (Data-Driven Lab) at Maastricht University, translating AI in medical imaging into solutions that can be implemented in clinical and research practice. As AI portfolio holder within GROW, I manage a pipeline from prototypes to CE-marked tools, aligning clinicians, developers, IT, and external partners around validation, governance, safety, and adoption. I also lead and coordinate large Dutch and EU grant programmes and consortia, turning strategy into deliverable-focused workplans, budgets, timelines, and risk management—so ambitious projects get funded and executed.

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
  • Aerts, S., Werthen-Brabants, L., Khan, H., Giraldo, D. L., De Brouwer, E., Geys, L., Popescu, V., Sijbers, J., Woodruff, H. C., Dhaene, T., Deschrijver, D., Van Wijmeersch, B., Lambin, P., & Peeters, L. M. (2026). Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening. Frontiers in Immunology, 17, Article 1625837. https://doi.org/10.3389/fimmu.2026.1625837
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  • Kondylakis, H., Osuala, R., Puig-Bosch, X., Lazrak, N., Diaz, O., Kushibar, K., Chouvarda, I., Charalambous, S., Starmans, M. PA., Colantonio, S., Tachos, N., Joshi, S., Woodruff, H. C., Salahuddin, Z., Tsakou, G., Aussó, S., Alberich, L. C., Papanikolaou, N., Lambin, P., ... Lekadir, K. (2026). A review of methods for trustworthy AI in medical imaging: The FUTURE-AI Guidelines. IEEE Journal of Biomedical and Health Informatics, 30(3), 2299-2315. https://doi.org/10.1109/JBHI.2025.3614546
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  • Kuang, S., Schuitmaker, L., Wu, M., Salahuddin, Z., Van Der Wiel, A., Van De Laak, J., Lieuwes, N., Biemans, R., Jung, J., Yaromina, A., Dubois, L. J., Woodruff, H. C., & Lambin, P. (2026). Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study. npj Precision Oncology, 10(1), Article 121. https://doi.org/10.1038/s41698-026-01322-3
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  • Budginaite, E., Magee, D. R., Kloft, M., Nankivell, M., Langley, R. E., Cunningham, D., Allum, W. H., Raghuram, G., Yoshikawa, T., Oshima, T., Woodruff, H. C., & Grabsch, H. I. (2026). A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology. In S. Ali, D. C. Hogg, & M. Peckham (Eds.), Medical Image Understanding and Analysis - 29th Annual Conference, MIUA 2025, Proceedings (Vol. 15918 LNCS, pp. 88-101). Springer Verlag. https://doi.org/10.1007/978-3-031-98694-9_7
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  • Maas, L., Contreras-Meca, C., Ghezzo, S., Belmans, F., Corsi, A., Cant, J., Vos, W., Bobowicz, M., Rygusik, M., Laski, D. K., Annemans, L., Hiligsmann, M., Lambin, P., & Woodruff, H. (2025). Cost-effectiveness analysis of artificial intelligence (AI) in earlier detection of liver lesions in cirrhotic patients at risk of hepatocellular carcinoma in Italy. Journal of Medical Economics, 28(1), 1023-1036. https://doi.org/10.1080/13696998.2025.2525006
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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 S22006. https://doi.org/10.1117/1.JMI.12.S2.S22006
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  • Lambin, P., Woodruff, H. C., Mali, S. A., Zhong, X., Kuang, S., Lavrova, E., Khan, H., Lekadir, K., Zwanenburg, A., Deasy, J., Bobowicz, M., Marti-Bonmati, L., Maidment, A., Dumontier, M., Kinahan, P. E., Nobel, J. M., Amirrajab, S., & Salahuddin, Z. (2025). Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine. Nature Reviews Clinical Oncology, 22(11), 831-846. https://doi.org/10.1038/s41571-025-01067-1
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  • Khan, H., Woodruff, H. C., Giraldo, D. L., Werthen-Brabants, L., Mali, S. A., Amirrajab, S., De Brouwer, E., Popescu, V., Van Wijmeersch, B., Gerlach, O., Sijbers, J., Peeters, L. M., & Lambin, P. (2025). Leveraging hand-crafted radiomics on multicenter FLAIR MRI for predicting disability worsening in people with multiple sclerosis. Frontiers in Neuroscience, 19, Article 1610401. https://doi.org/10.3389/fnins.2025.1610401
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  • Khan, H., Aerts, S., Vermeulen, I., Woodruff, H. C., Lambin, P., & Peeters, L. M. (2025). Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward. Journal of NeuroEngineering and Rehabilitation, 22(1), Article 204. https://doi.org/10.1186/s12984-025-01748-z
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  • Budginaite, E., Magee, D. R., Kloft, M., Woodruff, H. C., & Grabsch, H. I. (2025). Corrigendum to “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(2024) 100367 (Journal of Pathology Informatics (2024) 15, (S2153353924000063), (10.1016/j.jpi.2024.100367)). Journal of Pathology Informatics, 18, Article 100457. https://doi.org/10.1016/j.jpi.2025.100457
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Other publications

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