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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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Recent publications
  • 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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  • Rad, N. M., Sosef, O., Seegers, J., Koolen, L. J. E. R., Hoofwijk, J. J. W. A., Woodruff, H. C., Hoofwijk, T. A. G. M., Sosef, M., & Lambin, P. (2024). Prognostic models for colorectal cancer recurrence using carcinoembryonic antigen measurements. Frontiers in Oncology, 14, Article 1368120. https://doi.org/10.3389/fonc.2024.1368120
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  • Van Camp, A., Woodruff, H. C., Cockmartin, L., Marshall, N. W., Bosmans, H., & Lambin, P. (2024). Simulated image-specific microcalcification clusters and associated mass enhancement to enhance training of a deep learning model for cancer detection in contrast-enhanced mammography. In M. L. Giger, H. M. Whitney, K. Drukker, & H. Li (Eds.), 17th International Workshop on Breast Imaging, IWBI 2024 (Vol. 13174). Article 1317404 SPIE. https://doi.org/10.1117/12.3026879
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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