Prof Dr P. Lambin

Zhang, Y., Mali, S. A., Khan, D., Amirrajab, S., Ibor-Crespo, E., Jimenez-Pastor, A., Ribas, G., Flor-Arnal, S., Zerunian, M., Aube, C., Marti-Bonmati, L., Salahuddin, Z., & Lambin, P. (2026). A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer. Insights into Imaging, 17(1), Article 141. https://doi.org/10.1186/s13244-026-02296-3

Schuitmaker, L., van der Wiel, A. M. A., Lieuwes, N. G., Biemans, R., Mutsters, N. A. M., Jung, J., Bastos, V. C., Sagarra, E. P., Kuang, S., Setton, J., Langie, S. A. S., Kampen, K. R., Theys, J., Yaromina, A., Dubois, L. J., & Lambin, P. (2026). Deficiencies in the Fanconi anemia or homologous recombination pathway enhance the antitumor effects of the hypoxia-activated prodrug CP-506. Molecular Therapy: Oncology, 34(1), Article 201161. https://doi.org/10.1016/j.omton.2026.201161

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

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

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

Lambin, P., & Salahuddin, Z. (2026). Reply to 'Radiomics Quality Score 2.0: what changed from version 1.0 and why it matters'. Nature Reviews Clinical Oncology, 23(1), Article 86. https://doi.org/10.1038/s41571-025-01096-w

Laurent, P.-A., Andre, F., Bobard, A., Deandreis, D., Demaria, S., Depil, S., Eichmueller, S. B., Fernandez-Palomo, C., Foijer, F., Galluzzi, L., Galon, J., Guckenberger, M., Harrington, K. J., Herrera, F. G., Huber, P. E., Italiano, A., Karam, S. D., Kroemer, G., Lambin, P., ... Deutsch, E. (2025). Pushing the boundaries of radiotherapy-immunotherapy combinations: highlights from the 7th immunorad conference. OncoImmunology, 14(1), Article 2432726. https://doi.org/10.1080/2162402X.2024.2432726

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

Martí-Bonmatí, L., Blanquer, I., Tsiknakis, M., Tsakou, G., Martinez, R., Capella-Gutierrez, S., Zullino, S., Meszaros, J., Bron, E. E., Gelpi, J. L., Riklund, K., Chaabane, L., Schlemmer, H. P., Aznar, M., Serrano Candelas, P., Gordebeke, P., Hierath, M., Scollen, S., Martin-Sanchez, F., ... EUCAIM Consortium (2025). Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure. Insights into Imaging, 16(1), Article 47. https://doi.org/10.1186/s13244-025-01913-x

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