APA (7th ed.) Citation

I, R., H, B., E, M., A, P., S, L. S., Y, R., . . . J, E. (2026). Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients. European journal of nuclear medicine and molecular imaging, 53(6), 3915. https://doi.org/10.1007/s00259-026-07803-8

Chicago Style (17th ed.) Citation

I, Rivera, Bourien H, Morel-Corlu E, Peinoit A, Le Sourd S, Rolland Y, Garin E, Acosta O, and Edeline J. "Machine Learning Models Classifiers Enable a Strong Prediction of Radioembolization-induced Liver Disease, and Define a New Bilirubin Threshold for Selection of Patients." European Journal of Nuclear Medicine and Molecular Imaging 53, no. 6 (2026): 3915. https://doi.org/10.1007/s00259-026-07803-8.

MLA (9th ed.) Citation

I, Rivera, et al. "Machine Learning Models Classifiers Enable a Strong Prediction of Radioembolization-induced Liver Disease, and Define a New Bilirubin Threshold for Selection of Patients." European Journal of Nuclear Medicine and Molecular Imaging, vol. 53, no. 6, 2026, p. 3915, https://doi.org/10.1007/s00259-026-07803-8.