APA (7th ed.) Citation

Papp, L., Spielvogel, C. P., Grubmüller, B., Grahovac, M., Krajnc, D., Ecsedi, B., . . . Hacker, M. (2021). Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. European Journal of Nuclear Medicine and Molecular Imaging, 48(6), 1795. https://doi.org/10.1007/s00259-020-05140-y

Chicago Style (17th ed.) Citation

Papp, L., et al. "Supervised Machine Learning Enables Non-invasive Lesion Characterization in Primary Prostate Cancer with [68Ga]Ga-PSMA-11 PET/MRI." European Journal of Nuclear Medicine and Molecular Imaging 48, no. 6 (2021): 1795. https://doi.org/10.1007/s00259-020-05140-y.

MLA (9th ed.) Citation

Papp, L., et al. "Supervised Machine Learning Enables Non-invasive Lesion Characterization in Primary Prostate Cancer with [68Ga]Ga-PSMA-11 PET/MRI." European Journal of Nuclear Medicine and Molecular Imaging, vol. 48, no. 6, 2021, p. 1795, https://doi.org/10.1007/s00259-020-05140-y.